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Bipin Gajbhiye, Anshika Aggarwal, and Shalu Jain. "Automated Security Testing in DevOps Environments Using AI and ML." International Journal for Research Publication and Seminar 15, no. 2 (2024): 259–71. http://dx.doi.org/10.36676/jrps.v15.i2.1472.
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The rapid adoption of DevOps practices has transformed the software development landscape by emphasizing continuous integration, continuous delivery (CI/CD), and agile methodologies. However, this rapid pace of development often introduces significant security challenges, as traditional security testing methods struggle to keep up with the accelerated release cycles. To address these challenges, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into automated security testing has emerged as a promising solution. This paper explores the use of AI and ML to enhance automated security testing within DevOps environments, offering a comprehensive approach to identifying, predicting, and mitigating security vulnerabilities in real time. Automated security testing leverages AI and ML algorithms to analyze code, detect anomalies, and predict potential security threats. These technologies enable the continuous monitoring of codebases, allowing for the early identification of vulnerabilities before they are exploited. By incorporating AI-driven security testing into the CI/CD pipeline, organizations can ensure that security is not an afterthought but a continuous process integrated into every stage of the software development lifecycle. AI and ML models can be trained to recognize patterns associated with security risks, such as code injection, unauthorized access, and data leakage. These models continuously learn from new data, improving their accuracy over time and adapting to evolving threats. The dynamic nature of AI-driven security testing makes it particularly suited for DevOps environments, where frequent code changes and updates can introduce new vulnerabilities. Moreover, AI and ML can assist in automating complex tasks, such as threat modeling, risk assessment, and the prioritization of security issues, enabling security teams to focus on higher-order tasks that require human expertise.
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Oluwatosin Oladayo Aramide. "AI-Driven Cybersecurity in Storage Infrastructure." World Journal of Advanced Engineering Technology and Sciences 12, no. 2 (2024): 990–1001. https://doi.org/10.30574/wjaets.2024.12.2.0270.
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This paper sheds some light on how AI-powered cybersecurity can be applied to protecting storage infrastructures, namely, high-throughput NFS and S3 object stores. As data becomes more sensitive and volumes larger, conventional security is failing and perhaps the most vulnerable to this are AI/ML data. The research suggests taking into consideration the behavior-based threat identification, which reflects application to detection of ransomware, data exfiltration, insider threats, and others, prior to their evolvement. An AI can proactively identify anomalies by studying the activities and actions of the users and systems and help raise an alert on the occurrence of a possible breach. The article also discusses the integration of AI systems with SIEM (Security Information and Event Management) and SOAR (Security Orchestration, Automation, and Response) tools, leveraging Open Telemetry for seamless coordination and real-time threat response. As it suggests the sure need to adopt appropriate security measures to highly sensitive AI/ML datasets, the article lends prominence to the flexibility and scalability of AI-enhanced cybersecurity as a solution to security issues concerning storage in a dynamic environment.
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Gedam, Sahil Milind. "AI/ML-Driven Phishing Defence: Crafting a Robust Email Security Framework." International Journal of Scientific Research and Engineering Trends 11, no. 2 (2025): 1605–10. https://doi.org/10.61137/ijsret.vol.11.issue2.274.
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Reddy Bhimanapati, Vijay Bhasker, Shalu Jain, and Pandi Kirupa Gopalakrishna Pandian. "Security Testing for Mobile Applications Using AI and ML Algorithms." Journal of Quantum Science and Technology 1, no. 2 (2024): 44–58. http://dx.doi.org/10.36676/jqst.v1.i2.15.
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Mobile apps have revolutionized the digital world, making mobile devices essential to billions of users' everyday lives. This growth in mobile use has also increased security concerns to mobile apps, from data breaches to malicious software assaults. Traditional security testing methodologies, although useful, sometimes fail to address these attackers' sophistication and evolution. This study examines the use of AI and ML algorithms in mobile application security testing to improve vulnerability discovery, analysis, and mitigation.AI and ML algorithms use massive volumes of data and real-time analytics to spot vulnerabilities faster and more accurately than conventional security testing techniques. These technologies enable automated code analysis, anomaly detection, behavioral analysis, and penetration testing, creating a proactive and adaptive security framework. Automation employing AI and ML may find source code security flaws by learning from a massive database of known vulnerabilities and applying it to fresh code. This speeds up manual code checks and improves vulnerability detection. Anomaly detection techniques may monitor application user behavior for abnormalities that may signal security issues like illegal access or data exfiltration.By identifying unusual user behavior and highlighting it, behavioral analysis improves application security. This method detects suspicious activity in real time, allowing fast threat action. AI-driven penetration testing may also mimic complex attacks to find application defensive gaps that hostile actors might exploit.Due to frequent app updates and feature additions, AI and ML in mobile application security testing provide continuous security evaluation. These algorithms can learn and adapt to new risks, keeping security testing current and effective as threats change. Implementing AI and ML in security testing is difficult. AI systems may falsely label normal actions as security risks, which is a major worry. This might cause unneeded interruptions and diminish system dependability. Large datasets used to train AI algorithms present privacy and ethical problems. Despite these limitations, AI and ML in mobile app security assessment have substantial advantages. These technologies are crucial in the fight against mobile security risks because they can analyze massive volumes of data, discover complicated patterns, and respond to emerging threats in real time. AI and ML in security testing will likely become mainstream as mobile apps become more complicated and important, assuring user security and reliability. This article indicates that AI and ML in mobile application security testing advances cybersecurity. These solutions solve mobile app security issues by improving security testing accuracy, speed, and flexibility. To properly secure mobile apps using AI and ML, future research should address security testing difficulties including false positives and data privacy.
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Researcher. "INTEGRATING AI/ML INTO DEVSECOPS: STRENGTHENING SECURITY AND COMPLIANCE IN CLOUD-NATIVE APPLICATIONS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 1128–48. https://doi.org/10.5281/zenodo.14043775.
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Cloud-native application security in contemporary enterprise settings can be revolutionized by incorporating Artificial Intelligence (AI) and Machine Learning (ML) into DevSecOps procedures. This thorough examination looks at how businesses may improve security testing automation, vulnerability identification, and compliance monitoring across the development lifecycle by utilizing AI/ML capabilities. Organizations report up to 76% fewer false positives, 88% higher threat detection accuracy, and 71% quicker vulnerability remediation, according to the study, which is based on substantial industry research and implementation data. While tackling important issues like model training quality and integration complexity, the study also looks at workable frameworks for integrating AI-driven security controls in CI/CD pipelines, Infrastructure as Code (IaC), and real-time threat detection systems. This analysis shows how AI/ML integration may significantly improve security operations while lowering manual involvement and increasing development velocity through thorough case studies and performance indicators.
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Pakalapati, Naveen, Jawaharbabu Jeyaraman, and Sai Mani Krishna Sistla. "Building Resilient Systems: Leveraging AI/ML within DevSecOps Frameworks." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 2 (2023): 213–30. http://dx.doi.org/10.60087/jklst.vol2.n2.p230.
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This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques within DevSecOps frameworks to enhance system resilience. In today's dynamic and rapidly evolving technological landscape, resilience has become a critical aspect of software development and operations. DevSecOps, an evolution of the DevOps methodology, emphasizes the importance of integrating security practices throughout the software development lifecycle. By leveraging AI/ML capabilities within DevSecOps frameworks, organizations can proactively identify and mitigate security threats, optimize system performance, and enhance overall resilience. This paper discusses various strategies for incorporating AI/ML algorithms into DevSecOps workflows, including anomaly detection, predictive analytics, and automated incident response. Furthermore, it examines the challenges and considerations associated with implementing AI/ML-driven approaches within DevSecOps environments, such as data privacy concerns, model interpretability, and algorithmic biases. Through a comprehensive exploration of these concepts, this paper provides insights into building resilient systems by harnessing the power of AI/ML within DevSecOps frameworks.
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Cedrick Agorbia-Atta, Imande Atalor, Rita Korkor Agyei, and Richard Nachinaba. "Leveraging AI and ML for Next-Generation Cloud Security: Innovations in Risk-Based Access Management." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1487–97. http://dx.doi.org/10.30574/wjarr.2024.23.3.2788.
