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1
Kingsley David Onyewuchi Ofoegbu, Olajide Soji Osundare, Chidiebere Somadina Ike, Ololade Gilbert Fakeyede, and Adebimpe Bolatito Ige. "Data-Driven Cyber Threat Intelligence: Leveraging Behavioral Analytics for Proactive Defense Mechanisms." Computer Science & IT Research Journal 4, no. 3 (2023): 502–24. http://dx.doi.org/10.51594/csitrj.v4i3.1501.
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As the cyber threat landscape becomes increasingly sophisticated, traditional defense mechanisms often fall short in anticipating and mitigating advanced attacks. The paper explores the critical role of behavioral analytics in transforming cyber threat intelligence (CTI) into a proactive defense strategy. This study underscores the importance of analyzing user behavior patterns, network activity, and system interactions to detect anomalies that may indicate potential threats. By leveraging large datasets and advanced analytical techniques, organizations can move beyond reactive cybersecurity measures, instead anticipating and preventing attacks before they fully manifest. The integration of behavioral analytics with CTI provides a comprehensive understanding of both external threats and internal vulnerabilities, enabling the development of dynamic defense mechanisms that adapt to the evolving threat environment. The research also discusses the benefits of automated threat intelligence platforms, which use machine learning algorithms to continuously analyze behavioral data and refine threat detection models in real-time. This approach reduces the reliance on manual analysis, enhances the speed and accuracy of threat detection, and minimizes false positives. Moreover, the study highlights the importance of cross-sector collaboration and data sharing in building a robust CTI framework that leverages collective intelligence to combat cyber threats. Case studies of successful implementations in various industries demonstrate the practical applications and efficacy of behavioral analytics in enhancing cybersecurity resilience. The findings suggest that organizations that adopt data-driven, behavior-focused CTI are better equipped to defend against both known and unknown threats, ensuring a more secure digital environment. This proactive approach to cybersecurity not only strengthens organizational defenses but also contributes to the broader goal of creating a safer cyber ecosystem by identifying and neutralizing threats at their inception. Keywords: Data-Driven, Cyber Threat, Intelligence, Bahavioral Analytics, Proactive Defense Mechanism.
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REDDY, PERLA MAHESH, and SMT. M. VANI. "Cyber Threat Intelligence Analysis for Proactive Cybersecurity Defense: A Survey and New Perspectives." Journal of Engineering Sciences 16, no. 04 (2025): 107–13. https://doi.org/10.36893/jes.2025.v16i04.018.
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Cyber threats continue to evolve in complexity and sophistication, posing significant risks to organizations, governments, and individuals. Traditional reactive security measures are often insufficient to prevent advanced cyberattacks, leading to increased interest in Cyber Threat Intelligence (CTI) as a proactive defense strategy. This paper presents a comprehensive survey of CTI methodologies, data sources, and analytical techniques used for cybersecurity defense. It examines the role of threat intelligence in identifying, analyzing, and mitigating potential cyber threats before they materialize. Additionally, the paper explores new perspectives and emerging trends in CTI, including artificial intelligence (AI)-driven threat detection, big data analytics, and automated threat intelligence sharing. The proposed framework leverages machine learning and real-time data analysis to enhance cybersecurity resilience. Experimental results demonstrate the effectiveness of proactive threat intelligence in reducing response time and improving overall cybersecurity posture.
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Dr. Rajitha Kotoju and Md. Abrar Khan. "Cognitive Cyber Threat Intelligence: AI-Driven Behavioural Profiling for Proactive Security." international journal of engineering technology and management sciences 9, Special Issue 1 (2025): 156–59. https://doi.org/10.46647/10.46647/ijetms.2025.v09si01.025.
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The rise of sophisticated cyber threats necessitates a shift from reactive security measures toproactive cyber defense. Cognitive Cyber Threat Intelligence (CCTI) leverages AI-drivenbehavioural profiling to predict and mitigate cyber-attacks before they occur. By analyzing attackerpatterns, threat intelligence data, and real-time system anomalies, CCTI enhances situationalawareness and automates threat detection. This paper explores the integration of machine learning,behavioural analytics, and cognitive computing to develop a dynamic cybersecurity frameworkcapable of adaptive threat intelligence. We also discuss the impact of predictive analytics on cyberdefense strategies and how AI can identify, classify, and neutralize cyber threats with minimal humanintervention. Through case studies and experimental analysis, this research highlights theeffectiveness of CCTI in reducing attack surfaces and strengthening cybersecurity resilience. Thefindings contribute to advancing automated, intelligence-driven security mechanisms that align withmodern cyber defense requirements.
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Krutika Dwarka Naidu, Dr. Syed Irfan Ali, Sujal Shyam Hasoriya, and Sujal Ganvir. "Threat Foresight: Web Threat Detection and Forecasting Trends and Insights." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 129–33. https://doi.org/10.32628/ijsrst25122209.
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The increasing sophistication and frequency of web threats necessitate advanced analytics and forecasting techniques to mitigate potential cyber risks. Traditional security measures, while effective to some extent, often struggle to adapt to evolving cyber threats. The advent of Artificial Intelligence (AI) and Generative AI (GenAI) has introduced novel methodologies for detecting, analyzing, and predicting web-based threats. This review paper explores the landscape of web threat analytics, evaluates traditional and modern forecasting techniques, and examines the role of AI and GenAI in enhancing cybersecurity. Furthermore, it highlights the challenges, limitations, and future directions in web threat analytics to guide future research and development.
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Enyinaya Stefano Okafor, Olatunji Akinrinola, Favour Oluwadamilare Usman, Olukunle Oladipupo Amoo, and Nneka Adaobi Ochuba. "CYBERSECURITY ANALYTICS IN PROTECTING SATELLITE TELECOMMUNICATIONS NETWORKS: A CONCEPTUAL DEVELOPMENT OF CURRENT TRENDS, CHALLENGES, AND STRATEGIC RESPONSES." International Journal of Applied Research in Social Sciences 6, no. 3 (2024): 254–66. http://dx.doi.org/10.51594/ijarss.v6i3.854.
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Cybersecurity is a critical concern in satellite telecommunications networks, given their vulnerability to cyber threats. This abstract presents a conceptual development of current trends, challenges, and strategic responses in using cybersecurity analytics to protect these networks. The paper discusses the increasing reliance on satellite telecommunications, making them attractive targets for cyber attacks. It explores the role of cybersecurity analytics in detecting and mitigating these threats, highlighting the importance of proactive monitoring and threat intelligence. Challenges in cybersecurity analytics for satellite networks are identified, including the complexity of satellite systems, the limited visibility into network traffic, and the evolving nature of cyber threats. The paper discusses strategic responses to these challenges, such as the use of advanced analytics techniques, machine learning, and artificial intelligence to enhance threat detection and response capabilities. Key trends in cybersecurity analytics for satellite networks are examined, including the growing adoption of cloud-based security solutions, the rise of insider threats, and the need for collaboration between satellite operators and cybersecurity experts. The paper also discusses the importance of regulatory compliance and the role of industry standards in ensuring the security of satellite networks. In conclusion, the paper emphasizes the importance of cybersecurity analytics in protecting satellite telecommunications networks and recommends a proactive approach to cybersecurity that includes continuous monitoring, threat intelligence sharing, and collaboration with cybersecurity experts.
 Keywords: Cybersecurity, Analytics, Satellite, Telecommunications, Conceptual Development.
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Kumar Kande, Santosh. "Proactive Phishing Threat Exposure Mitigation through Adaptive Vulnerability Management: Utilizing Threat Intelligence, User Behavior Analytics, and Predictive Analytics." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 2141–561. http://dx.doi.org/10.21275/sr241010075237.
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Ismail, Assoujaa. "Designing Effective Threat Hunting to Enhance Security Programs." WSEAS TRANSACTIONS ON COMMUNICATIONS 23 (December 30, 2024): 142–48. https://doi.org/10.37394/23204.2024.23.19.
