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Journal articles on the topic 'Automated Threat Detection'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Shah, Anki, J. Kathyayani, D. Janani, E. Abhinav, and Rajashree Sutrawe. "CYBER THREAT DETECTION AND PROFILING USING AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–6. https://doi.org/10.55041/ijsrem.ncft025.

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The increasing reliance on the internet has escalated the frequency and sophistication of cyber threats, making timely identification and mitigation essential. This research presents an AI-powered framework for cyber threat detection and profiling using Natural Language Processing (NLP) and Machine Learning (ML) techniques. By utilizing Twitter as an Open Source Intelligence (OSINT) platform, the system collects real-time threat intelligence, classifies threats, and maps them to the MITRE ATT&CK framework to provide actionable insights. Key processes include data preprocessing, feature ext

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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 syst

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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 prop

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Franciskumar, Gavit, and Mr Satish Kumar. "CipherScan: An Advanced Web Application Vulnerability Scanner for Enhanced Cybersecurity." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43804.

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The rapid development of cyber threats such as zero-day attacks and ransomware attacks has placed increasing pressure on proactive security practices. Traditional security products such as firewalls and antivirus software are not sufficient for efficient detection and neutralization of modern attacks. Intelligent, automated, and scalable security solutions need to be deployed by organizations to secure sensitive data and IT infrastructure. CipherSite has been engineered to integrate vulnerability scanning with real-time dynamic threat detection through artificial intelligence-powered analytics

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Ramasankar Molleti, Vinod Goje, Puneet Luthra, and Prathap Raghavan. "Automated threat detection and response using LLM agents." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 079–90. http://dx.doi.org/10.30574/wjarr.2024.24.2.3329.

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The increase of cyber threats from individual cases to a worldwide problem is the reason why people have shifted their cybersecurity perspectives. Basic defense processes, originally well understood and effective, fail to match modern attacks’ complexity and velocity. Taking into consideration LLMs as a recent addition to AI, this paper aims at discussing their application in integrating threat detection and response automation systems. As a result, LLMs, which have higher capabilities for natural language processing, deliver a revolutionary perspective regarding cybersecurity. Since LLM agent

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Ramasankar, Molleti, Goje Vinod, Luthra Puneet, and Raghavan Prathap. "Automated threat detection and response using LLM agents." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 079–90. https://doi.org/10.5281/zenodo.15067889.

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The increase of cyber threats from individual cases to a worldwide problem is the reason why people have shifted their cybersecurity perspectives. Basic defense processes, originally well understood and effective, fail to match modern attacks’ complexity and velocity. Taking into consideration LLMs as a recent addition to AI, this paper aims at discussing their application in integrating threat detection and response automation systems. As a result, LLMs, which have higher capabilities for natural language processing, deliver a revolutionary perspective regarding cybersecurity. Since LLM

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Mamayson, Emmanuel B., Mark Cherwin L. Alejandria, and Jose Gil K. Escalante. "Kalasag: An Integrated and Advanced Cybersecurity Tools for Cyber Threats Protection." International Journal of Research and Innovation in Applied Science X, no. VI (2025): 826–29. https://doi.org/10.51584/ijrias.2025.10060061.

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This paper presents Kalasag, an integrated and advanced cybersecurity tools for cyber threats protection developed to enhance threat detection and accelerate incident response within enterprise environments. Traditional security infrastructures often consist of siloed tools, resulting in operational inefficiencies and delayed threat mitigation. Kalasag unifies multiple cybersecurity functions— including network-based intrusion detection, host monitoring, threat intelligence, and automated incident response—within a single, coherent system. By leveraging real-time data correlation, anomaly dete

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P. Nisha, Mrs M. "Dark web Guardian: Real time threat Detection and Analysis." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03502.