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In the increasing reliance on the cloud computing era, securing digital assets against sophisticated cyber threats has become a critical concern for organizations globally. Traditional security mechanisms, which often rely on static and pre-defined access control policies, must be revised to address these threats' dynamic and evolving nature. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud security through the development of advanced Risk-Based Access Management (RBAM) systems. The primary objective is to evaluate how AI and ML can improve dynamic access control, threat prediction, and mitigation strategies within cloud environments. The research adopts a mixed-methods approach, combining quantitative analysis of RBAM system performance with qualitative insights from cybersecurity experts. AI/ML models were developed using extensive historical access log datasets and integrated into a cloud-based RBAM prototype. The system's performance was assessed based on its accuracy in threat detection, reduction in false positives, and effectiveness in dynamically adjusting access controls. Results indicate that the AI-enhanced RBAM system significantly outperforms traditional methods, achieving a 30% reduction in false positives and a 25% decrease in unauthorized access incidents. Additionally, AI-driven threat prediction models demonstrated high accuracy, enabling preemptive actions to mitigate potential security breaches. These findings highlight the transformative potential of AI and ML in cloud security, providing a more adaptive and proactive defense against emerging threats. The study concludes with recommendations for refining AI/ML models and exploring their application in other areas of cloud security, emphasizing the need for continued innovation to safeguard the increasingly complex digital landscapes that organizations operate within today.
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Cedrick, Agorbia-Atta, Atalor Imande, Korkor Agyei Rita, and Nachinaba Richard. "Leveraging AI and ML for Next-Generation Cloud Security: Innovations in Risk-Based Access Management." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1487–97. https://doi.org/10.5281/zenodo.14945549.
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In the increasing reliance on the cloud computing era, securing digital assets against sophisticated cyber threats has become a critical concern for organizations globally. Traditional security mechanisms, which often rely on static and pre-defined access control policies, must be revised to address these threats' dynamic and evolving nature. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud security through the development of advanced Risk-Based Access Management (RBAM) systems. The primary objective is to evaluate how AI and ML can improve dynamic access control, threat prediction, and mitigation strategies within cloud environments. The research adopts a mixed-methods approach, combining quantitative analysis of RBAM system performance with qualitative insights from cybersecurity experts. AI/ML models were developed using extensive historical access log datasets and integrated into a cloud-based RBAM prototype. The system's performance was assessed based on its accuracy in threat detection, reduction in false positives, and effectiveness in dynamically adjusting access controls. Results indicate that the AI-enhanced RBAM system significantly outperforms traditional methods, achieving a 30% reduction in false positives and a 25% decrease in unauthorized access incidents. Additionally, AI-driven threat prediction models demonstrated high accuracy, enabling preemptive actions to mitigate potential security breaches. These findings highlight the transformative potential of AI and ML in cloud security, providing a more adaptive and proactive defense against emerging threats. The study concludes with recommendations for refining AI/ML models and exploring their application in other areas of cloud security, emphasizing the need for continued innovation to safeguard the increasingly complex digital landscapes that organizations operate within today.
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Rutvij, Shah, Puthraya Karthik, and Paul Josson. "AI-Driven Threat Intelligence Systems: Predictive Cybersecurity Models for Adaptive IT Defense Mechanisms." Global Journal of Engineering and Technology [GJET] 4, no. 2 (2025): 13–15. https://doi.org/10.5281/zenodo.14964173.
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<em>In the rapidly evolving digital landscape, cyber threats have become increasingly sophisticated, necessitating advanced threat intelligence systems. Artificial Intelligence (AI) has emerged as a pivotal technology in cybersecurity, enabling predictive models that enhance adaptive IT defense mechanisms. This paper explores AI-driven threat intelligence systems, detailing their architecture, methodologies, and applications in mitigating cyber threats. We discuss machine learning (ML) and deep learning (DL) models in predictive cybersecurity, real-time threat detection, and automated response systems. Furthermore, we address the challenges, ethical considerations, and future trends in AI-powered cybersecurity. Additionally, we examine the role of AI in securing Android platforms, the significance of AI-driven security for Software Developers, and how Java-based security frameworks contribute to robust cyber defense strategies.</em>
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Researcher. "HUMAN-AI COLLABORATION IN FOOD MANUFACTURING: ENHANCING DATA SECURITY AND COMPLIANCE." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 703–14. https://doi.org/10.5281/zenodo.13364386.
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This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) technologies on the food manufacturing industry, focusing on the synergistic relationship between human expertise and AI-driven solutions. It examines key areas where this collaboration drives significant improvements, including supply chain security, predictive maintenance, regulatory compliance, data security, and continuous improvement processes. Integrating AI and ML technologies offers unprecedented capabilities in data analysis, predictive modeling, and process optimization, enabling food manufacturers to enhance operational efficiency, reduce risks, and maintain high standards of product quality and safety. The article highlights how combining AI's computational power and human insight creates a powerful synergy that addresses the industry's most pressing challenges in an increasingly complex and competitive market.
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Vyas, Deval G. "An analysis of the transformative impact of AI and ML on the finance sector." International Journal of Information Technology and Management 19, no. 2 (2024): 44–53. https://doi.org/10.29070/ew4hyt02.
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Banks are undergoing a technological transformation as a result of AI and ML, which are boosting operational efficiency, improving the customer experience, and bolstering security measures. Automating procedures, analysing massive volumes of data, and making data-driven decisions has never been easier than with these tools. Banking in the modern day is increasingly dependent on chatbots driven by artificial intelligence, fraud detection systems, risk assessment tools, and individualised financial services. This research explores the possibilities, challenges, and outcomes of the banking industry's transformation brought about by AI & ML. In order to improve banking efficiency, save costs, and decrease risks while addressing regulatory and ethical concerns, the report highlights the increasing dependence on AI-driven solutions.
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Vyas, Deval G. "An analysis of the transformative impact of AI and ML on the finance sector." International Journal of Information Technology and Management 19, no. 2 (2024): 44–53. https://doi.org/10.29070/qh70yg96.
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Banks are undergoing a technological transformation as a result of AI and ML, which are boosting operational efficiency, improving the customer experience, and bolstering security measures. Automating procedures, analysing massive volumes of data, and making data-driven decisions has never been easier than with these tools. Banking in the modern day is increasingly dependent on chatbots driven by artificial intelligence, fraud detection systems, risk assessment tools, and individualised financial services. This research explores the possibilities, challenges, and outcomes of the banking industry's transformation brought about by AI & ML. In order to improve banking efficiency, save costs, and decrease risks while addressing regulatory and ethical concerns, the report highlights the increasing dependence on AI-driven solutions.
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Babu, D. Suresh. "TRANSFORMING CYBERSECURITY WITH MACHINE LEARNING: KEY TRENDS AND TECHNOLOGICAL ADVANCES." international journal of advanced research in computer science 15, no. 6 (2024): 23–25. https://doi.org/10.26483/ijarcs.v15i6.7157.
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The rapid evolution of cyber-attacks necessitates advanced security measures. Machine learning (ML), a branch of artificial intelligence (AI), has emerged as a powerful tool for enhancing Cybersecurity. This paper reviews the latest trends in ML for Cybersecurity, including advancements in anomaly detection, adversarial machine learning, automated incident response, federated learning, and explainable AI (XAI). These innovations enable more accurate detection and response to cyber threats. However, the growing integration of ML in Cybersecurity introduces new challenges, such as adversarial attacks on ML models and the need for transparency in AI-driven security solutions.
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Saiyed, Asifiqbal. "AI-Driven Innovations in Fintech: Applications, Challenges, and Future Trends." International Journal of Electrical and Computer Engineering Research 5, no. 1 (2025): 8–15. https://doi.org/10.53375/ijecer.2025.437.
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Integrating Artificial Intelligence (AI) and Machine Learning (ML) in fintech has revolutionized trading, risk management, fraud detection, and regulatory compliance. AI-driven automation enhances efficiency, while predictive analytics improves market forecasting and decision-making. Case studies demonstrate significant transformations in financial institutions, reducing operational costs and increasing accuracy. However, challenges such as data security, model interpretability, and bias remain critical concerns. This paper explores the impact of AI and ML in fintech, analyzing their benefits, limitations, and future implications for practitioners and regulators. Recommendations for improving AI transparency and regulatory adaptability are also discussed.
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Rakibul Hasan Chowdhury. "The evolution of business operations: unleashing the potential of Artificial Intelligence, Machine Learning, and Blockchain." World Journal of Advanced Research and Reviews 22, no. 3 (2024): 2135–47. http://dx.doi.org/10.30574/wjarr.2024.22.3.1992.