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Cyber threat hunting is a proactive cybersecurity approach focused on identifying threats that evade traditional security measures. It involves the integration of human expertise, data analytics, and advanced tools to detect anomalies within organizational networks and systems. Despite its potential, many organizations remain dissatisfied with their threat hunting programs due to gaps in required analytical skills and the lack of integration of advanced techniques such as machine learning. This paper explores the design of an effective threat hunting exercise, examining its role in complementing traditional security measures. It emphasizes the importance of advanced data analytics, threat intelligence integration, and automation to enhance the effectiveness of threat hunting. The proposed framework underscores the significance of the data collection and analysis process, improving detection rates and reducing the impact of advanced threats. This study also addresses the challenges faced in threat hunting, including skills gaps and the need for better tools, and outlines strategies for overcoming these obstacles to create more robust security programs.
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Falasi, Dr Mariam Al, and Dr Tao Zhang. "AUGMENTING SIEM WITH THREAT INTELLIGENCE FOR PREDICTIVE CYBER DEFENSE: A PROACTIVE THREAT HUNTING APPROACH." International Journal of Cyber Threat Intelligence and Secure Networking 2, no. 03 (2025): 1–5. https://doi.org/10.55640/ijctisn-v02i03-01.
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Security Information and Event Management (SIEM) systems play a crucial role in detecting and responding to cyber threats through real-time monitoring and log analysis. However, traditional SIEMs often struggle with proactively identifying emerging threats. This paper explores the augmentation of SIEM platforms with external and internal Cyber Threat Intelligence (CTI) to enhance predictive cyber defense capabilities. By integrating threat intelligence feeds, behavioral analytics, and machine learning techniques, the proposed approach transforms SIEMs from reactive tools into proactive threat hunting systems. The study reviews current architectures, implementation challenges, and real-world use cases, demonstrating how enriched SIEM environments improve threat detection, reduce false positives, and support faster incident response. The paper also outlines future directions for building adaptive, intelligence-driven security operations.
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Yogeswara, Reddy Avuthu. "Cloud-Native Security Analytics: Real-Time Threat Intelligence in DevSecOps Pipelines Using AI and Big Data." Journal of Scientific and Engineering Research 8, no. 8 (2021): 253–61. https://doi.org/10.5281/zenodo.14274206.
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As cloud-native applications continue to grow in scale and complexity, the need for real-time threat detection and automated security measures has become increasingly critical. The integration of Artificial Intelligence (AI) and big data analytics into DevSecOps pipelines offers a robust solution for enhancing cloud-native security by enabling real-time threat intelligence and automated threat mitigation. This paper presents a novel framework for cloud-native security analytics that utilizes AI models to detect anomalies in real-time and provides predictive threat intelligence by processing large volumes of data from distributed cloud environments. The proposed system continuously monitors security events and autonomously responds to potential threats by employing AI-based anomaly detection, predictive analytics, and big data-driven insights. We demonstrate the effectiveness of this framework by evaluating it in a simulated cloud-native environment, achieving high anomaly detection accuracy and near-instantaneous response times. This research provides an innovative approach to enhancing the security posture of cloud-native infrastructures while maintaining the agility and speed of modern DevSecOps practices.
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Tanjung, Rully Eko Setiawan, Muhammad Syaroni Rofii, and Stepi Anriani. "Customs Intelligence Surveillance and Analysis Tools in Anticipation of Smuggling Threats." Indonesian Journal of Multidisciplinary Science 2, no. 12 (2023): 4230–43. http://dx.doi.org/10.55324/ijoms.v2i12.668.
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Customs intelligence activities carried out by the intelligence unit are carried out in the context of early detection of violations in the field of customs and excise. One of the threats facing customs is the threat of smuggling which can be detrimental to the state and society. the purpose of this study is to determine customs intelligence supervision and the use of external data service applications that become customs intelligence analysis tools. This study uses a qualitative method with a descriptive approach. The results of the first study show that the surveillance activities that have been carried out by customs intelligence are in accordance with the intelligence cycle which includes collection, assessment, analysis, distribution, evaluation and updating of data and/or information. Customs violations related to export-import activities can be categorized as smuggling crimes. The results of the second study show that customs intelligence analysts use external data service applications such as: Global Trade Atlas (GTA), Automatic Identification System (AIS), Marine Traffic, Sea Web, Panjiva, Questnet, Intelligence Media Analytics (IMA), and Intelligence Socio. Analytics (ISA) for targeting, profiling, and document research. Supervision by customs intelligence using the help of external data service applications is utilized by intelligence analysts as an analytical tool in order to anticipate the threat of smuggling.
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Baskaran, Saravanakumar. "A Comprehensive Framework for Threat Intelligence-Driven Incident Detection." International Journal of Scientific Research and Management (IJSRM) 7, no. 08 (2019): 288–93. http://dx.doi.org/10.18535/ijsrm/v7i8.ec01.
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The increasing complexity of cybersecurity threats demands more advanced and intelligence-driven methods for incident detection. Traditional security measures are often reactive, leaving organizations vulnerable to sophisticated attacks. This article presents a comprehensive framework that integrates threat intelligence into incident detection processes, enhancing the ability to detect, respond to, and mitigate cyber threats in real-time. By leveraging actionable threat intelligence data, organizations can stay ahead of emerging threats and improve their overall cybersecurity posture. This framework highlights the use of machine learning models, data analytics, and automated incident response tools, ensuring efficient, real-time detection and minimizing false positives.
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Edim Bassey Edim, Akpan Itoro Udofot, and Omotosho Moses Oluseyi. "AI-augmented cyber security threat intelligence – enhancing situational awareness." International Journal of Science and Research Archive 14, no. 1 (2025): 890–97. https://doi.org/10.30574/ijsra.2025.14.1.2650.
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In the evolving landscape of cyber threats, traditional threat intelligence methods are increasingly inadequate for addressing the complexity and speed of modern attacks. This paper explores the transformative impact of Artificial Intelligence (AI) on enhancing cyber security threat intelligence and situational awareness. By leveraging advanced AI technologies—such as machine learning, natural language processing, and data analytics—organizations can significantly improve their ability to detect, analyze, and respond to threats. We provide a comprehensive review of current AI applications in threat intelligence, illustrating how these technologies enable proactive threat management and enhance situational awareness. Through detailed case studies, we demonstrate the effectiveness of AI-driven solutions in various sectors, including finance and healthcare. The paper also addresses key challenges such as data privacy, system integration, and adversarial AI, offering recommendations for future research and development. This study underscores the critical role of AI in advancing cyber security practices and provides insights into how organizations can harness AI to achieve a more robust and responsive threat intelligence framework.
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ABDULRAHMAN, IBRAHIM ABDUL, UZOAMAKA C. OGOR, GABRIEL TOSIN AYODELE, CHIDOZIE ANADOZIE, and JACOB ALEBIOSU. "AI-Driven Threat Intelligence and Automated Incident Response: Enhancing Cyber Resilience through Predictive Analytics." Research Journal in Civil, Industrial and Mechanical Engineering 2, no. 1 (2025): 16–32. https://doi.org/10.61424/rjcime.v2i1.236.
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Cybersecurity is a critical concern for organizations as the complexity and volume of cyber threats continue to grow. Traditional methods of threat detection and incident response, such as signature-based detection and rule-based systems, are increasingly ineffective against sophisticated and evolving attacks. This study explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing threat intelligence and automating incident response. By leveraging predictive analytics, anomaly detection, and real-time data processing, AI-driven systems offer significant improvements in both the detection and mitigation of cyber threats. The research evaluates the effectiveness of an AI-powered threat intelligence system across various attack types, including phishing, ransomware, DDoS attacks, Advanced Persistent Threats (APTs), and malware variants. Results show that the AI system achieves a 94.44% detection rate for phishing attacks, with significant improvements in response times and mitigation accuracy. Predictive analytics further enhances cyber resilience by forecasting potential threats with 90% accuracy, allowing for proactive defense strategies. Despite the positive results, the study acknowledges limitations such as dataset diversity, model biases, and scalability issues. The findings suggest that AI, when integrated with human expertise, can revolutionize cybersecurity by providing faster, more accurate, and scalable solutions. Future research should focus on improving the explainability of AI models, addressing ethical concerns, and exploring the scalability of AI-driven solutions in large-scale networks. The study advocates for the adoption of predictive analytics as a core element in cybersecurity practices to build more resilient systems capable of combating the increasing threat landscape.