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Abstract - The dark web represents a significant security threat due to its anonymity and the prevalence of illegal activities, including cybercrime, data breaches, and the sale of illicit goods. In response, real-time threat detection and analysis have become critical components of cybersecurity strategies. This paper introduces "Dark Web Guardian," a system designed to monitor and identify threats in real-time by analyzing dark web activities. The study focuses on the integration of advanced threat detection techniques, such as machine learning algorithms, behavioural analysis, and automated

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Emmanuel Joshua, John Do, and Rushil Patel. "AI-Driven Threat Intelligence System (AIDTIS): Leveraging large language models for automated threat research and detection development." International Journal of Science and Research Archive 14, no. 3 (2025): 270–85. https://doi.org/10.30574/ijsra.2025.14.3.0339.

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Cyber threats are evolving at an unprecedented pace, challenging organizations to stay ahead of sophisticated adversaries. Traditional threat research methods often require extensive manual effort, leading to delays in identifying and mitigating threats. This paper proposes an AI-Driven Threat Intelligence System (AIDTIS), a theoretical approach that leverages large language models (LLMs) to automate and enhance threat research and detection development. Our simulations and theoretical models suggest that such a system could significantly reduce threat research time, improve detection accuracy

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Ramaiah, CH, D. Adithya Charan, and R. Syam Akhil. "Secure automated threat detection and prevention (SATDP)." International Journal of Engineering & Technology 7, no. 2.20 (2018): 86. http://dx.doi.org/10.14419/ijet.v7i2.20.11760.

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Secure automated threat detection and prevention is the more effective procedure to reduce the workload of analyst by scanning the network, server functions& then informs the analyst if any suspicious activity is detected in the network. It monitors the system continuously and responds according to the threat environment. This response action varies from phase to phase. Here suspicious activities are detected by the help of an artificial intelligence which acts as a virtual analyst concurrently with network intrusion detection system to defend from the threat environment and taking appropr

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Yerabolu, Malleswar Reddy. "The Evolution of AI-Driven Threat Hunting: A Technical Deep Dive into Modern Cybersecurity." European Journal of Computer Science and Information Technology 13, no. 7 (2025): 36–49. https://doi.org/10.37745/ejcsit.2013/vol13n73649.

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The integration of artificial intelligence and machine learning in threat hunting represents a transformative evolution in cybersecurity defense strategies. As traditional signature-based detection methods prove inadequate against sophisticated cyber threats, AI-driven systems offer advanced capabilities in real-time threat detection, analysis, and response. The article delves into the technical foundations of AI-based threat hunting systems, exploring their multi-layered architecture, data processing mechanisms, and advanced detection capabilities. From zero-day attack detection to advanced p

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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. B

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Chitoor Venkat Rao Ajay Kumar, Shanti Lekhana Yakkaladevi, Samagna Pandiri, and Yeshwanth Godugu. "Reinforcement learning-based phishing detection model." World Journal of Advanced Research and Reviews 25, no. 1 (2025): 2291–95. https://doi.org/10.30574/wjarr.2025.25.1.0256.

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Phishing attacks are a persistent cybersecurity threat, exploiting human vulnerabilities via deceptive emails and malicious URLs. This project introduces a novel Reinforcement Learning (RL)-based system to automate phishing detection and response. By employing advanced RL algorithms, such as Deep Q-Learning and Policy Gradient methods, the system dynamically learns to identify phishing indicators within email content and URLs through Natural Language Processing (NLP) and feature extraction techniques. The RL agent continuously adapts its detection strategies based on evolving threats and user

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Nair, Rajesh Rajamohanan. "Proactive Threat Hunting: The Vanguard of Modern Cybersecurity Defense." European Journal of Computer Science and Information Technology 13, no. 22 (2025): 53–67. https://doi.org/10.37745/ejcsit.2013/vol13n225367.

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Proactive threat hunting represents a paradigm shift in cybersecurity defense strategies, moving organizations beyond traditional reactive approaches to a more aggressive posture against advanced persistent threats. This article examines how structured threat hunting methodologies enable security teams to identify sophisticated adversaries before significant damage occurs. By implementing a comprehensive threat hunting program with appropriate technical infrastructure, specialized personnel, and formalized processes, organizations can substantially reduce attacker dwell time and mitigate breac

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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 intellige

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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

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Kailash Dhakal, Mohammad Mosiur Rahman, Mashfiquer Rahman, Khairul Anam, Mostafizur Rahman, and Ramesh Poudel. "How machine learning is transforming cyber threat detection." World Journal of Advanced Engineering Technology and Sciences 13, no. 2 (2024): 963–73. https://doi.org/10.30574/wjaets.2024.13.2.0581.