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The convergence of Artificial Intelligence (AI), Machine Learning (ML), and Blockchain technologies is reshaping contemporary business operations. This abstract explores their collective impact on efficiency, transparency, and strategic advantage in organizations. AI and ML drive data-driven decision-making, automate processes, and enhance customer experiences through personalized interactions. Blockchain ensures transparency and security in transactions, fostering trust and accountability. Together, these technologies revolutionize traditional business models, offering insights into future trends and challenges in the digital era. Ethical considerations, security concerns, and regulatory landscapes are crucial in navigating this transformative landscape. As businesses embrace these innovations, they gain competitive edges, optimize resource allocation, and elevate customer satisfaction in a dynamic marketplace.
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Rakibul, Hasan Chowdhury. "The evolution of business operations: unleashing the potential of Artificial Intelligence, Machine Learning, and Blockchain." World Journal of Advanced Research and Reviews 22, no. 3 (2024): 2135–47. https://doi.org/10.5281/zenodo.14772115.
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The convergence of Artificial Intelligence (AI), Machine Learning (ML), and Blockchain technologies is reshaping contemporary business operations. This abstract explores their collective impact on efficiency, transparency, and strategic advantage in organizations. AI and ML drive data-driven decision-making, automate processes, and enhance customer experiences through personalized interactions. Blockchain ensures transparency and security in transactions, fostering trust and accountability. Together, these technologies revolutionize traditional business models, offering insights into future trends and challenges in the digital era. Ethical considerations, security concerns, and regulatory landscapes are crucial in navigating this transformative landscape. As businesses embrace these innovations, they gain competitive edges, optimize resource allocation, and elevate customer satisfaction in a dynamic marketplace.
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Mambwe, Hockings, Petros Chavula, Fredrick Kayusi, Gilbert Lungu, and Agnes Uwimbabazi. "Machine learning and AI for security mechanisms: A Systematic Literature Review Using a PRISMA Framework." LatIA 3 (March 25, 2025): 331. https://doi.org/10.62486/latia2025331.
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Cyber threats are evolving rapidly, posing significant risks to individuals, organizations, and digital infrastructure. Traditional cybersecurity measures, which rely on predefined rules and static defence mechanisms, struggle to counter emerging threats such as zero-day attacks and advanced persistent threats (APTs). The integration of artificial intelligence (AI) and machine learning (ML) into cybersecurity presents a transformative approach, enhancing threat detection, anomaly identification, and automated response mechanisms. This study systematically reviews the role of ML and AI in cybersecurity defence using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A comprehensive literature search was conducted across multiple academic databases, identifying and analyzing studies published within the last decade. The review focuses on AI-driven cybersecurity applications, including intrusion detection systems (IDS), malware analysis, and anomaly detection in cloud and IoT environments. Findings indicate that ML models, such as neural networks, support vector machines, and ensemble learning techniques, improve detection accuracy and adaptability to evolving threats. AI-driven automated response systems enhance incident mitigation, reducing reliance on human intervention. However, challenges such as adversarial attacks, data privacy concerns, and computational resource demands persist. The study concludes that AI and ML significantly enhance cybersecurity resilience but require continuous advancements in model robustness, interpretability, and ethical considerations. Future research should focus on refining AI-driven security mechanisms, addressing adversarial vulnerabilities, and improving regulatory frameworks to maximize AI’s potential in cybersecurity.
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Deepthi Kamidi. "Leveraging Artificial Intelligence for Enhanced Data Protection: A Comprehensive Review of Cloud Security amid Emerging threats." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 16–26. https://doi.org/10.52783/jisem.v10i43s.8291.
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Cloud computing offers scalable, adaptable, and affordable solutions that spur innovation across multiple industries, it has fundamentally changed how industries function. However, with this widespread adoption comes the growing challenge of protecting sensitive data, especially as more sophisticated cyberattacks become common. Advanced Persistent challenges (APTs), insider assaults, data breaches, and Distributed Denial of Service (DDoS) attacks are just a few of the challenges that modern cloud environments must contend with. These threats highlight flaws in conventional security paradigmsThe integration of cutting-edge technologies like artificial intelligence (AI) and machine learning (ML) into cloud security is becoming more and more important in response to these issues. These technologies are proving to be effective instruments for increasing prediction accuracy, automating threat detection, and enabling real-time encryption protocol modifications. We can improve cloud security by utilising AI and ML to detect anomalies, find zero-day vulnerabilities, and employ predictive models that assist in addressing problems before they become more serious. A thorough analysis of the present uses of AI and ML in cloud security is provided in this work including how these tools are being used to enhance traditional methods like encryption and access control. It also evaluates the latest research in AI-driven threat detection, behavioral analysis, and adaptive encryption. Additionally, we highlight critical gaps in current AI/ML security frameworks, particularly in terms of scalability, false positive rates, and the challenges of real-time implementation. The primary goals of this review are threefold: first, to systematically analyze the emerging threats to cloud data security; second, to propose the development of more adaptive and robust algorithms that use AI and ML to enhance cloud protection; and third, to present a framework for integrating these algorithms into existing cloud security infrastructures. Ultimately, we hope this review contributes valuable insights that can shape the future of AI/ML-driven cloud security, helping to tackle the evolving challenges that come with modern cloud computing.
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Mamidi, Sundeep Reddy. "The Role of AI and Machine Learning in Enhancing Cloud Security." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 403–17. http://dx.doi.org/10.60087/jaigs.v3i1.161.
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Cloud computing has transformed how organizations store, process, and manage data, offering unparalleled flexibility and scalability. However, the rise in cyber threats presents significant challenges to maintaining robust cloud security. This chapter explores the crucial role that Artificial Intelligence (AI) and Machine Learning (ML) play in enhancing cloud security. By leveraging AI and ML capabilities, organizations can proactively detect, mitigate, and respond to evolving cyber threats, ultimately strengthening their cloud infrastructure. AI-driven techniques enable security systems to recognize patterns, anomalies, and potential threats within vast datasets. ML algorithms, learning from historical attack data, can predict future threats and develop more effective defense mechanisms. Furthermore, AI-enhanced authentication and access control mechanisms bolster identity management, reducing the risk of unauthorized access and data breaches.
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Raja, Venkata Sandeep Davu. "AI-Driven Virtualization: Optimizing Resource Utilization in Modern Data CentersRaja Venkata Sandeep Reddy Davu." European Journal of Advances in Engineering and Technology 11, no. 6 (2024): 21–30. https://doi.org/10.5281/zenodo.13325349.
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Data centres are using AI to improve network and resource management to satisfy market needs for apps and tasks. AI-driven virtualization improves data centre resource utilisation, network agility, security, and compliance. AI-driven virtualization transformed data centre management. These changes improve data centre reliability, efficiency, and scalability. Data centre administrators may optimise resource distribution, network traffic balancing, and workload demand estimation with AI and ML to improve performance and lower costs. AI-supported virtualization optimises resource use by dynamically assigning computing, networking, and storage resources based on workload and demand. Use predictive analytics and dynamic resource allocation to improve data centre design and reduce waste and costs. AI-driven virtualization helps businesses adapt to changing workloads. Virtualization capabilities like autonomous provisioning, predictive maintenance, and self-healing can help data centre infrastructure manage unpredictable workloads and events. AI makes virtualization possible for modern data centres, which is essential for security and compliance. Advanced algorithms in AI security systems detect and analyse suspicious tendencies to protect important data. AI-powered compliance management improves industry standards and data security. AI-driven virtualization has advanced data centres, as evidenced by real-world examples and case studies. AI driven virtualization improves data centre efficiency, saves money, and ensures compliance, paving the way for digital innovation and progress. <em> </em>
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Dhabliya, Dharmesh, Nuzhat Rizvi, Anishkumar Dhablia, A. Phani Sridhar, Sunil D. Kale, and Dipanjali Padhi. "Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems." E3S Web of Conferences 491 (2024): 02033. http://dx.doi.org/10.1051/e3sconf/202449102033.
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In today's data-driven environment, protecting machine learning ecosystems has taken on critical importance. Organisations are relying more and more on AI and ML models to guide important decisions and operations, which have led to an increase in system vulnerabilities. The critical need for techniques to create resilient machine learning (ML) systems that can withstand changing threats is discussed in this study.Data protection is an important component of securing ML environments. Every part of the process, from data preprocessing through model deployment, needs to be secured. In order to reduce potential vulnerabilities, this incorporates code review procedures, safe DevOps practises, and container security.System resilience is vitally dependent on on-going monitoring and anomaly detection. Organisations can respond quickly to security problems by detecting deviations from normal behaviour early on and adjusting their defences as necessary.A strong incident response plan is essential. To protecting machine learning ecosystems necessitates a comprehensive strategy that includes monitoring, incident response, model security, pipeline security, and data protection. By implementing these tactics, businesses may create robust machine learning (ML) systems that can endure the changing threat landscape, protect their data, and guarantee the validity of their AI-driven decision-making processes.