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Sabeeruddin, Shaik. "Insider Threat Monitoring Frameworks: Leveraging Behavioral Analytics." International Journal on Science and Technology 15, no. 2 (2024): 1–7. https://doi.org/10.5281/zenodo.14752331.
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Insider threats provide a significant risk to organizational security due to their access to essential systems and sensitive information. This article examines how behavioral analytics might improve insider threat monitoring systems, providing firms with preemptive methods to identify and mitigate potential risks. Utilizing machine learning and artificial intelligence (AI), behavioral analytics facilitates real-time monitoring and anomaly detection, hence enhancing organizational resilience. This study explores the issue statement, proposes a solution through behavioral analytics, and assesses its applications, effects, and extent. This study also addresses the problems and future prospects of behavioral analytics for insider threat detection, enabling firms to adapt to changing security environments. Emphasis is placed on incorporating behavioral models, ethical considerations, and organizational preparedness for implementing these solutions.
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Dr.Vijayalakshmi Chintamaneni, Dr.M.SreeRamu, and Shaik Abubakar Siddiq. "Insider Threats in the Age of Cyber Threat Intelligence: Behavioral Indicators and Detection Strategies." international journal of engineering technology and management sciences 9, Special Issue 1 (2025): 132–43. https://doi.org/10.46647/ijetms.2025.v09si01.022.
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Insider threats remain one of the most challenging aspects of cyber security, as they oftenbypasstraditional perimeter defenses. This paper explores how cyber threat intelligence (CTI) canenhance insider threat detection through behavioral analytics, anomaly detection, and machinelearning-based profiling. We investigate real-world insider threat incidents across the financial sector,critical infrastructure, and corporate environments to identify key indicators of malicious activity. Byintegrating AI-driven risk scoring models with CTI frameworks, we propose a predictive approachthat improves early threat detection and mitigation. Our findings emphasize the importance ofcontinuous monitoring, access control, and intelligence-sharing to counter evolving insider threatseffectively.
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Brandao, Pedro Ramos. "Exploring the Role of Artificial Intelligence in Detecting Advanced Persistent Threats." Computers 14, no. 7 (2025): 245. https://doi.org/10.3390/computers14070245.
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The rapid evolution of cyber threats, particularly Advanced Persistent Threats (APTs), poses significant challenges to the security of information systems. This paper explores the pivotal role of Artificial Intelligence (AI) in enhancing the detection and mitigation of APTs. By leveraging machine learning algorithms and data analytics, AI systems can identify patterns and anomalies that are indicative of sophisticated cyber-attacks. This study examines various AI-driven methodologies, including anomaly detection, predictive analytics, and automated response systems, highlighting their effectiveness in real-time threat detection and response. Furthermore, we discuss the integration of AI into existing cybersecurity frameworks, emphasizing the importance of collaboration between human analysts and AI systems in combating APTs. The findings suggest that the adoption of AI technologies not only improves the accuracy and speed of threat detection but also enables organizations to proactively defend against evolving cyber threats, probably achieving a 75% reduction in alert volume.
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Sandhya, Guduru. "AI-Driven Threat Hunting: Sigma Rules, Elastic EQL, and MITRE CAR Analytics in Splunk UBA." Journal of Scientific and Engineering Research 8, no. 12 (2021): 239–43. https://doi.org/10.5281/zenodo.15387322.
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Artificial intelligence (AI) has transformed threat hunting by improving the accuracy and efficiency of detecting malicious activities. This paper examines the use of Sigma rules, Elastic Event Query Language (EQL), and the MITRE Cyber Analytics Repository (CAR) within Splunk User Behaviour Analytics (UBA) to enhance cyber threat detection. Sigma rules provide a structured approach to writing detection signatures, while Elastic EQL enables causality-based event analysis. MITRE CAR offers a standardised framework for threat analytics. Additionally, graph neural networks (GNNS) are applied to Zeek/Bro logs to identify patterns of adversarial behaviour. By integrating these techniques, organisations can improve their ability to detect, analyse, and respond to cyber threats in real time.
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Reddy, Mr K. V. Siva Prasad, B. Mohith, P. Mahesh Babu, and K. Navtej. "CyberSleuth AI: Intelligent Network Forensics Analyzer." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1643–47. https://doi.org/10.22214/ijraset.2025.68420.
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Abstract: CyberSleuth represents a cutting-edge cybersecurity initiative designed to protect Canada's critical infrastructure through advanced threat detection and response capabilities. This comprehensive system combines artificial intelligence, machine learning, and human expertise to provide real-time monitoring, analysis, and protection against evolving cyber threats. By leveraging AI-driven analytics for network traffic analysis, anomaly detection, and automated threat response, CyberSleuth processes vast amounts of security data to identify potential threats while minimizing false positives. The system's architecture integrates multiple layers of security, including predictive analytics, behavioral analysis, and automated incident response mechanisms, all while maintaining a human-in-the-loop approach for critical decision-making. Through its partnership model between the Government of Canada and critical infrastructure organizations, CyberSleuth facilitates rapid threat intelligence sharing and collaborative defense strategies. This hybrid approach of combining advanced technology with human expertise and interorganizational cooperation creates a robust framework for protecting vital infrastructure against sophisticated cyber attacks. The system's success in early threat detection, incident response automation, and cross-sector collaboration demonstrates its effectiveness in strengthening national cybersecurity resilience
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Rahul Bhatia. "The Future of SIEM: How AI and ML Are Rewriting Threat Detection." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 459–68. https://doi.org/10.32996/jcsts.2025.7.7.50.
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Security Information and Event Management (SIEM) systems have undergone a fundamental transformation through the integration of artificial intelligence and machine learning technologies. This article traces the evolution from traditional rule-based detection methods to sophisticated AI-enhanced platforms capable of identifying complex attack patterns. Modern SIEM solutions leverage deep learning architectures, unsupervised anomaly detection, behavioral analytics, and natural language processing to overcome historical limitations. Real-world implementations demonstrate significant operational improvements, including earlier threat detection, reduced false positives, and automated response capabilities. Despite these advancements, persistent challenges exist regarding model deterioration, data quality, privacy considerations, and interpretability requirements. Future directions include federated learning approaches that maintain privacy while enabling collaborative threat intelligence, quantum-resistant analytics preparing for post-quantum threats, human-AI collaboration frameworks optimizing analyst workflows, and standardized evaluation methodologies for security-specific implementations. This technological progression represents a paradigm shift from reactive notification systems to proactive threat hunting platforms capable of addressing sophisticated attack methodologies in contemporary threat landscapes.
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Datta, N. Bala Suresh. "Holistic Cyber Threat Intelligence System with Bert for Advanced Threat Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2662–64. https://doi.org/10.22214/ijraset.2025.68780.
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Cyber threats are evolving at an unprecedented rate, making traditional security measures insufficient in detecting and mitigating sophisticated attacks. This project introduces an AI-powered Cyber Threat Intelligence System that leverages machine learning, natural language processing (NLP), and automated threat analysis to enhance cybersecurity defenses. The system integrates data from multiple threat intelligence sources, such as OSINT feeds, security reports, and real-time network traffic, to identify, classify, and prioritize security threats. By employing a BERT-based NLP engine, the system can extract relevant threat entities, assign risk scores, and recommend mitigation strategies. Additionally, it incorporates Security Information and Event Management (SIEM) integration to facilitate automated security responses and real-time alerts. To improve accuracy and efficiency, the system utilizes a combination of supervised and unsupervised learning models, ensuring it adapts to new and emerging cyber threats. A key feature of the system is its automated threat prioritization mechanism, which helps security analysts focus on the most critical vulnerabilities first. The platform also supports API-based integrations with existing enterprise security solutions, enabling seamless deployment in various organizational environments. Unlike traditional signature- based detection methods, this system employs behavioral analytics to identify anomalies and zero-day threats proactively. By continuously learning from past incidents and new attack patterns, the system enhances overall cybersecurity resilience, reducing response time and improving threat intelligence capabilities.