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Using machine learning (ML) has made it faster and more precise to discover cyber security threats. Older methods of detecting threats usually struggle with today’s attack volume and complexity which causes delays and can result in mistakes. ML technology helps security teams notice known and new threats in a much shorter period than manual detection. Adopting supervised and unsupervised model types, they can adapt to any new kinds of attacks, raising the chance of detecting them with fewer errors. This study assesses different ML tools and explores when they show better outcomes than standard

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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 positi

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Researcher. "SECURITY DETECTIONS AS CODE: MODERNIZING THREAT DETECTION THROUGH SOFTWARE ENGINEERING PRINCIPLES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 991–99. https://doi.org/10.5281/zenodo.14281933.

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This article offers an in-depth examination of Security Detections as Code (SDaC), an innovative approach that integrates software engineering principles with security operations to transform threat detection and response. It explores how organizations can leverage code-based security detection rules to enhance detection accuracy, streamline operations, and reduce incident response times. Through a detailed analysis of implementation methodologies, technical frameworks, and organizational impacts, the article highlights the potential of treating security detections as versioned, testable code

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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 fra

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Malipeddi, Anil Kumar, and Sreekanth Pasunuru. "Using AI for Intrusion Detection and Threat Intelligence: Enhancing Enterprise Security in the Digital Age." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/isjem01331.

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The rise of cyber threats has underscored the need for advanced tools that can predict, detect, and respond to security incidents with minimal human intervention. Artificial Intelligence (AI) is now at the forefront of such tools, transforming intrusion detection and threat intelligence systems with its ability to analyze vast amounts of data, learn from patterns, and adapt to emerging threats. This article explores how AI is reshaping intrusion detection systems (IDS) and threat intelligence platforms, examining the methods, advantages, and challenges associated with AI-driven security system

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Bhaskar, Parumanchala, Farooq Sunar Mahammad, K. Ramachari, et al. "Automated Cyber Threat Identification Using Natural Language Processing." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 395–99. https://doi.org/10.47001/irjiet/2025.inspire64.

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This abstract challenge by leveraging Natural Language Processing (NLP) to automate cyber threat identification. The proposed system utilizes advanced NLP techniques to analyse vast amounts of textual data from sources such as cybersecurity reports, social media, forums, and dark web communications. The proliferation of cyberthreats in today's digital world poses serious security and privacy issues. Because malevolent behaviour is dynamic, traditional threat detection techniques usually fall behind. By increasing threat detection's precision, effectiveness, and scalability, the solution seeks

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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 impro

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ML, Shiwani. "Prediction of Threat Detection using." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30759.

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In today's tech-driven world, security concerns in public spaces are escalating. Closed-Circuit Television (CCTV) systems are common but managing their data overload is tough. Enter the "Automated Threat Recognition System," powered by deep learning, particularly YOLOv5. With real-time threat detection, it autonomously identifies aggression and violence swiftly, optimizing security monitoring and saving time and resources. This system is crucial for effectively tackling modern security challenges. Index Terms- Colab, Kaggle, Makesense.AI, Yolov5, OpenCv, numpy

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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 red

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Dubrovina, A. I., and M. H. Alcordi. "Development of methods for neutralizing «Zero-day» threats." Herald of Dagestan State Technical University. Technical Sciences 50, no. 4 (2024): 93–100. http://dx.doi.org/10.21822/2073-6185-2023-50-4-93-100.

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Objective. The purpose of this study is to develop and analyze methods for neutralizing «zero-day» threats in order to increase the level of cybersecurity and protection of information systems. Method. In this article, a behavioral analysis of the threat is used. The characteristic features of the zero-day exploit behavior have been studied. The threat model is based on solving the tasks of timely detection and neutralization of the threat. Result. The actual problem of information systems security - the threat of «zero-day» is considered. The review of existing neutralization methods and disc

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Rosh Perumpully Ramadass. "Kubernetes runtime security framework: Integrated detection and automated remediation workflow." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1766–73. https://doi.org/10.30574/wjaets.2025.15.3.1091.