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Amer, Lawrence. "AI in cyber security: A dual perspective on hacker tactics and defensive strategies." Cyber Security: A Peer-Reviewed Journal 8, no. 3 (2025): 198. https://doi.org/10.69554/clxc9075.
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The use of artificial intelligence (AI) in cyber security has significantly changed the dynamics of warfare, ushering in an era in the continuous battle between those launching attacks and those defending against them. This paper presents an analysis of the impact of AI on cyber security, examining its role from a defensive standpoint. It begins by investigating how cybercriminals harness AI to enhance their capabilities. Cutting-edge machine learning (ML) algorithms empower the development of targeted and stealth attacks, using tactics ranging from AI-driven social engineering strategies to malware that can evade detection methods, as well as intelligent botnet systems capable of orchestrating complex offensives. Specific instances such as AI-fuelled malware creation tools and adaptive command and control servers are explored to illustrate the changing landscape of cyber threats and show how cyber security experts utilise AI to strengthen defences. AI-empowered intrusion detection systems, anomaly detection based on ML and automated incident response platforms stand at the forefront of contemporary cyber defence measures. These technologies enable threat identification, in-time predictive analytics and rapid responses to emerging security breaches. By juxtaposing these contrasting uses of AI in cyber security, the paper offers a nuanced perspective on the competition within this domain. This paper seeks to provide cyber security experts with knowledge to predict and combat changing threats and to give researchers a basis for creating new AI-driven security solutions. This holistic strategy not only showcases the game-changing impact of AI in cyber security but also emphasises the crucial requirement for ongoing creativity in this swiftly advancing domain.
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Reeti, Chopra. "Digital arrest in India: Driven cybersecurity for national digital security." i-manager's Journal on Digital Forensics & Cyber Security 3, no. 1 (2025): 34. https://doi.org/10.26634/jdf.3.1.22016.
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India's rapid digital transformation has led to a significant increase in cybercrimes, including deepfake-enabled scams such as Digital Arrest fraud, highlighting the urgent need for advanced security solutions. This paper presents a novel Artificial Intelligence (AI)-driven cybersecurity framework specifically designed for real-time deepfake detection and anomaly analysis. The system employs advanced machine learning (ML) and deep learning (DL) techniques to identify inconsistencies in multimedia content, such as facial discrepancies, and detect unusual user behaviors, like suspicious financial transactions. Hypothetical results demonstrate that this AI approach yields superior threat detection rates, such as >95%, and significantly reduced false positives and response times, thereby minimizing financial losses and data breach costs. To address privacy concerns, the study emphasizes privacy-preserving AI methods. Future research will focus on enhancing AI model interpretability and exploring hybrid human-AI systems to contribute to safer digital environments and support sustainable digital transformation.
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Salameh, Walid, Ola M. Surakhi, and Mohammad Y. Khanafseh. "A Comprehensive Survey on the Data-Driven Approaches used for Tackling the COVID-19 Pandemic." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 21 (April 25, 2024): 200–217. http://dx.doi.org/10.37394/23208.2024.21.21.
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The current evolution of Artificial Intelligence (AI) is fueled by the massive data sources generated by the Internet of Things (IoT), social media, and a diverse range of mobile and web applications. Machine learning (ML) and deep learning become the key to analyzing these data intelligently and developing complementary intelligent data-driven services in the healthcare sector. The world witnessed many AI-enabled tools that contributed to fighting against the COVID-19 pandemic and accelerated with unprecedented accuracy the development and the deployment of many countermeasures. The main objective of this study is to provide a comprehensive survey on the role of AI and ML methods in the healthcare sector. The study offers cases on how AI/ML can arm the world against future pandemics. Specifically, the study presents all available datasets, the main research problems related to COVID-19, and the solutions that AI and ML technologies offer. Finally, based on the analysis of the current literature, the limitations and open research challenges are highlighted. Our findings show that AI and ML technologies can play an essential role in COVID-19 forecasting, prediction, diagnosis, and analysis. In comparison, most of the previous works did not deploy a comprehensive framework that integrates the ML and DL with network security. This work emphasizes the mandate of including network security in all COVID-19 applications and providing complete and secure healthcare services.
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Shahana, Atia, Rakibul Hasan, Sayeda Farjana Farabi, et al. "AI-Driven Cybersecurity: Balancing Advancements and Safeguards." Journal of Computer Science and Technology Studies 6, no. 2 (2024): 76–85. http://dx.doi.org/10.32996/jcsts.2024.6.2.9.
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As Artificial Intelligence (AI) continues its rapid evolution, its profound influence on cybersecurity becomes increasingly evident. This study delves into the pivotal role of AI in fortifying cybersecurity measures, emphasizing its capacity for enhanced threat detection, automated response mechanisms, and the development of resilient security frameworks. However, alongside its promise, recognition of AI's susceptibility to exploitation in sophisticated cyber-attacks exists, underscoring the imperative for continual advancements in AI-driven security solutions. This research offers a nuanced perspective on AI's impact on cybersecurity, advocating for the proactive integration of AI strategies, sustained research efforts, and formulating ethical guidelines. Adopting supervised machine learning (ML) algorithms like decision trees, support vector machines, and neural networks aims to harness AI's potential to bolster cybersecurity while concurrently addressing associated risks, paving the way for a secure digital landscape. Regarding accuracy, the neural network outperforms other models by 98%.
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Venkadesh, Dr P. "Aegis AI - Intelligent Cyber Resilience." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42978.
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As cyber threats continue to evolve in complexity and scale, traditional security measures have become insufficient. Aegis AI (AAI): Intelligent Cyber Resilience presents a cutting-edge approach that integrates artificial intelligence (AI) and machine learning (ML) to strengthen cybersecurity defenses. This study explores the role of AI-driven threat intelligence, automated incident response, and adaptive learning in combating cyberattacks. The proposed AAI framework utilizes deep learning, anomaly detection, and reinforcement learning techniques to predict and mitigate threats in real time. By enhancing cyber resilience, AAI reduces response times, minimizes false positives, and ensures robust security automation. The research also addresses adversarial machine learning risks and ethical concerns surrounding AI in cybersecurity. Our findings demonstrate that AI-powered security systems significantly improve detection accuracy and automate cyber defense strategies. Future research will focus on integrating federated learning, real-time behavioural analytics, and AI-driven compliance frameworks to further enhance AAI’s effectiveness in securing digital ecosystems. Aegis AI is an advanced AI-driven cybersecurity framework designed to enhance cyber resilience by integrating intelligent threat detection, automated incident response, and adaptive learning. It leverages machine learning models to identify, predict, and mitigate cyber threats in real time, reducing response times and minimizing security breaches. By continuously updating its knowledge through federated learning and behavioural analytics, Aegis AI ensures proactive defense against evolving cyber threats, making digital ecosystems more secure and resilient. Aegis AI transforms cybersecurity by providing an intelligent, self-adaptive defense system that not only detects and mitigates threats in real time but also evolves with emerging cyber risks. By automating security responses and continuously learning from new threats, Aegis AI ensures a resilient, proactive, and future-proof cybersecurity framework, safeguarding digital assets with unmatched efficiency. Keywords: AI in Cybersecurity, Threat Intelligence, Machine Learning, Cyber Resilience, Automated Incident
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Md Habibul Arif, Habibor Rahman Rabby, Nusrat Yasmin Nadia, Md Iftekhar Monzur Tanvir, and Abdullah Al Masum. "AI-Driven Risk Assessment in National Security Projects: Investigating machine learning models to predict and mitigate risks in defense and critical infrastructure projects." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 71–85. https://doi.org/10.32996/jcsts.2025.7.2.6.
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Artificial Intelligence (AI) is revolutionizing national security and risk assessment, providing enhanced predictive capabilities, automated threat detection, and strategic decision-making tools. This paper explores the integration of AI and machine learning (ML) in national defense strategies, cybersecurity frameworks, and critical infrastructure protection. AI-driven risk assessment models utilize big data analytics, deep learning, and predictive algorithms to proactively identify, classify, and mitigate security threats before they materialize. The study examines AI applications in cyber risk management, military defense systems, fraud prevention, and digital forensics, highlighting their effectiveness in safeguarding government agencies, financial institutions, and energy grids. Additionally, the paper discusses ethical considerations, algorithmic biases, and regulatory challenges associated with AI-driven risk assessment. The findings emphasize the increasing reliance on AI in cybersecurity and national security operations, demonstrating how AI-based risk assessment tools contribute to threat intelligence, operational resilience, and automated decision-making in critical security environments. The research concludes with future directions for AI adoption, emerging innovations, and policy recommendations to ensure ethical and effective deployment of AI in national security frameworks.