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Nagamalla, Vishwesh, J. Raj karkee, and Ravi Kumar Sanapala. "Integrating Predictive Big Data Analytics with Behavioral Machine Learning Models for Proactive Threat Intelligence in Industrial IoT Cybersecurity." International Journal of Wireless and Ad Hoc Communication 7, no. 2 (2023): 08–24. http://dx.doi.org/10.54216/ijwac.070201.
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This paper introduces a comprehensive framework for industrial Internet of Things (IoT) cybersecurity, integrating multiple algorithms to enhance threat intelligence. The proposed framework encompasses five key algorithms, each addressing specific aspects of data preprocessing, time series analysis, predictive analytics, and behavioral machine learning. The Data Preprocessing and Integration algorithm refines raw IoT data through a meticulous 20-step process, ensuring high-quality input for subsequent analyses. The Time Series Analysis algorithm delves into temporal patterns, while the Random Forest algorithm focuses on predictive analytics for proactive threat detection. The LSTM Ensemble algorithm extends the analysis into behavioral machine learning, capturing temporal dependencies and detecting anomalies. The Weighted Average Ensemble combines outputs from predictive analytics and behavioral models, leveraging their correlation for enhanced threat intelligence. An ablation study dissects the individual contributions of each algorithmic component, shedding light on their specific impacts. The results highlight the significance of each step, guiding optimizations for improved performance. The proposed framework outperforms existing methods in various performance metrics, showcasing its potential as a robust solution for proactive threat intelligence in complex industrial environments. This framework stands at the forefront of industrial IoT cybersecurity, offering a holistic and adaptive approach to address evolving threats. The ablation study enhances the transparency and understanding of the framework, contributing to its continuous refinement and effectiveness in safeguarding critical industrial systems.
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John, Jeo. "Enhancing Cybersecurity Posture through Dynamic Vulnerability Matching and Threat Intelligence Integration Precious." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35927.
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As the digital landscape continues to evolve, organizations face increasingly sophisticated cyber threats that challenge traditional cybersecurity measures. In response, this paper proposes a novel approach to bolstering cybersecurity posture by integrating dynamic vulnerability matching and threat intelligence. The proposed framework combines proactive identification of vulnerabilities within an organization's network with real-time threat intelligence feeds. Leveraging advanced analytics and machine learning algorithms, the system dynamically matches vulnerabilities to relevant threat intelligence, allowing for prioritized remediation efforts. This dynamic matching ensures that resources are allocated efficiently, focusing on mitigating the most imminent threats to the organization's security. Furthermore, the integration of threat intelligence enriches the vulnerability management process by providing contextual information about emerging threats, attack vectors, and adversary tactics. This contextual awareness enables organizations to anticipate and proactively defend against potential cyber attacks, thereby reducing the window of vulnerability and minimizing the impact of security breaches. Through empirical evaluation and case studies, we demonstrate the efficacy of the proposed framework in enhancing cybersecurity posture across diverse organizational environments. By empowering organizations to adaptively respond to evolving cyber threats, this approach enables them to stay ahead of adversaries and effectively safeguard their critical assets and data.
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Hafsat, Bida Abdullahi. "Developing Intelligent Cyber Threat Detection Systems Through Advanced Data Analytics." Developing Intelligent Cyber Threat Detection Systems Through Advanced Data Analytics 9, no. 2 (2024): 10. https://doi.org/10.5281/zenodo.10670055.
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Cyberattacks are evolving, and conventional signature-based detection mechanisms will not succeed at detecting such attacks. Sophisticated detection systems that utilize modern data analytics, such as machine learning and artificial intelligence, can identify hidden patterns or behavioral relationships in the large array of cyber-related residuals. This study suggests cyber threat detection research into a comprehensive artificial intelligence framework. The features should have behavior modeling, intelligent correlation, and dynamic detection models. All these difficulties are the challenges to human research efforts as related to new endeavors with multi- source data sets. They also include three different, most optimized algorithms with chances of being free from such production variants that are biased multi-mode sources. With the constant informing of realistic threats, machine learning models have to produce sturdy representations that can transfer knowledge to identify innovative attacks. Transparency and auditability of a model encourage faith in automated decisions. Continual training against adversarial samples and concept drift makes them resilient. End-to-end, multi-layered cyber defense benefits from a variety of sources, including integrated analytics leveraging the full spectrum visibility through orchestration across the network, user, and malware data. The alternative learning paradigms of self-supervision and reinforcement learning provide hope to topics such as high-valued threat intelligence. Finally, human-machine integration, which takes advantage of strengths based on complementary aptitudes, shall chart the next course. Analyst cognition-enhancing algorithms decrease operational workloads. The scope of this study is to promote cyber protection with A.I. evolving beyond traditional limitations. Keywords:- The Areas of Cyber Security, Threat Detection, Anomaly Detection, Machine Learning) Artificial Intelligence Methods Data Analysis.
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Siva, Krishna Jampani. "AI-Driven Threat Intelligence: Revolutionizing Proactive Cyber Defense." Journal of Scientific and Engineering Research 8, no. 6 (2021): 220–27. https://doi.org/10.5281/zenodo.14637382.
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Artificial Intelligence is revolutionizing cybersecurity, changing defensive strategies from reactive to proactive. This article has focused on AI-driven threat intelligence as the transformative force, detailing machine learning models that enable real-time data analysis, pattern recognition, and predictive analytics. AI's ability to detect anomalies and predict cyber threats before they materialize has surpassed traditional reactive measures. This proactive approach significantly enhances organizational resilience against constantly evolving threats. Innovations in anomaly detection and predictive modeling offer robust protection that can adapt. The discussion underscores the cross-industry impact of these advancements and the critical need for further development in cybersecurity solutions using AI, encouraging the audience to contribute to this field.
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Sharma, Sujeet. "AI-Powered Cybersecurity: The Future of Threat Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45943.
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Abstract - This paper explores the transformative role of Artificial Intelligence (AI) in cybersecurity, focusing on its ability to enhance threat detection, automate responses, and mitigate evolving cyber threats. Traditional cybersecurity measures, reliant on predefined rules and signature-based detection, struggle to combat advanced threats like polymorphic malware and zero-day exploits. AI-powered solutions leverage machine learning, deep learning, and behavioral analytics to proactively identify and neutralize threats in real time. Key advantages include reduced human error, scalability, and predictive threat intelligence. However, challenges such as adversarial AI attacks, data privacy concerns, and ethical biases must be addressed. The study highlights real-world applications, emerging trends like Explainable AI (XAI), and the integration of AI with quantum computing. By analyzing current advancements and future prospects, this research underscores AI’s critical role in shaping the future of cybersecurity. Key Words: artificial Intelligence, Machine Learning, cybersecurity, Threat Detection, Intrusion Detection Systems (IDS), Anomaly Detection, Network Security,
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Desai, Aditya R. "A Review on Cybercrime Control through Behavioural Pattern Analysis Using a Comprehensive Database and Enhanced APIS." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3304–9. https://doi.org/10.22214/ijraset.2025.68894.
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Spam links have become a prevalent cybersecurity concern, leading to cyber threats such as phishing attacks, malware infections, ransomware propagation, identity theft, and financial fraud. Traditional detection methods, such as static blacklists and rule-based approaches, struggle to keep up with the rapid evolution of cyber threats[1]. This paper presents a comprehensive approach to spam link detection, integrating multiple threat intelligence sources such as Google Safe Browsing API, OpenPhish, PhishTank, and URLhaus, along with an intelligent behavioral pattern analysis module[3]. The proposed system leverages a dynamic threat intelligence database, which improves real-time detection accuracy, reduces false positives, and enhances the adaptability of spam link detection mechanisms[2]. Our study highlights the effectiveness of combining multiple APIs, behavioral analytics, and historical threat data in providing a robust detection framework. Additionally, this paper explores real-world applications of spam link detection in cybersecurity, including web security, enterpriselevel monitoring, and AI-driven automated threat detection. The role of machine learning and artificial intelligence in identifying spam links is also discussed, with a focus on enhancing automated security protocols. This review serves as a foundation for future research in AI-powered spam link identification and automated cybersecurity threat intelligence
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Magnus Chukwuebuka Ahuchogu. "The Role of Cyber Threat Intelligence in Protecting National Infrastructure." Power System Technology 49, no. 1 (2025): 1548–69. https://doi.org/10.52783/pst.1699.