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This article presents a comprehensive framework for implementing runtime security and automated remediation in Kubernetes environments. It addresses the growing security challenges faced by organizations adopting containerized architectures by examining Falco's capabilities for real-time threat detection through system call analysis and rule-based anomaly detection. The integration between Falco and Argo's event-driven automation tools creates a proactive security alert and remediation system that can automatically respond to detected threats. The article details implementation considerations,

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J, LAKSHMI. "A Python - Powered Keylogger Detection Tool." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31982.

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Keyloggers are a prevalent threat that record users' keystrokes to steal credentials and sensitive data. This paper presents a Python-based keylogger detection tool that scans running processes and checks them against indicators of compromise (IOCs). The detector integrates process inspection, signature matching, and automated remediation functions. A graphical user interface allows easy operation and threat response. The tool was tested against known keyloggers and demonstrated effective discovery with minimal false positives. This demonstrates Python's capabilities for building specialized s

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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 la

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Aminat Bolaji Bello, Akeem Olakunle Ogundipe, Awobelem A. George, and Olabode Anifowose. "The role of AI and machine learning in cybersecurity: Advancements in threat detection, anomaly detection and automated response." International Journal of Science and Research Archive 14, no. 2 (2025): 1587–97. https://doi.org/10.30574/ijsra.2025.14.2.0542.

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The increasing complexity and frequency of cyber threats have prompted organizations to seek more sophisticated defense mechanisms. Traditional signature-based methods and manual threat-hunting processes often fall short against evolving malware, zero-day exploits, and social engineering techniques. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as pivotal tools, enabling automated threat detection, real-time anomaly analysis, and proactive incident response. This review synthesizes current research and practices related to AI-driven cybersecurity, examining supervised and

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Dr., Anitha S., and D. Pravishka. "Intelligent Automation Framework for Penetration Testing and Security Assessment." Research and Reviews: Advancement in Cyber Security 2, no. 3 (2025): 39–45. https://doi.org/10.5281/zenodo.15583371.

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<em>As cyber threats grow in complexity and frequency, the need for efficient, continuous security assessment is critical. Traditional penetration testing is often time-consuming, manual, and resource-intensive. This paper presents a modular Automated Penetration Testing Framework that automates key phases namely reconnaissance, scanning, vulnerability detection, exploitation, and reporting using open-source tools and rule-based logic. The framework reduces human error, improves test consistency, and supports real-time reporting, making it ideal for dynamic and resource-constrained environment

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Varalakshmi, I., S. Pariselvam, and D. Oviya. "Hybrid Adaptive Threat Intelligence Detection System for Modern Cyber Attacks." Journal of Neonatal Surgery 14, no. 30S (2025): 654–71. https://doi.org/10.63682/jns.v14i30s.7031.

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Situations like zero-day attacks and advanced persistent threats require strong real-time detection of intrusion methods. The HATIDS combines signature-based detection and machine learning algorithms namely Isolation Forest and One-Class Support Vector Machine (SVM) employing a new weighted feature fusion engine for the best threat scoring. In the experiment on CIC-IDS2017 dataset and attacks such as DDoS and botnets, HATIDS has a 94.26% detection accuracy, 12 false positives reduced (6%) and 18 false negatives reduced (7%), and a mitigation time of 450 seconds, better than the previous hybrid

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Olakunle Abayomi Ajala and Olusegun Abiodun Balogun. "Leveraging AI/ML for anomaly detection, threat prediction, and automated response." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 2584–98. http://dx.doi.org/10.30574/wjarr.2024.21.1.0287.