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Dr., Shivaling Balkrishna Rajmane. "The Next Frontier: Future Business Management in the Age of AI and Machine Learning." International Journal of Advance and Applied Research S6, no. 16 (2025): 231–34. https://doi.org/10.5281/zenodo.15143767.
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<em>Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized business management by optimizing operations, improving decision-making, and enhancing customer experiences. As businesses generate and process vast amounts of data, AI and ML enable organizations to extract actionable insights, automate complex processes, and drive strategic growth. These technologies are widely applied across multiple business functions, including supply chain management, human resource management, marketing, customer relationship management (CRM), and financial forecasting.</em> <em>Despite the numerous benefits, AI and ML adoption in business management presents several challenges, such as data security concerns, ethical implications, bias in AI models, and high implementation costs. This study explores the current role of AI and ML in business management, focusing on their applications, benefits, challenges, and ethical considerations. Furthermore, the paper examines future trends, including explainable AI (XAI), AI-driven decision augmentation, and the integration of AI with emerging technologies such as block-chain and the Internet of Things (IoT). By understanding the transformative potential of AI and ML, businesses can develop strategic approaches to leverage these technologies for sustainable growth and long-term competitive advantage.</em>
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KUMAR, R. SENTHIL. "DevOps in 2025: Trends, Innovations, and the Path Ahead." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40564.
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stract— DevOps, the combination of development and operations, has fundamentally transformed software delivery by fostering collaboration, automation, and continuous integration. As we approach 2025, the DevOps landscape is evolving rapidly, driven by advances in artificial intelligence (AI), machine learning (ML), cloud technologies, and automation tools. This paper explores the current state of DevOps, innovations shaping its future, and emerging trends that are likely to define DevOps practices in the coming years. We highlight the integration of AI/ML into DevOps pipelines, the rise of autonomous DevOps systems, and the increasing focus on security and governance in increasingly complex environments. Keywords—DevOps, 2025, AI/ML, automation, autonomous systems, security, cloud-native, DevSecOps.
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Mr. Om D. Bhonsle, Mr. Rohit G. Gupta, Mr. Vishal C. Gupta, and Dr. Poorva G. Waingankar. "Detecting Deepfake Media with AI and ML." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 02 (2025): 267–74. https://doi.org/10.47392/irjaeh.2025.0037.
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Deepfake technology has rapidly advanced, enabling the creation of highly realistic yet manipulated digital media. These artificial videos and images pose significant risks to digital security, misinformation, and identity fraud. Traditional forensic techniques struggle to detect deepfakes effectively due to the increasing sophistication of Generative Adversarial Networks (GANs) and other deep learning-based synthesis methods. The need for a robust, scalable, and automated detection system has become crucial for ensuring media authenticity. This research presents DeepFake Bot, an AI-driven system designed to identify manipulated media with high accuracy. The model integrates Convolutional Neural Networks (CNNs) for spatial analysis and Recurrent Neural Networks (RNNs) for temporal consistency verification. Key detection techniques include eye-blinking pattern analysis, facial texture inconsistency detection, and motion anomaly recognition. The system undergoes extensive training using publicly available deepfake datasets, ensuring its ability to generalize across diverse manipulation techniques. The proposed method is evaluated on large-scale benchmark datasets, including FaceForensics++, Celeb-DF, and the DeepFake Detection Challenge (DFDC) dataset. Experimental results demonstrate that DeepFake Bot achieves 92.4% accuracy, outperforming existing deepfake detection models while maintaining real-time processing efficiency.
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Sunday Adeola Oladosu, Adebimpe Bolatito Ige, Christian Chukwuemeka Ike, Peter Adeyemo Adepoju, Olukunle Oladipupo Amoo, and Adeoye Idowu Afolabi. "AI-driven security for next-generation data centers: Conceptualizing autonomous threat detection and response in cloud-connected environments." GSC Advanced Research and Reviews 15, no. 2 (2023): 162–72. https://doi.org/10.30574/gscarr.2023.15.2.0136.
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The dynamic evolution of next-generation data centers, driven by cloud-native and hybrid architectures, has necessitated a paradigm shift in cybersecurity. Traditional security models, designed for static and on-premise environments, struggle to address the complexities of cloud-connected infrastructures and the rapidly evolving threat landscape. Emerging challenges, such as advanced persistent threats (APTs), ransomware, and insider attacks, demand sophisticated and adaptive security solutions. In this context, artificial intelligence (AI) emerges as a transformative technology capable of redefining threat detection and response mechanisms. This review explores the conceptualization of AI-driven security for next-generation data centers, focusing on autonomous threat detection and response. By leveraging AI and machine learning (ML), security systems can achieve real-time anomaly detection, advanced behavior analysis, and predictive risk assessment. These capabilities enhance the accuracy and speed of identifying malicious activities while reducing false positives. Additionally, autonomous response mechanisms, such as self-healing networks and adaptive security policies, enable rapid containment and mitigation of threats, minimizing potential damages. The review also discusses the integration of AI with existing Security Operations Centers (SOCs), highlighting its potential to augment human decision-making and automate repetitive tasks. Furthermore, it examines the role of advanced encryption, identity management, and compliance tools in fortifying security frameworks. Future trends, including the impact of 5G and edge computing, are explored, emphasizing their implications for real-time applications and IoT security. This study underscores the importance of proactive, AI-driven strategies in securing next-generation data centers, ensuring scalability, resilience, and robust protection in an increasingly interconnected digital landscape. By bridging the gap between cloud-native and on-premise environments, AI-powered security frameworks offer a promising path toward achieving autonomous, adaptive, and future-proof cybersecurity.
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Mrs. N. Madhavi, Mrs. G. Shruthi, M. Srishanth, T. Veera Prasanna Laxmi, G. Siddhartha, and L. Manish. "Smart Farming - Precision Agriculture Using ML." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1035–41. https://doi.org/10.47392/irjaem.2025.0169.
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Agriculture is the backbone of global food production, and with the rising population, the demand for sustainable farming solutions is more critical than ever. The Smart Farming Precision Agriculture project leverages Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and drones to tackle major agricultural challenges and optimize farming operations. This system integrates IoT sensors to enable real-time data collection on crucial factors such as soil health, weather conditions, temperature, and crop status. By analyzing this data, machine learning models provide accurate predictions on crop growth, disease risks, and yield estimation. Automated features like smart irrigation, pest detection, and nutrient monitoring help farmers make informed decisions, reducing resource wastage and improving efficiency. A key feature of the project is drone-based monitoring and spraying, which ensures precise pesticide and fertilizer application, minimizing environmental impact while maximizing productivity. Additionally, CCTV surveillance enables 24/7 field monitoring, enhancing security and protecting crops from external threats like wild animals or theft. The system also includes mobile app integration, allowing farmers to receive real-time alerts, crop recommendations, and remote irrigation control, making farm management more accessible and user-friendly. The proposed solution is cost-effective, eco-friendly, and focused on long-term sustainability and profitability. By incorporating AI-driven automation, it not only improves yield but also ensures better crop protection with minimal human intervention. This project empowers farmers with data-driven insights, enabling precision farming techniques that lead to higher productivity, reduced costs, and enhanced food security.
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Keshetti, Sandeep, and Sandeep Kumar. "AI-Driven Security Frameworks: Enhancing Threat Detection and Response in Modern Systems." International Journal of Research in Humanities and Social Sciences 13, no. 3 (2025): 233–54. https://doi.org/10.63345/ijrhs.net.v13.i3.13.
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As cyber threats become more sophisticated and massive, traditional security measures fall behind, necessitating the use of AI-based security frameworks for efficient threat detection and response. This paper analyzes the role of artificial intelligence (AI) in strengthening cybersecurity measures, especially in threat detection, prevention, and real-time response mechanisms in modern systems. The use of AI technologies, such as machine learning (ML), deep learning (DL), and reinforcement learning (RL), in security systems has proven extremely promising in identifying known and new cyber threats, often outperforming traditional security mechanisms. However, there are some gaps between existing research and practical applications. One such primary challenge is model interpretability, as many of these systems operate as “black boxes,” whose decision-making processes are not easy to understand. Moreover, AI’s dependency on large datasets poses data privacy concerns, especially in sensitive environments. Another limitation is the scalability of AI models, particularly when deployed across large and complex network infrastructures, where they may fail to learn and adapt to evolving threats in real-time. Although AI can identify anomalies and potential vulnerabilities, autonomous, adaptive threat response mechanisms lag behind in the early stages of development. This paper identifies these research gaps, the potential of AI in addressing them, and presents recommendations for future advancements in AI-based security frameworks for providing more robust, transparent, and scalable solutions to modern cybersecurity challenges.