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Cyber threats pose a significant risk to national infrastructure, with critical sectors such as energy, transportation, healthcare, and finance increasingly targeted by sophisticated cyberattacks. Cyber Threat Intelligence (CTI) plays a crucial role in enhancing cybersecurity defenses by providing actionable insights into emerging threats, adversarial tactics, and vulnerabilities. This paper explores the role of CTI in protecting national infrastructure, emphasizing its contribution to threat detection, risk mitigation, and incident response. We examine the integration of artificial intelligence and big data analytics in CTI to improve threat prediction and real-time analysis. Additionally, we discuss the challenges in implementing CTI, including information sharing barriers, data privacy concerns, and the evolving nature of cyber threats. By analyzing case studies of cyber incidents and successful CTI implementations, this research highlights best practices for strengthening national cybersecurity frameworks. The findings underscore the necessity of a proactive and intelligence-driven approach to safeguard critical infrastructure against ever-evolving cyber risks. DOI:https://doi.org/10.52783/pst.1699
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Bhavik Patel, Patel Krunalkumar Bhagavanbhai, and Niravkumar Dhameliya. "Revolutionizing Cybersecurity with AI: Predictive Threat Intelligence and Automated Response Systems." Darpan International Research Analysis 12, no. 4 (2024): 1–5. http://dx.doi.org/10.36676/dira.v12.i4.126.
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The sophistication and breadth of cyber threats are continuously expanding, making it more difficult for traditional security measures to keep up. Artificial intelligence is revolutionizing cybersecurity by equipping businesses to proactively counter threats with automated reaction systems and predictive threat intelligence. Data analytics, behavioral analysis, and machine learning enable AI-powered systems to anticipate cyber assaults, enabling more efficient and rapid threat detection. By automating reaction mechanisms and mitigating threats in real-time, AI systems can minimize human error and maximize damage mitigation. AI techniques, such as anomaly detection, predictive modeling, and real-time threat analysis; data privacy, ethics, and the risks of hostile attacks are among the subjects covered, as are the benefits and drawbacks of utilizing AI in cybersecurity. This article provides the framework for future intelligent, automated cyber defense methods and illustrates how AI may alter cybersecurity using real-life examples and case studies.
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RAJESH RAJAMOHANAN NAIR. "Evaluating the Effectiveness of AI-Driven Threat Intelligence Systems: A Technical Analysis." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 514–24. https://doi.org/10.32996/jcsts.2025.7.3.58.
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This technical article examines the growing implementation of artificial intelligence in cybersecurity operations, specifically focusing on threat intelligence platforms. Through empirical analysis and industry data, It demonstrates that organizations deploying AI-driven threat intelligence solutions experience significantly improved detection and response metrics compared to traditional Security Operations Center (SOC) models. It validates that AI integration leads to faster threat detection, more accurate classification, and reduced mean time to repair across various security incidents. The article explores the technical underpinnings of these systems, including machine learning models, behavioral analytics, and automated response frameworks, while also addressing implementation challenges and best practices. The article findings provide compelling evidence that AI-driven approaches represent not merely an enhancement to existing security operations but a fundamental transformation in how organizations detect, analyze, and respond to sophisticated cybersecurity threats. It concludes by examining emerging technologies such as federated learning, explainable AI, adversarial learning, and autonomous response capabilities that will shape the future evolution of AI-driven threat intelligence.
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Maosa, Herbert, Karim Ouazzane, and Viktor Sowinski-Mydlarz. "Real-Time Cyber Analytics Data Collection Framework." International Journal of Information Security and Privacy 16, no. 1 (2022): 1–10. http://dx.doi.org/10.4018/ijisp.311465.
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In cyber security, it is critical that event data is collected in as near real time as possible to enable early detection and response to threats. Performing analytics from event logs stored in databases slows down the response time due to the time cost of database insertion and retrieval operations. The authors present a data collection framework that minimizes the need for long-term storage. Events are buffered in memory, up to a configurable threshold, before being streamed in real time using live streaming technologies. The framework deploys virtualized data collecting agents that ingest data from multiple sources including threat intelligence. The framework enables the correlation of events from various sources, improving detection precision. The authors have tested the framework in a real time, machine-learning-based threat detection system. The results show a time gain of 300 milliseconds in transmission time from event capture to analytics system, compared with storage-based collection frameworks. Threat detection was measured at 95%, which is comparable to the benchmark snort IDS.
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Bukowski, Michał. "Combating economic cybercrime using artificial intelligence (AI)." PRZEGLĄD POLICYJNY 151, no. 3 (2023): 339–65. http://dx.doi.org/10.5604/01.3001.0053.9746.
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Combating economic cybercrime using AI can be a new powerful approach. AI technologies have the potential to detect and respond to cyber threats in real-time, identify patterns and anomalies in large data sets, and automate various security processes. The basic ways of using artificial intelligence to combat economic cybercrime are Threat Detection, Behavioral Analysis, Fraud Prevention, Phishing and Malware Detection, Vulnerability Management, Incident Response and Threat Hunting, Predictive Analytics or Security Automation. However, it should be noted that while AI can significantly improve cybersecurity operations, it is not a standalone solution. It should be used in conjunction with other security measures such as regular software updates, employee training, and strong access controls to create a robust defense against economic cybercrime
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Hasan, Kamrul, Forhad Hossain, Al Amin, Yadab Sutradhar, Israt Jahan Jeny, and Shakik Mahmud. "Enhancing Proactive Cyber Defense: A Theoretical Framework for AI-Driven Predictive Cyber Threat Intelligence." Journal of Technologies Information and Communication 5, no. 1 (2025): 33122. https://doi.org/10.55267/rtic/16176.
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The rapid evolution of cyber threats and the dynamic nature of the threat landscape have necessitated the development of proactive and predictive defense mechanisms. This research proposes an AI-driven framework for predictive cyber threat intelligence aimed at enhancing organizational cybersecurity by identifying and mitigating threats before they materialize. The framework integrates diverse data sources, including network logs, endpoint data, and threat intelligence feeds, to generate actionable insights using advanced machine learning algorithms such as anomaly detection, pattern recognition, and predictive analytics. A continuous feedback loop ensures the adaptability of the framework through model retraining, anomaly adjustment, and performance monitoring. By leveraging supervised and unsupervised learning models, the framework addresses both known and unknown threats, providing scalable, real-time threat detection and risk assessment capabilities. This approach shifts the cybersecurity paradigm from reactive to proactive, enabling organizations to anticipate and counteract sophisticated cyber-attacks effectively. The proposed system’s application is demonstrated through practical scenarios, highlighting its potential to transform decision-making in high-stakes cybersecurity environments.
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Qamar, Sara, Zahid Anwar, Mohammad Ashiqur Rahman, Ehab Al-Shaer, and Bei-Tseng Chu. "Data-driven analytics for cyber-threat intelligence and information sharing." Computers & Security 67 (June 2017): 35–58. http://dx.doi.org/10.1016/j.cose.2017.02.005.
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Researcher. "THE ROLE OF AI IN DETECTING INSIDER THREATS IN HEALTHCARE ORGANIZATIONS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 239–48. https://doi.org/10.5281/zenodo.13960643.