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The rapid evolution of information and communication technologies, notably the Internet, has yielded substantial benefits while posing challenges to information system security. With an increasing frequency of cyber threats—from unauthorized access to data breaches—the digital landscape's vulnerability is evident. Addressing the financial impact of cybercrime, this study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity. Analyzing advancements and outcomes, the research explores practical techniques for anomaly detection, threat predic

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Olakunle, Abayomi Ajala, and Abiodun Balogun Olusegun. "Leveraging AI/ML for anomaly detection, threat prediction, and automated response." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 2584–98. https://doi.org/10.5281/zenodo.13377680.

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The rapid evolution of information and communication technologies, notably the Internet, has yielded substantial benefits while posing challenges to information system security. With an increasing frequency of cyber threats—from unauthorized access to data breaches—the digital landscape's vulnerability is evident. Addressing the financial impact of cybercrime, this study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity. Analyzing advancements and outcomes, the research explores practical techniques for anomaly detection, t

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Pasupuleti, Murali Krishna. "Threat Intelligence Automation Using Natural Language Processing on Dark Web Data." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 06 (2025): 399–411. https://doi.org/10.62311/nesx/rphcrcscrcp3.

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Abstract: This study presents an automated framework for threat intelligence gathering using Natural Language Processing (NLP) on dark web data. The growing sophistication of cyberattacks necessitates real-time detection of emerging threats. Traditional manual analysis of dark web forums is time-consuming and insufficient. This research proposes a hybrid NLP pipeline that integrates named entity recognition, sentiment analysis, and topic modeling to extract actionable threat indicators from darknet discussions. A dataset comprising over 100,000 dark web posts was analyzed, yielding high accura

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S, Dr Swapna. "Advanced Threat Detection with Active Directory and SIEM." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2799–805. https://doi.org/10.22214/ijraset.2025.68478.

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As cyber threats become more sophisticated, traditional security mechanisms relying solely on Active Directory (AD) for authentication and authorization lack real-time threat detection and response capabilities. This project enhances security by integrating AD with Splunk, a Security Information and Event Management (SIEM) solution, within a virtualized environment where Microsoft Server 2022 hosts AD services and a Domain Controller, while Splunk provides centralized security monitoring. PowerShell scripting automates user management and event log monitoring, improving administrative efficien

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Pitkar, Harshad. "Cloud Security Automation Through Symmetry: Threat Detection and Response." Symmetry 17, no. 6 (2025): 859. https://doi.org/10.3390/sym17060859.

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Cloud security automation has emerged as a critical solution for organizations facing increasingly complex cybersecurity challenges in cloud environments. This study examines the current state of cloud security automation, focusing on its role in symmetry between threat detection and response capabilities. Through analysis of recent market trends and technological developments, this paper explores key technologies, including Security Information and Event Management (SIEM), Extended Detection and Response (XDR), and Security Orchestration, Automation, and Response (SOAR) platforms. The integra

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Ismail, Dr Walaa Saber. "Threat Detection and Response Using AI and NLP in Cybersecurity." Journal of Internet Services and Information Security 14, no. 1 (2024): 195–205. http://dx.doi.org/10.58346/jisis.2024.i1.013.

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Introduction: In an age of rapid technical innovation and a growing digital world, protecting sensitive data from cyberattacks is crucial. The dynamic and complicated nature of these attacks requires novel cybersecurity solutions. Methods: This study analyses how Artificial Intelligence (AI) and Natural Language Processing (NLP) strengthen cybersecurity. The qualitative research approach is followed to gather data through a literature review of relevant scholarly articles and conduct interviews with cybersecurity specialists. Results: Recent AI advances have greatly enhanced the detection of a

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Nellore, Nitya Sri. "Securing Cloud-Native Architectures with Dynamic Threat Detection: A Scalable Approach for Multi-Tier Applications." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–6. https://doi.org/10.55041/ijsrem37877.

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As the adoption of cloud-native architectures grows, the complexity and scale of these systems introduce unique security challenges. Multi-tier applications, with their distributed and interconnected components, are particularly vulnerable to sophisticated cyber threats. This paper presents a scalable framework for securing cloud-native architectures through dynamic threat detection mechanisms. The framework leverages real-time monitoring, anomaly detection algorithms, and automated threat responses to ensure system integrity and resilience. Experimental results demonstrate the efficacy of the

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Sunnat, Rizaev, and Kholmuradov Sardorbek. "The Rise of AI in Cybersecurity: Transforming Threat Detection and Response." Multidisciplinary Journal of Science and Technology 5, no. 5 (2025): 144–48. https://doi.org/10.5281/zenodo.15351063.