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Verma, Saurabh, Pankaj Pali, Saloni Chourey, and Radhika Chourasiya. "AI-Enhanced Watermarking for Securing Medical Images: Integration of GANs and Blockchain Technology." International Journal of Innovative Research in Computer and Communication Engineering 11, no. 04 (2023): 3250–56. http://dx.doi.org/10.15680/ijircce.2023.1104286.
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The adoption of public cloud platforms has significantly transformed data storage, processing, and management paradigms, offering unprecedented scalability and flexibility. However, these advantages come with heightened security risks, including data breaches, unauthorized access, and compromised data integrity. Traditional security measures often prove inadequate in the face of the dynamic and complex nature of cloud environments. This study evaluates the effectiveness of machine learning (ML)-based security frameworks for enhancing data protection in public cloud infrastructures. By leveraging ML algorithms' capabilities to analyze large datasets, detect anomalies, and respond to threats in real-time, these frameworks offer a promising solution for robust cloud security. The methodology involves data collection from public cloud environments, feature extraction, selection of appropriate ML models, training and validation of these models, and performance evaluation against traditional security methods. Experimental results demonstrate that ML-based security frameworks significantly improve the detection and mitigation of security threats, offering superior data protection compared to conventional approaches. This research provides valuable insights into the deployment of ML-driven security solutions, contributing to the advancement of cloud security practices and informing organizational strategies for data protection in public cloud environments.
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Joshi, Diksha. "AI-Driven Optimization of Banking Operations Using ML, NLP, and Advanced Techniques for Secure Data Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41145.
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The banking sector faces challenges in operational efficiency, decision-making, and data security. Integrating advanced technologies can address these issues effectively. This study aims to enhance operational efficiency in banking by integrating artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and blockchain technologies. We analyzed a comprehensive dataset of 25,341 documents using various predictive models. A segmentation model was employed to forecast deposit periods. The performance of logistic regression was evaluated and compared to that of a Naive Bayes model. The logistic regression model achieved an accuracy rate of 91.39%, outperforming the Naive Bayes model, which had an accuracy rate of 91.17%. This demonstrates the effectiveness of our approach in providing accurate predictions and insights. This research demonstrates significant advancements in banking operations through the integration of AI, NLP, and blockchain technology. The approach enhances decision-making, improves data processing efficiency, and ensures robust data security. It sets a new benchmark for future innovations in fintech, showcasing substantial performance improvements and practical applications. Key Words: artificial intelligence, machine learning, NLP, blockchain technology, predictive modeling, operational efficiency, data security, banking innovation
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Sandeep, Phanireddy. "AI-Enhanced Linux Security and Server Hardening." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 3 (2020): 1–7. https://doi.org/10.5281/zenodo.15086765.
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Linux has long been celebrated for its stability, versatility, and open-source community support. However, even robust Unix-like systems face threats ranging from opportunistic malware to sophisticated nation-state attacks. Traditional server hardening practices file permission lockdowns, process whitelisting, configuration auditing still matters but can be overwhelmed by the complexity of large-scale or fast-changing infrastructures. This paper explores how AI techniques complement established security measures, from anomaly detection in logs to intelligent process monitoring. By marrying core Unix security principles with machine learning (ML)–based analytics, organizations can safeguard mission-critical servers from zero-day exploits, stealthy intrusions, and misconfigurations. We discuss real-world use cases, highlight key tools, and share recommended workflows to deploy AI-driven threat prevention on Linux systems.
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Ravi Kiran Kodali. "Advancing Machine Learning and Deep Learning Techniques for Predictive Analytics in Cyber Security and Data Science Applications." Journal of Information Systems Engineering and Management 10, no. 9s (2025): 373–82. https://doi.org/10.52783/jisem.v10i9s.1236.
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The rapid evolution of cyber threats and the exponential growth of data-driven applications have necessitated the advancement of predictive analytics techniques in cybersecurity and data science. Machine learning (ML) and deep learning (DL) have emerged as powerful tools for detecting, analyzing, and mitigating cyber threats while also enhancing decision-making processes in data science applications. This paper explores state-of-the-art ML and DL methodologies for predictive analytics, emphasizing their role in proactive security measures and intelligent data analysis. Traditional security approaches often struggle to keep pace with the increasing complexity and volume of cyber threats. The integration of ML and DL offers dynamic, adaptive, and automated solutions that can identify anomalies, predict potential attacks, and strengthen defensive mechanisms. Supervised, unsupervised, and reinforcement learning models have been widely adopted for various cybersecurity applications, including intrusion detection, malware classification, fraud detection, and threat intelligence. Meanwhile, DL architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers have demonstrated superior performance in feature extraction and pattern recognition, enabling advanced predictive analytics in cybersecurity. Beyond security applications, ML and DL play a crucial role in data science, enabling predictive modeling across diverse industries, such as healthcare, finance, and smart cities. Predictive analytics in data science leverages vast datasets to forecast trends, optimize decision-making, and drive innovation. However, challenges such as data privacy, model interpretability, adversarial attacks, and computational complexity must be addressed to ensure the reliability and ethical deployment of AI-driven solutions. This study presents a comprehensive review of the latest advancements in ML and DL for predictive analytics, examining their applications, benefits, and limitations. It also explores hybrid approaches that combine multiple techniques for enhanced accuracy and robustness. The paper further discusses emerging trends, including federated learning for privacy-preserving analytics, explainable AI (XAI) for model transparency, and quantum-enhanced ML for accelerated computations. Through extensive analysis and comparative evaluation, this research highlights the transformative potential of ML and DL in securing digital infrastructures and optimizing predictive analytics. The findings underscore the need for continuous innovation in algorithm design, data handling strategies, and cybersecurity frameworks to counter evolving cyber threats and maximize the utility of AI-driven predictive models. Ultimately, this study contributes to advancing the intersection of ML, DL, cybersecurity, and data science, paving the way for resilient, intelligent, and efficient digital ecosystems.
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Researcher. "THE TRANSFORMATIVE IMPACT OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ON API MANAGEMENT: A COMPREHENSIVE REVIEW." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 602–15. https://doi.org/10.5281/zenodo.14198191.
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Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have catalyzed a paradigm shift in API management, fundamentally transforming how organizations develop, secure, and optimize their API infrastructure. This comprehensive article review examines the convergence of AI/ML technologies with API management systems, analyzing five key domains: intelligent automation, enhanced security frameworks, predictive analytics, discovery mechanisms, and governance protocols. The article systematically analyzes current implementations and emerging trends and demonstrates how AI-driven solutions address traditional API management challenges while enabling unprecedented capabilities such as real-time threat detection, self-healing systems, and automated compliance monitoring. As evidenced by Microsoft Azure's API Management platform, which has successfully implemented AI-driven anomaly detection and predictive scaling, leading to a 78% reduction in API-related incidents and 40% improvement in resource utilization across their cloud services. The findings indicate that organizations implementing AI/ML-enhanced API management systems report significant improvements in operational efficiency (reducing manual intervention by up to 60%), security incident response times (improved by 45%), and developer productivity (increased by 35%). However, these advancements raise critical data privacy concerns, particularly regarding the training data used for AI models and the potential for sensitive information exposure through API interactions. Organizations must carefully balance the benefits of AI-driven API management with robust data protection measures, including data minimization, anonymization techniques, and strict access controls for AI model training data. The article introduces a comprehensive implementation framework based on the Well-Architected principles, providing organizations with structured guidance for strategic planning, phased implementation, quality assurance, and continuous evolution of AI-driven API management systems. While challenges persist in integration complexity, data quality requirements, and organizational adoption, the proposed framework offers a systematic approach to addressing these challenges through architectural considerations, staged implementation methodologies, and robust governance models. This article provides a structured approach to understanding and implementing the transformative impact of AI/ML on API management, offering insights for practitioners while identifying crucial areas for future research and development in this rapidly evolving field.
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Ding, Aaron Yi, Ella Peltonen, Tobias Meuser, et al. "Roadmap for edge AI." ACM SIGCOMM Computer Communication Review 52, no. 1 (2022): 28–33. http://dx.doi.org/10.1145/3523230.3523235.
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Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimisation, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.