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This article explores the critical role of Artificial Intelligence (AI) in detecting and preventing insider threats within healthcare organizations. It examines the growing challenges of data security in the healthcare sector, highlighting the significant financial and reputational risks posed by insider threats. The article discusses various types of insider threats, including malicious actors, negligent employees, and compromised credentials. It then delves into how AI enhances insider threat detection through behavioral analytics, machine learning, natural language processing, and predictive analytics. The article outlines key steps for implementing AI-based insider threat detection systems and addresses the ethical considerations and privacy concerns associated with such implementations. By leveraging AI technologies, healthcare organizations can significantly improve their ability to protect sensitive patient data and maintain the integrity of their systems.
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Dhanyamraju, Sri Anusha, M. Ananya, and Dechamma M.P Drishya. "Applications of Artificial Intelligence in Computer Engineering." Journal of Advanced Research in Artificial Intelligence & It's Applications 2, no. 2 (2025): 1–6. https://doi.org/10.5281/zenodo.14890815.
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<em>Artificial Intelligence is a revolutionary asset in cybersecurity; thus, it provides enhanced ability toward the identification, thwarting, and addressing the changed nature of cyber threats. This, in turn, increases more advanced attack techniques and data explosion growth, making conventional security systems frequently unable to protect further. AI boosts up cyber security by providing fast, immediate threat identification using its capabilities of machine learning algorithms and anomaly detection and predictive analytics. This paper discusses the integration of AI in various cybersecurity domains, including intrusion detection, malware analysis, and endpoint security. It also discusses the challenges associated with the deployment of AI-based solutions, including issues related to data privacy, adversarial threats, and the need for qualified experts. Tackling these issues can greatly enhance security protocols and reduce risks in a constantly evolving threat environment. The research concludes with a discussion on potential paths and innovation required to improve the role AI plays in protecting digital systems.</em>
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Jawaid, Syed Adnan. "Artificial Intelligence with Respect to Cyber Security." Journal of Advances in Artificial Intelligence 1, no. 2 (2023): 96–102. http://dx.doi.org/10.18178/jaai.2023.1.2.96-102.
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Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats. Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy. AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.
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Researcher. "AI IN CYBERSECURITY: ADVANCEMENTS AND CHALLENGES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 992–1001. https://doi.org/10.5281/zenodo.13999861.
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This article explores the transformative role of artificial intelligence (AI) in cybersecurity, highlighting its potential to revolutionize threat detection and mitigation in an increasingly sophisticated cyber threats era. It examines the rapid growth of AI adoption in the cybersecurity sector, driven by the rising costs of data breaches and the limitations of traditional security measures. The article delves into advanced AI-powered capabilities such as anomaly detection, malware identification, phishing prevention, behavioral analytics, and threat intelligence integration. While acknowledging AI's promising future in cybersecurity, including predictive threat intelligence and automated incident response, the article also addresses critical challenges such as data bias, explainability issues, adversarial attacks, ethical considerations, and the cybersecurity skills gap. By presenting both the advancements and obstacles in AI-driven cybersecurity, this study provides a comprehensive overview of this rapidly evolving field's current landscape and future trajectory.
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Islam, S. A. Mohaiminul, MD Shadikul Bari, Ankur Sarkar, A. J. M. Obaidur Rahman Khan, and Rakesh Paul. "AI-Powered Threat Intelligence: Revolutionizing Cybersecurity with Proactive Risk Management for Critical Sectors." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 7, no. 01 (2024): 1–8. https://doi.org/10.60087/jaigs.v7i01.291.
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The rapid evolution of cyber threats has necessitated a paradigm shift in cybersecurity strategies, particularly in critical sectors such as healthcare, finance, energy, and transportation. This paper explores the transformative role of AI-powered threat intelligence in revolutionizing cybersecurity practices. By leveraging advanced machine learning algorithms, natural language processing, and predictive analytics, AI-driven systems can detect, analyze, and mitigate threats with unprecedented speed and accuracy. This research highlights the integration of real-time data processing, threat intelligence platforms, and adaptive security frameworks to enable proactive risk management. Case studies and experimental results underscore the effectiveness of AI-powered approaches in anticipating cyberattacks, reducing response times, and minimizing operational disruptions. The findings demonstrate that AI is not merely a tool but a pivotal enabler of robust, adaptive, and scalable cybersecurity strategies in the face of an ever-evolving threat landscape.
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Djenna, Amir, Ezedin Barka, Achouak Benchikh, and Karima Khadir. "Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics." Sensors 23, no. 14 (2023): 6302. http://dx.doi.org/10.3390/s23146302.
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Cybercriminals are becoming increasingly intelligent and aggressive, making them more adept at covering their tracks, and the global epidemic of cybercrime necessitates significant efforts to enhance cybersecurity in a realistic way. The COVID-19 pandemic has accelerated the cybercrime threat landscape. Cybercrime has a significant impact on the gross domestic product (GDP) of every targeted country. It encompasses a broad spectrum of offenses committed online, including hacking; sensitive information theft; phishing; online fraud; modern malware distribution; cyberbullying; cyber espionage; and notably, cyberattacks orchestrated by botnets. This study provides a new collaborative deep learning approach based on unsupervised long short-term memory (LSTM) and supervised convolutional neural network (CNN) models for the early identification and detection of botnet attacks. The proposed work is evaluated using the CTU-13 and IoT-23 datasets. The experimental results demonstrate that the proposed method achieves superior performance, obtaining a very satisfactory success rate (over 98.7%) and a false positive rate of 0.04%. The study facilitates and improves the understanding of cyber threat intelligence, identifies emerging forms of botnet attacks, and enhances forensic investigation procedures.
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Ayyarrappan, Mariappan. "AI-driven Security Enhancements for Web Applications." International Scientific Journal of Engineering and Management 03, no. 08 (2024): 1–3. https://doi.org/10.55041/isjem01992.
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As the sophistication of cyber threats escalates, traditional security measures—firewalls, basic intrusion detection systems, and static rule checks— often struggle to keep pace. Recent advancements in artificial intelligence (AI) provide novel opportunities to fortify web application security. This paper discusses how AI-driven methods, such as machine learning–based anomaly detection, natural language processing (NLP) for threat intelligence, and predictive analytics, can enhance protection against a broad range of attacks (e.g., SQL injection, Cross-Site Scripting). We include diagrams and charts to illustrate conceptual models of AI-based security flows, highlight best practices for data ingestion and feature engineering, and address challenges like false positives and model drift. By adopting AI-driven security enhancements, organizations can proactively respond to evolving threats, reducing exposure and fortifying their web applications. Keywords AI Security, Web Applications, Intrusion Detection, Machine Learning, Threat Intelligence, Cyber Attacks
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Yadav, Dr Neha. "AI in Cybersecurity: A Literature review." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03378.
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Abstract - In cybersecurity, artificial intelligence is revolutionizing incident response, risk management, and threat identification in a progressively hostile cyber threat landscape. This research presents a thorough literature review on AI in cybersecurity, focusing on both aspects of the balance sheet. This paper discusses how AI-driven technologies like machine learning, deep learning, natural language processing, and expert systems improve security frameworks through predictive analytics, real-time threat intelligence, and anomaly detection. The research explores various uses of AI such as network protection, cloud safety, healthcare, and finance, highlighting how AI-driven solutions enhance the resilience of cybersecurity against attacks. Nonetheless, there are drawbacks too, primarily associated with algorithmic prejudices and aggressive attacks. This paper discusses AI cybersecurity tools like Cylance, Darktrace, and IBM Watson, analyzing their influences and effects on security operations. The study also explores recent advancements and enhancements in AI-driven cybersecurity, ethical concerns, and regulatory structures. To establish a safe digital space, this document highlights the importance of a unified strategy that integrates AI with human expertise, ethics, and regulatory adherence. Key Words: Artificial Intelligence, Machine Learning, Deep Learning, Expert Systems, Natural Language Processing, Threat Detection, AI-driven Threat Intelligence, Dynamic Threats, Cyber Attack, Cyber Security.
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Swapnil Chawande. "The role of Artificial Intelligence in cybersecurity." World Journal of Advanced Engineering Technology and Sciences 11, no. 2 (2024): 683–96. https://doi.org/10.30574/wjaets.2024.11.2.0014.