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This article explores the transformative impact of artificial intelligence (AI) on cybersecurity, focusing on its role in enhancing threat detection and response mechanisms. As cyber threats become increasingly sophisticated, traditional methods are proving inadequate. The study examines key applications of AI, including anomaly detection, automated incident response, and user behavior analytics. Findings highlight significant improvements in detection accuracy and response times, alongside challenges such as data quality and workforce skills gaps. Ethical considerations regarding privacy and

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Sujan Kumar, Kummari. "Next-Generation Security Operations: Leveraging Automation for Proactive Threat Mitigation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43432.

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ABSTRACT As cybersecurity threats evolve, traditional Security Operations Centers (SOCs) face challenges such as alert overload, manual processes, and delayed incident response. The proposed method is automated SOC solution leveraging open-source technologies to enhance threat detection, streamline investigation processes, and enable proactive threat mitigation. The approach integrates comprehensive threat monitoring, a collaborative case management system, and an automation framework for security response actions. By implementing predefined processes and responsive capabilities, the solution

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A, Ms Ambika. "Advanced CCTV Using Machine Learning and Internet of Things." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 3307–16. https://doi.org/10.22214/ijraset.2025.72772.

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Current CCTV systems mainly act as surveillance tools, often just providing video evidence after an incident has occurred, without the ability to detect threats in real-time or respond automatically. This paper introduces an CCTV system that combines Machine Learning (ML) and Internet of Things (IoT) sensors, shifting from mere monitoring to proactive surveillance. The system employs YOLO V8 algorithms for real-time object detection, recognizing suspicious activities and analyzing behaviors, which helps in crime detection and minimizes response delays. Moreover, it integrates IoT-based environ

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Karan Singh Alang and Prof.(Dr) Vishwadeepak Singh Baghela. "Leveraging Large Language Models for Threat Detection and Cyber Defence: A Framework for Automated Security Analytics." International Journal for Research Publication and Seminar 16, no. 2 (2025): 17–25. https://doi.org/10.36676/jrps.v16.i1.46.

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In today’s rapidly evolving digital landscape, the volume and sophistication of cyber threats require innovative approaches to threat detection and cyber defence. Traditional security systems, while effective to a degree, are increasingly challenged by complex attack vectors that exploit vulnerabilities across multiple technology layers. This study introduces an advanced framework that leverages large language models (LLMs) for automated security analytics, offering a transformative solution to modern cybersecurity challenges. By integrating cutting-edge natural language processing techniques

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Bhardwaj, Arvind Kumar, P. K. Dutta, and Pradeep Chintale. "AI-Powered Anomaly Detection for Kubernetes Security: A Systematic Approach to Identifying Threats." Babylonian Journal of Machine Learning 2024 (August 20, 2024): 142–48. http://dx.doi.org/10.58496/bjml/2024/014.

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This study delves into the intricacies of AI-based threat detection in Kubernetes security, with a specific focus on its role in identifying anomalous behavior. By harnessing the power of AI algorithms, vast amounts of telemetry data generated by Kubernetes clusters can be analyzed in real-time, enabling the identification of patterns and anomalies that may signify potential security threats or system malfunctions. The implementation of AI-based threat detection involves a systematic approach, encompassing data collection, model training, integration with Kubernetes orchestration platforms, al

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JAYASIMHA, ROYYALA, and MRS. T. SUMITRA. "The Cyberattack Correlation and Prevention for Distribution Systems via Machine Learning." Journal of Engineering Sciences 16, no. 04 (2025): 159–63. https://doi.org/10.36893/jes.2025.v16i04.026.