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Janaki Sivakumar. "Ai-Driven Cyber Threat Detection: Enhancing Security Through Intelligent Engineering Systems." Journal of Information Systems Engineering and Management 10, no. 19s (2025): 790–98. https://doi.org/10.52783/jisem.v10i19s.3116.
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The rapid proliferation of digital technologies has significantly expanded the attack surface for cyber threats, making traditional security measures increasingly inadequate. Artificial Intelligence (AI)-driven cyber threat detection is emerging as a transformative approach to safeguarding digital ecosystems through intelligent engineering systems. This paper explores the integration of AI and machine learning (ML) techniques in cyber threat detection, focusing on how these advanced technologies enhance security, automate threat intelligence, and mitigate evolving cyber risks in real-time. AI-driven systems leverage sophisticated algorithms such as deep learning, neural networks, and anomaly detection models to identify and respond to cyber threats with unprecedented speed and accuracy. Unlike conventional rule-based security mechanisms, AI-powered threat detection continuously learns from vast datasets. This enables adaptive responses to new and sophisticated attack vectors, including zero-day exploits, ransomware, and advanced persistent threats (APTs). This paper discusses various AI methodologies, including supervised and unsupervised learning models, reinforcement learning, and hybrid AI frameworks that enhance threat identification and response automation. A key challenge in AI-driven cybersecurity is ensuring high detection accuracy while minimizing false positives, which can lead to operational inefficiencies. This study evaluates feature engineering techniques, adversarial AI threats, and explainable AI (XAI) approaches to enhance transparency in AI-based decision-making. Additionally, the role of natural language processing (NLP) in analyzing threat intelligence feeds, social engineering detection, and predictive analytics for proactive threat prevention is examined. Furthermore, the research highlights real-world applications of AI-driven cyber defense in sectors such as finance, healthcare, and critical infrastructure, where cybersecurity breaches can have catastrophic consequences. The integration of AI in Security Operations Centers (SOCs) and its synergy with blockchain technology for enhanced authentication and data integrity is also discussed. Despite its potential, AI-driven cybersecurity faces limitations such as data privacy concerns, adversarial AI attacks, and the need for robust regulatory frameworks to ensure ethical AI usage. This paper presents a roadmap for future research in AI-driven threat detection, emphasizing the importance of collaboration between AI researchers, cybersecurity experts, and regulatory bodies to develop resilient and adaptive security solutions. By leveraging AI’s predictive and autonomous capabilities, organizations can fortify their cybersecurity posture, mitigate risks proactively, and enhance overall digital resilience. This research contributes to the ongoing discourse on intelligent cybersecurity solutions and provides insights into the next generation of AI-enhanced security frameworks.
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Rajitha, Akula, Aravinda K, Amandeep Nagpal, et al. "Machine Learning and AI-Driven Water Quality Monitoring and Treatment." E3S Web of Conferences 505 (2024): 03012. http://dx.doi.org/10.1051/e3sconf/202450503012.
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This study examines the latest utilization of the combination of machine learning (ML) and artificial intelligence (AI) in the monitoring and upgrading of water quality, which has become a crucial component of environmental management. In this paper, a thorough examination of modern methods and recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) algorithms, which have considerably enhanced the precision and effectiveness of water quality tracking systems. The study analyzes the integration of these innovations into water treatment methods, focusing their ability to more efficiently identify and reduce contaminants compared to traditional techniques. This paper examines a collection of case studies in which artificial intelligence (AI)-powered devices have been used, showcasing significant developments in the evaluation of water quality and improved levels of treatment efficiency. The present study additionally analyzes the various problems and potential future developments of Artificial Intelligence (AI) and Machine Learning (ML) within this particular domain. These challenges cover issues of scalability, data security, as well as the importance for interdisciplinary collaboration. This paper gives a comprehensive analysis of the impact of AI and ML technologies on water quality management, demonstrating their potential to transform current practices towards greater sustainability and efficiency.
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Lokendra Singh Kushwah. "Enhancing Payment Ecosystems with AI/ML: Real-Time Analytics for Fraud Prevention and User Insights." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 2124–32. https://doi.org/10.30574/wjarr.2025.26.1.1273.
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The integration of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized payment ecosystems by enhancing fraud prevention, optimizing transaction processing, and personalizing user experiences. AI-driven fraud detection systems leverage real-time analytics and anomaly detection to identify suspicious activities with up to 99.2% accuracy, reducing false positives by 60% while maintaining high transaction approval rates. Machine learning models, including ensemble classification techniques and deep neural networks, enable adaptive security mechanisms that respond to evolving fraud patterns. The implementation of microservices architecture, coupled with intelligent data management strategies, enables unprecedented scalability and performance optimization. Machine learning models, including anomaly detection algorithms and classification systems, work in concert to provide multi-layered security while reducing false positives and maintaining high transaction approval rates. Additionally, AI-powered personalization engines analyze behavioral data to deliver context-aware payment recommendations, improving customer satisfaction by 38% and increasing transaction completion rates by 41%. The implementation of microservices architectures and intelligent data management strategies ensures scalability, resilience, and compliance with global regulatory standards. These technological advancements, combined with sophisticated feature engineering and real-time decision-making capabilities, have established new standards in payment processing efficiency, security, and user experience. As AI continues to evolve, its role in financial security and seamless payment experiences will expand, setting new benchmarks in fraud mitigation, operational efficiency, and user-centric payment processing.
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Thelma, Chanda Chansa, Zohaib Hassan Sain, Yusuf Olayinka Shogbesan, Edwin Vinandi Phiri, and Wisdom Matthew Akpan. "Ethical Implications of AI and Machine Learning in Education: A Systematic Analysis." International Journal of Instructional Technology 3, no. 1 (2024): 1–13. https://doi.org/10.33650/ijit.v3i1.9364.
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Ethical considerations should be examined to determine how AI and ML affect education. Educational AI and ML bring privacy, security, and student data usage problems. This research examined AI and ML ethics in higher education at selected universities. Ethical issues AI and machine learning in education provide fairness, privacy, and openness. AI training data may perpetuate educational biases and impair student achievement. For complete comprehension, mixed methods research included quantitative and qualitative data. Four Lusaka district universities contributed 100 survey respondents. The initiative included four universities' department chairs, professors, and students. Structured open-ended interviews and questionnaires collected data. Quantitative questionnaire data was descriptively examined in SPSS and Excel, while semi-structured interview data was thematically evaluated. According to research, AI may reduce educational monitoring and learner engagement. Another concern is the digital gap and AI access. AI's sophisticated skills may be inaccessible to impoverished students, worsening educational inequity. The report advised training students and staff on data security and providing explicit permission procedures for data use in AI-driven educational systems, including strong encryption, anonymisation, and access limits.
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Kumar, Keshav. "Artificial Intelligence for Improving Cybersecurity Framework." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48949.
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Abstract As the attack types become more sophisticated, the ones in use today are losing their touch due to various reasons. Chief among these include zero-day exploits, AI-driven phishing, and polymorphic malware. This study explores incorporating artificial intelligence (AI) in cyber security frameworks to counter such threats, thereby proposing to shift the focus from reactive to proactive and adaptive mechanisms. It employs machine learning (ML) algorithms, neural networks, and natural language processing (NLP) to show how AI can better threat detection, automate incident response, and predict vulnerabilities in real-time. A new AI-based framework marries supervised learning for anomaly detection, reinforcement learning for adaptive protocol optimization, and generative adversarial networks (GANS) to simulate and counter advanced persistent threats (APTs).A set of examples is provided that validates the real-life functionality of the proposed framework in NIDS and cloud security environments and reveals a 40% speed improvement in threat identification and a 35% decrease in false positives compared to rule-based systems. Simultaneously, the study also deals with other ethical and operational issues such as adversarial attacks on AI models, privacy of valid data, and the "black box" problem of ML in decision-making. Using explainable AI (XAI) techniques and federated learning for distributed data processing, the proposed framework contends with the balancing act between transparency and robust security.This study presents the potential of AI to craft self-healing, context-sensitive cyber security infrastructures and summons standard regulatory guidelines governing AI on critical systems. The findings performed aim to empower departments to adopt intelligent, scale able defenses as the cyber warfare continues escalating. Keywords: AI-Driven Cyber security, Proactive Threat Detection, Adaptive Security Frameworks, Explainable AI (XAI), Machine Learning in Intrusion Detection
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Kashyap, Gaurav. "AI for Threat Detection and Mitigation: Using AI to identify and respond to cybersecurity threats in real-time." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem10936.