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AI implementation in cybersecurity frameworks became necessary due to rising cyber threat complexity and frequency that made standard security measures insufficient. This research analyzes how Artificial Intelligence helps security practitioners identify cyber threats at higher velocities and with greater accuracy than standard procedures. The paper studies how machine learning with natural language processing and behavioral analytics transforms cybersecurity systems by detecting irregularities while performing automatic threat defense operations. Through their qualitative research design and three specific real-world examples, including Darktrace, IBM Watson, and Cylance PROTECT, this article validates the extensive positive impact of AI tools on threat intelligence and incident response times within various industries. AI demonstrates its effectiveness through two key outcomes: real-time attack detection, preemptive containment, human analysis workload reduction, and attack response acceleration. Digital infrastructure security finds a future-oriented solution through AI even though limitations related to bias and explainability exist. This research highlights the urgent need to create new AI systems together with moral principles that will establish strong cybersecurity networks.
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Yilmaz, Erhan, and Ozgu Can. "Unveiling Shadows: Harnessing Artificial Intelligence for Insider Threat Detection." Engineering, Technology & Applied Science Research 14, no. 2 (2024): 13341–46. http://dx.doi.org/10.48084/etasr.6911.
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Insider threats pose a significant risk to organizations, necessitating robust detection mechanisms to safeguard against potential damage. Traditional methods struggle to detect insider threats operating within authorized access. Therefore, the use of Artificial Intelligence (AI) techniques is essential. This study aimed to provide valuable insights for insider threat research by synthesizing advanced AI methodologies that offer promising avenues to enhance organizational cybersecurity defenses. For this purpose, this paper explores the intersection of AI and insider threat detection by acknowledging organizations' challenges in identifying and preventing malicious activities by insiders. In this context, the limitations of traditional methods are recognized, and AI techniques, including user behavior analytics, Natural Language Processing (NLP), Large Language Models (LLMs), and Graph-based approaches, are investigated as potential solutions to provide more effective detection mechanisms. For this purpose, this paper addresses challenges such as the scarcity of insider threat datasets, privacy concerns, and the evolving nature of employee behavior. This study contributes to the field by investigating the feasibility of AI techniques to detect insider threats and presents feasible approaches to strengthening organizational cybersecurity defenses against them. In addition, the paper outlines future research directions in the field by focusing on the importance of multimodal data analysis, human-centric approaches, privacy-preserving techniques, and explainable AI.
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Ravindar, Reddy Gopireddy, and Malik Bushra. "Artificial Intelligence, Security, and Business Analytics: A Convergence for Cyber Defense and Risk Mitigation." European Journal of Advances in Engineering and Technology 8, no. 12 (2021): 18–22. https://doi.org/10.5281/zenodo.15075069.
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The growing complexity of cyber threats in financial and governmental sectors has necessitated the integration of Artificial Intelligence (AI) with cybersecurity and business analytics. AI-driven security solutions enhance threat detection, automate responses, and provide predictive insights that enable proactive risk management. Business analytics, powered by AI, is revolutionizing cybersecurity frameworks by offering data-driven decision-making models that enhance security postures. This research explores the role of AI in cybersecurity, its applications in fraud detection, risk assessment, and regulatory compliance, and its impact on financial institutions and government agencies. Through real-world case studies and emerging trends, this paper provides insights into how AI and business analytics are converging to create robust cyber defense mechanisms.
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Chioma Susan Nwaimo, Adetumi Adewumi, and Daniel Ajiga. "Advanced data analytics and business intelligence: Building resilience in risk management." International Journal of Science and Research Archive 6, no. 2 (2022): 336–44. https://doi.org/10.30574/ijsra.2022.6.2.0121.
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This paper explores the transformative role of advanced data analytics, business intelligence (BI), and artificial intelligence (AI) in enhancing risk management strategies for organizations navigating digital transformation and cybersecurity challenges. It examines how predictive analytics enables the early identification and mitigation of risks, empowering businesses to adopt proactive measures. The integration of BI tools is highlighted for their ability to support strategic decision-making under uncertainty through data visualization, scenario planning, and real-time insights. Additionally, the paper underscores the revolutionary impact of AI in cybersecurity frameworks, including automated anomaly detection and rapid response to emerging threats. Future trends such as explainable AI and AI-driven threat intelligence are discussed, emphasizing their potential to reshape risk management practices. The paper concludes with practical recommendations for organizations aiming to build resilience by adopting these technologies and fostering a data-driven culture.
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Journal, of Global Research in Mathematical Archives(JGRMA). "Enhancing Threat Intelligence and Cyber Defense through Big Data Analytics: A Review Study." Journal of Global Research in Mathematical Archives (JGRMA) 12, no. 04 (2025): 01–06. https://doi.org/10.5281/zenodo.15223174.
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In a number of industries, including cybersecurity, healthcare, banking, and power distribution, big data analysis has emerged as a game-changing technique. The proliferation of massive and heterogeneous information gathered from platforms, including social media, IoT devices, electronic conducted online, and network logs, necessitates advanced analytical techniques and robust technologies for effective processing and insight generation. This research investigates how statistical analysis of big data may be integrated into cybersecurity, emphasizing its role in anomaly detection, behavioral analysis, threat intelligence integration, and event correlation to enhance threat detection, response, and prediction. Despite its transformative potential, issues including security, confidentiality, effectiveness, and knowledge storage remain significant barriers to its adoption. In order to overcome these obstacles and progress in the sector, emerging technologies like blockchain integration, sophisticated data visualization, and IoT convergence provide encouraging answers. By leveraging these innovations, organizations can improve their ability to anticipate, mitigate, and respond to sophisticated cyber threats, ensuring robust protection for sensitive data and systems.
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Eniola, Akinola Odedina. "INTEGRATING AI-DRIVEN THREAT INTELLIGENCE INTO HEALTHCARE CYBER RISK ASSESSMENTS." International Journal of Engineering Technology Research & Management (IJETRM) 06, no. 08 (2022): 84–94. https://doi.org/10.5281/zenodo.15343248.
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Healthcare systems experience a dramatic expansion of cyber threats because of their widespread digitaltransformation. Healthcare organizations need dynamic risk assessments for their protected health information (PHI),medical devices and IT infrastructure because these become consistent targets for cyberattacks. This paper studieshow artificial intelligence (AI)-driven threat intelligence can enhance healthcare cyber risk assessments by providingan anticipatory solution to dynamic threat evolution. Current Artificial Intelligence technologies perform real-timelarge data analysis to detect anomalies and forecast security threats with superior speed and accuracy than conventionaltools using machine learning and natural language processing capabilities.The article introduces cyber security threats present in healthcare by discussing ransomware, phishing attacks andunauthorized internal access. A review of current risk evaluation methods proves incompatible with quick changes inthe digital security space. Behavioural analytics, predictive modelling, and automated detection systems form theessential part of this paper's analysis of how AI strengthens threat intelligence. The research analyses the practicaldifficulties and the ethical aspects that stem from integrating AI through the evaluation of data privacy limitations,algorithm bias, and the requirement for specialists from multiple disciplines. Examples from industry practice arepresented to show how organizations effectively use these methods while providing their achieved metrics. This paperdelivers strategic guidance to healthcare organizations which want to include AI threat intelligence systems in theircybersecurity infrastructure. The healthcare sector is experiencing a fundamental transformation of cyber riskmanagement because AI combines with other systems. Thus, this combination represents more than a technologicaladvancement.
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Kumari, Neelam, and Ashok Kumar. "Advanced Computational Techniques for Analyzing Cybersecurity Event Datasets Using Artificial Intelligence and Machine Learning." SCT Proceedings in Interdisciplinary Insights and Innovations 3 (January 1, 2025): 524. https://doi.org/10.56294/piii2025524.
