International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025
p-ISSN: 2395-0072
www.irjet.net
AI-Driven Cyber Threat Intelligence: Advanced Automated Threat Detection NITHIN V P MSC Computer Science, St. Thomas College (Autonomous), Thrissur, 680001, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------families, and zero-day exploits illustrate the scale and Abstract - The rapid expansion and sophistication of complexity of the challenges organisations face.
cyberattacks have exposed the fundamental weaknesses of traditional, reactive security methodologies. As malicious actors increasingly deploy automated and AI-driven attack strategies, modern cyber defense requires intelligent, adaptive mechanisms capable of anticipating and countering evolving threats. This paper presents an extensive analysis of Artificial Intelligence–enabled Cyber Threat Intelligence and its role in advancing automated threat detection systems. It reviews the major AI techniques—spanning supervised and unsupervised learning, deep learning architectures, and languageprocessing models—that contribute to contemporary cybersecurity solutions. Supervised learning enhances the detection of known threats, while unsupervised methods excel at identifying atypical behaviors associated with new and stealthy attacks. Deep learning approaches, including convolutional and recurrent neural networks, show strong performance in extracting complex patterns from largescale security data. In addition, language-processing techniques contribute by interpreting intelligence from unstructured reports and online sources. The study highlights the benefits of AI-driven defense frameworks alongside key limitations such as model transparency, data dependence, and susceptibility to adversarial interference. Emerging research directions, including federated learning and quantum-driven computation, are discussed to provide a future outlook on intelligent cyber defense evolution.
Conventional cybersecurity technologies primarily depend on static rules and signature matching to detect malicious activity. While effective against previously identified threats, these systems offer limited protection against new attack variants. The enormous volume of data produced by contemporary infrastructures further overwhelms human analysts, leading to delays in threat identification and frequent false alarms. The reactive nature of traditional solutions means attackers often gain a significant advantage, as defensive actions typically occur only after a compromise has taken place. The growing inadequacy of legacy security models has accelerated the adoption of Artificial Intelligence as a core component of next-generation threat detection. AI-based Cyber Threat Intelligence introduces a predictive and adaptive approach by analyzing diverse data sources, identifying subtle behavioral deviations, and detecting emerging threats before they escalate. Through machine learning, deep learning, and natural language processing, AI tools continuously learn from evolving environments, enabling rapid and precise identification of malicious behaviors. These capabilities allow organizations to transition from reactive defense to a more proactive and intelligence-driven security posture. This paper provides a structured examination of AI-driven Cyber Threat Intelligence. It discusses the primary computational techniques used for threat detection, evaluates their strengths and limitations, and explores how AI enhances predictive security. The paper also outlines the practical challenges encountered when integrating AI in cybersecurity—such as data quality, adversarial interference, and the need for transparent decision-making models—and highlights the future direction of intelligent automated defence technologies.
Key Words: Artificial Intelligence, Cyber Threat Intelligence (CTI), Automated Threat Detection, Machine Learning, Deep Learning, Anomaly Detection, Predictive Analytics, Explainable AI (XAI), Adversarial AI, Incident Response
1. INTRODUCTION Digital environments have undergone a dramatic transformation, resulting in a constantly expanding cyberattack surface. The widespread adoption of cloud platforms, interconnected devices, and distributed computing has created new opportunities for attackers to exploit system weaknesses. Modern adversaries employ sophisticated strategies designed to bypass conventional defense tools, allowing them to infiltrate networks, remain undetected for extended periods, and cause substantial operational or financial disruption. Threats such as advanced persistent intrusions, rapidly evolving malware
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2. LITERATURE REVIEW The rapid evolution of cyber threats has forced a major shift in how security systems are designed and operated. Early generations of cybersecurity tools relied heavily on predefined signatures, static rule sets, and manually constructed blacklists. These mechanisms were effective during a period when malware families evolved slowly, and threat patterns exhibited limited variation. However, as
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