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As the digital landscape evolves and cyber threats become increasingly sophisticated, traditional security systems struggle to keep up with the volume, variety, and velocity of attacks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity by enabling the automated detection, analysis, and mitigation of threats in real-time. By leveraging machine learning (ML) algorithms, natural language processing (NLP), and anomaly detection, AI can process vast amounts of data, identify patterns, and respond to potential threats faster and more accurately than conventional methods. This paper explores the role of AI in modern cybersecurity, focusing on its applications in threat detection and mitigation. It examines how AI systems, such as intrusion detection systems (IDS), security information and event management (SIEM) platforms, and endpoint protection tools, are being used to combat cyber threats. The paper also discusses the challenges associated with implementing AI in cybersecurity, including false positives, adversarial attacks, and the need for continuous training, and offers insights into future trends in AI-driven threat mitigation. Keywords: Cyber Security, Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning (ML), Deep Learning.
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Khadija, Danladi Sankara, and A. Senthil kumar Dr. "Review on Analyzing the Latest Trends in AI and ML within Software Engineering: A Comprehensive Examination of Emerging Technologies." SEKA : JOURNAL OF MULTIDISCIPLINARY STUDIES 1, no. 1 (2024): 10–17. https://doi.org/10.5281/zenodo.13944565.
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<strong>DOI: </strong><strong>10.5281/zenodo.11371993</strong> <strong>Abstract---</strong>This research paper embarks on an extensive exploration of the dynamic landscape where Artificial Intelligence (AI) and Machine Learning (ML) intersect with Software Engineering. The investigation takes a deep dive into three pivotal emerging trends: Robotic Process Automation (RPA), 6G network and communication protocols, and AI-driven cybersecurity. The study aims to provide a nuanced understanding of these technologies, unraveling their applications, challenges, and implications for the evolution of software development practices. This collective understanding aims to inform strategic decision-making, foster innovation, and optimize software development processes in the ever-evolving digital landscape. In essence, this research paper contributes to the broader narrative of the latest trends in AI and ML within Software Engineering, providing a road map for stakeholders to navigate and harness the transformative potential of these emerging technologies in the pursuit of efficient, secure, and cutting-edge software development practices.
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Ivan, Mishchenko. "Biometric Authentication in Android: Enhancing Security with AI-Powered Solutions." Asian Journal of Research in Computer Science 18, no. 4 (2025): 416–26. https://doi.org/10.9734/ajrcos/2025/v18i4629.
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Aims: This study aims to analyze biometric authentication methods on the Android platform, focusing on enhancing security through ready-to-use AI solutions. The research evaluates existing biometric authentication techniques, their vulnerabilities, and the application of AI-driven approaches to mitigate security risks. Study Design: This is a review and analytical study that examines current biometric authentication mechanisms, AI-based enhancements, and their impact on security and accuracy. Place and Duration of Study: The study is based on literature review and practical analysis of AI-enhanced biometric authentication methods applied in real-world Android applications. Methodology: The research explores the evolution of biometric authentication in Android, emphasizing the use of AI-driven tools such as ML Kit for Face Detection, TensorFlow Lite, and OpenCV. The study assesses the effectiveness of these technologies in improving recognition accuracy, reducing false acceptance and rejection rates, and addressing security threats such as spoofing attacks. Performance metrics, including False Acceptance Rate (FAR), False Rejection Rate (FRR), and processing time, were used to evaluate AI-enhanced solutions. Results: The findings indicate that AI-based enhancements significantly reduce the FAR by 15–20%, improving the overall reliability of biometric authentication. Machine learning models and image preprocessing techniques help adapt authentication to varying conditions, such as poor lighting and occlusions. However, AI integration introduces increased computational overhead, slightly extending processing time from 500ms to 700–800ms. Hardware-backed security measures mitigate risks associated with biometric data storage and manipulation. Conclusion: AI-driven biometric authentication methods substantially improve security and accuracy on Android devices, addressing key vulnerabilities in traditional biometric techniques. Despite minor processing time increases, the trade-off is justified by enhanced protection against spoofing attacks and improved adaptability to environmental conditions. Future research should focus on optimizing AI models for mobile efficiency and developing multi-factor authentication approaches to further enhance security.
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Natarajan Sankaran. "Enhancing IoT edge intelligence: Machine learning-driven visualization for smart cities decision-making." World Journal of Advanced Research and Reviews 19, no. 2 (2023): 1680–91. https://doi.org/10.30574/wjarr.2023.19.2.1685.
Full textAbstract:
Revolutionizing data processing, security and real-time decision making, the move to IoT edge intelligence is advancing the state of the art in how we approach these and all challenges of modern business. Latency, bandwidth constraints, security vulnerability are the traditional pain points of traditional cloud-based service models, edge computing is a critical solution. The IoT systems can be made more responsive, better able to utilize resources more effectively, and more secure by way of integrating ML driven visualization and edge AI strategies. Nevertheless, there are still some challenges about this such as scaling, data privacy, and computational efficiency. These risks can be mitigated with the solutions like federated learning, blockchain integration and then the anomaly detection, and all that data can actually flow seamlessly and securely. Edge AI takes the best of centralized cloud along with cost efficiency of distributed systems and results in reducing dependence on centralized cloud infrastructure, and optimizing data processing by doing the computation locally to lower latency and save bandwidth. Furthermore, ML based visualization tools help in making IoT applications efficient for smart cities, health-care and industrial automation domains. Though the technology was developed years ago, security continues to be a key consideration as blockchain technology ensures secure, tamper proof data management, while federated learning ensures that data is private because it is decentralized during training. It is expected that later IoT edge intelligence can be advanced further from emerging technology such as quantum computing and AI driven automation. Such advancements will enable more scalable, secure and efficient processing frameworks that would lead to making intelligent, autonomous decisioning in the real time environment. As organizations adopt the edge AI solutions, it is important to address their current limitations and exploit the future innovation for the further growth and efficiency of IoT ecosystems.
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Yang, Zheng, Yuting Zhang, Jie Zeng, et al. "AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models." Drones 9, no. 6 (2025): 392. https://doi.org/10.3390/drones9060392.
Full textAbstract:
As unmanned aerial vehicle (UAV) applications expand across logistics, agriculture, and emergency response, safety and security threats are becoming increasingly complex. Addressing these evolving threats, including physical safety and network security threats, requires continued advancement by integrating traditional artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL), which contribute to significantly enhancing UAV safety and security. Large language models (LLMs), a cutting-edge trend in the AI field, are associated with strong capabilities for learning and adapting across various environments. Their emergence reflects a broader trend toward intelligent systems that may eventually demonstrate behavior comparable to human-level reasoning. This paper summarizes the typical safety and security threats affecting UAVs, reviews the progress of traditional AI technologies, as described in the literature, and identifies strategies for reducing the impact of such threats. It also highlights the limitations of traditional AI technologies and summarizes the current application status of LLMs in UAV safety and security. Finally, this paper discusses the challenges and future research directions for improving UAV safety and security with LLMs. By leveraging their advanced capabilities, LLMs offer potential benefits in critical domains such as urban air traffic management, precision agriculture, and emergency response, fostering transformative progress toward adaptive, reliable, and secure UAV systems that address modern operational complexities.
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Al-Bermani, Noora Kadhim, Ali Kadhim Bermani, Abeer Raad, and Mehdi Ebady Manaa. "AI-driven cybersecurity-based hybrid approach using blockchain for smart grids." Journal of Discrete Mathematical Sciences and Cryptography 28, no. 4-B (2025): 1399–411. https://doi.org/10.47974/jdmsc-2286.
Full textAbstract:
Power systems have become more efficient, reliable, and sustainable as smart grids are increasingly integrated into them. Developing innovative security solutions is often required to counter evolving cyber threats because traditional security mechanisms lack real-time protection. The purpose of this paper is to propose a blockchain-based hybrid optimization approach for enhancing smart grid security and resilience. To improve decision-making, resource allocation, and attack mitigation, the framework incorporates artificial intelligence (AI) to detect anomalies in real-time, blockchain technology to store unrestricted data, and a hybrid optimization algorithm to make decisions in real-time. Adaptive Vulture Optimization Algorithm (AVOA) and Convolutional Neural Networks (CNN) combine to reduce computational overhead and maintain detection accuracy effectively. According to the proposed approach, which is compared to existing models, including ML-ID, HHT, and THD, it achieves superior performance, with a detection rate of 99.17%, a reduction in computation time, and enhanced scalability. These results demonstrate that AI-Blockchain hybrid frameworks are effective in protecting smart grids against emerging cyber threats, making them a reliable and scalable security solution.