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Introduction: The complexity and range of cyber threats continue to grow, presenting challenges that traditional security approaches struggle to address. Objective: Artificial intelligence is transforming cybersecurity by empowering organizations to proactively combat threats through automated response mechanisms and predictive threat analysis. Leveraging data analytics, behavioral insights, and machine learning, AI-driven systems can forecast cyberattacks, enabling faster and more accurate threat detection. Method: By automating responses and addressing threats in real time, these systems reduce the risk of human error and enhance damage control. Result: Key AI techniques, including anomaly detection, predictive modeling, and real-time threat evaluation, are explored alongside considerations of data privacy, ethical concerns, and the potential dangers of adversarial attacks. The advantages and limitations of applying AI in cybersecurity are examined as well. Conclusion: This article provides a foundation for the future of intelligent and automated cyber defense strategies, showcasing how AI can reshape the cybersecurity landscape through practical examples and real-world case studies.
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Adeola N. Raji, Abiola O. Olawore, Adeyinka Ayodeji, and Jennifer Joseph. "Integrating Artificial Intelligence, machine learning, and data analytics in cybersecurity: A holistic approach to advanced threat detection and response." World Journal of Advanced Research and Reviews 20, no. 3 (2023): 2005–24. http://dx.doi.org/10.30574/wjarr.2023.20.3.2741.
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Introduction: The integration of artificial intelligence (AI), machine learning (ML), and data analytics is revolutionizing cybersecurity practices. With the advancement in technology and new threats emerging in the cyberspace, conventional approaches to security are not effectively sufficient. This paper aims at identifying how these sophisticated technologies improve the methods of threat identification, response, and the overall analytical capability to strengthen the computerized structures against modern SNEs. The threat is changing at incredible speeds, making it impossible to just wait for new threats to unfold and take a response. AI&ML are capable to analyses enormous quantity of data in extremely short time, as well as find patterns and changing previous unnoticed by analysts, automatically respond to threats in real time. Data analytics forms the bedrock on which the advanced systems are built and serve to process and analyze a large chunk of the security related information. The combination of these technologies provides a strong foundation for the cybersecurity environment that can be responsive to emerging threats, utilize prior attacks for training purposes, and self-develop the methodology for better protection. Methodology: The study employed a comprehensive search strategy across multiple electronic databases, including IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, and Google Scholar. Keywords related to AI, ML, data analytics, and cybersecurity were used in combination with Boolean operators. To make the outcome more meaningful and relevant, the general criteria for the eligibility of the papers were as follows. The selection process involved two phases: Title and abstract evaluation for the inclusion in the initial set of studies and subsequent full-text review of these studies. Some of our extraction process involved the use of a data extraction form to gather specific details from each of the study included in the analysis. To evaluate the quality of the studies included, the CASP tools were used with slight modifications. In this study, two independent reviewers participated in the decision on the study inclusion, data extraction, and quality assessment to reduce bias. This approach of writing helped in providing a comprehensive and methodical analysis of the contemporary state and potential developments in the context of AI and ML in the realm of cybersecurity. Results and Discussion: The review highlights that AI and ML greatly boost the threat detection by detecting patterns and anomalies within large volumes of security data. These technologies can be used to descend new and previously unknown type of attack known as zero-day attack & APTs (advanced persistent threats). Using AI and ML for predictive analytics enables the organization to leverage previous attacks and contexts to predict future attacks, and prepare for their defense. The use of AI in response to security threats also minimizes response time in times of security threats and optimizes processes. These technologies integrate to help quickly and more with minimal human intervention respond to threats thereby also reducing the time it takes to respond to threats. However, issues like quality of the data used in the model, reliability of the algorithm besides, question marks like who will tamper with the AI systems. The review also discusses new trends in cyber defense and remediation that may be of interest in the future, namely continuous authentication and advanced threat hunting. Potential issues associated with data privacy and algorithmic bigotry are pointed out as promising directions for future studies in this domain. Conclusion: The integration of AI, ML, and data analytics in cybersecurity represents a paradigm shift in how organizations approach digital defense. These technologies provide relevant functions for increasing threat diagnostics and response capabilities, as well as improving the predictive feature offered by this automation. The integration of AI, ML along with data analytics results into an architecture that is strong, flexible, intelligent and adaptive enough to cope up with growing security threats. Despite all these issues, including the problems with data quality and reliability of algorithms, as well as the numerous ethical questions, employing these technologies in cybersecurity seems promising. New types of cyber threats constantly emerge and therefore ongoing enhancement of AI and ML security tools will be imperative. The long-term research should endeavor to address the challenges mentioned above as well as elaborate on additional possible uses of these technologies in strengthening cybersecurity
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Adeola N. Raji, Abiola O. Olawore, Adeyinka Ayodeji, and Jennifer Joseph. "Integrating Artificial Intelligence, machine learning, and data analytics in cybersecurity: A holistic approach to advanced threat detection and response." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 091–110. http://dx.doi.org/10.30574/wjarr.2024.24.2.3197.
Full textAbstract:
Introduction: The integration of artificial intelligence (AI), machine learning (ML), and data analytics is revolutionizing cybersecurity practices. With the advancement in technology and new threats emerging in the cyberspace, conventional approaches to security are not effectively sufficient. This paper aims at identifying how these sophisticated technologies improve the methods of threat identification, response, and the overall analytical capability to strengthen the computerized structures against modern SNEs. The threat is changing at incredible speeds, making it impossible to just wait for new threats to unfold and take a response. AI&ML are capable to analyses enormous quantity of data in extremely short time, as well as find patterns and changing previous unnoticed by analysts, automatically respond to threats in real time. Data analytics forms the bedrock on which the advanced systems are built and serve to process and analyze a large chunk of the security related information. The combination of these technologies provides a strong foundation for the cybersecurity environment that can be responsive to emerging threats, utilize prior attacks for training purposes, and self-develop the methodology for better protection. Methodology: The study employed a comprehensive search strategy across multiple electronic databases, including IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, and Google Scholar. Keywords related to AI, ML, data analytics, and cybersecurity were used in combination with Boolean operators. To make the outcome more meaningful and relevant, the general criteria for the eligibility of the papers were as follows. The selection process involved two phases: Title and abstract evaluation for the inclusion in the initial set of studies and subsequent full-text review of these studies. Some of our extraction process involved the use of a data extraction form to gather specific details from each of the study included in the analysis. To evaluate the quality of the studies included, the CASP tools were used with slight modifications. In this study, two independent reviewers participated in the decision on the study inclusion, data extraction, and quality assessment to reduce bias. This approach of writing helped in providing a comprehensive and methodical analysis of the contemporary state and potential developments in the context of AI and ML in the realm of cybersecurity. Results and Discussion: The review highlights that AI and ML greatly boost the threat detection by detecting patterns and anomalies within large volumes of security data. These technologies can be used to descend new and previously unknown type of attack known as zero-day attack & APTs (advanced persistent threats). Using AI and ML for predictive analytics enables the organization to leverage previous attacks and contexts to predict future attacks, and prepare for their defense. The use of AI in response to security threats also minimizes response time in times of security threats and optimizes processes. These technologies integrate to help quickly and more with minimal human intervention respond to threats thereby also reducing the time it takes to respond to threats. However, issues like quality of the data used in the model, reliability of the algorithm besides, question marks like who will tamper with the AI systems. The review also discusses new trends in cyber defense and remediation that may be of interest in the future, namely continuous authentication and advanced threat hunting. Potential issues associated with data privacy and algorithmic bigotry are pointed out as promising directions for future studies in this domain. Conclusion: The integration of AI, ML, and data analytics in cybersecurity represents a paradigm shift in how organizations approach digital defense. These technologies provide relevant functions for increasing threat diagnostics and response capabilities, as well as improving the predictive feature offered by this automation. The integration of AI, ML along with data analytics results into an architecture that is strong, flexible, intelligent and adaptive enough to cope up with growing security threats. Despite all these issues, including the problems with data quality and reliability of algorithms, as well as the numerous ethical questions, employing these technologies in cybersecurity seems promising. New types of cyber threats constantly emerge and therefore ongoing enhancement of AI and ML security tools will be imperative. The long-term research should endeavor to address the challenges mentioned above as well as elaborate on additional possible uses of these technologies in strengthening cybersecurity