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Cyberattacks on critical infrastructure, particularly power distribution systems, have increased in frequency and sophistication. Traditional security approaches often fail to provide real-time threat detection and correlation, leading to delayed response and significant operational disruptions. This paper presents a machine learningbased framework for cyberattack correlation and prevention in distribution systems. The proposed system leverages anomaly detection, pattern recognition, and predictive analytics to detect and mitigate cyber threats before they escalate. By analyzing network traffi

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Rizvi, Mohammed. "Enhancing cybersecurity: The power of artificial intelligence in threat detection and prevention." International Journal of Advanced Engineering Research and Science 10, no. 5 (2023): 055–60. http://dx.doi.org/10.22161/ijaers.105.8.

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Due to its ability to evaluate security threats in real-time and take appropriate action, artificial intelligence has emerged as a key component of cyber security. AI now has a bigger impact on spotting and stopping attacks that keep businesses on the cutting edge. Threat detection and prevention are the main focus of AI's role in cybersecurity. Artificial intelligence can detect trends and anomalies in network traffic and user behavior that may indicate a potential cyberattack through the use of machine learning algorithms and advanced data analysis. This allows security personnel to respond

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Kwon, Taeksoo, and Connor Hunjoon Kim. "Efficacy of Utilizing Large Language Models to Detect Public Threat Posted Online." Advances in Artificial Intelligence and Machine Learning 04, no. 04 (2024): 3125–34. https://doi.org/10.54364/aaiml.2024.44179.

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This paper examines the efficacy of utilizing large language models (LLMs) to detect public threats posted online. Amid rising concerns over the spread of threatening rhetoric and advance notices of violence, automated content analysis techniques may aid in early identification and moderation. Custom data collection tools were developed to amass post titles from a popular Korean online community, comprising 500 non-threat examples and 20 threats. Various LLMs (GPT-3.5, GPT-4, PaLM) were prompted to classify individual posts as either ”threat” or ”safe.” Results indicate promising performance,

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Chigozie Kingsley Ejeofobiri, Adedoyin Adetumininu Fadare, Olalekan Olorunfemi Fagbo, Valerie Ojinika Ejiofor, and Adetutu Temitope Fabusoro. "The role of Artificial Intelligence in enhancing cybersecurity: A comprehensive review of threat detection, response, and prevention techniques." International Journal of Science and Research Archive 13, no. 2 (2024): 310–16. http://dx.doi.org/10.30574/ijsra.2024.13.2.2161.

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As cyber threats continue to grow in scale and sophistication, traditional cybersecurity solutions have become increasingly insufficient to mitigate evolving risks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity by improving threat detection, automating response mechanisms, and preventing attacks before they occur. This review explores the intersection of AI and cybersecurity, focusing on AI-driven techniques in threat detection, automated response systems, and preventive measures. Furthermore, the paper discusses the challenges of deploying AI in cyber

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Vamsi Krishna Vemulapalli. "AI-driven cybersecurity: The future of adaptive threat defense." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 3248–55. https://doi.org/10.30574/wjarr.2025.26.2.1953.

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The rapid evolution of cyber threats has rendered traditional security approaches increasingly inadequate against sophisticated attackers. This article introduces an advanced AI-driven cybersecurity platform that leverages continuous learning and automated response capabilities to provide comprehensive protection across enterprise environments. The solution integrates multiple machine learning approaches—including behavioral analytics, deep learning models, and natural language processing—to establish baseline patterns, detect anomalies, and identify threats invisible to conventional tools. Th

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Kelkar, Atharva Abhijit, Palavi Manohar Adhav, Yogesh Madhukar Upare, and Pratiksha Sawant. "A Survey of Ransomware Resilience: Strategies for Prevention and Recovery." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1630–35. https://doi.org/10.22214/ijraset.2025.67557.

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Abstract: Ransomware and mobile malware have rapidly evolved into critical cybersecurity challenges, leveraging encryption, obfuscation, and self-updating techniques to evade detection[1]. Detection methods, such as static and dynamic analysis[2] and machine-learning-based anomaly detection [3], show promise in mitigating threats. Proactive approaches, including behaviorbased detection and hybrid cryptography like PayBreak [4], are essential for early threat identification and recovery. AI-driven defenses and situational awareness models [5] can improve detection rates, while socio-technical s

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