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@article{187408,
        author = {Yamini J and Kanujhashree B},
        title = {Cyber Threat Intelligence using Artificial Intelligence},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {4253-4263},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187408},
        abstract = {In today’s digital era, the frequency, sophistication, and diversity of cyber threats are increasing at an unprecedented rate, posing significant risks to individuals, organizations, and nation-states. Traditional cybersecurity measures, such as signature-based detection systems and manual threat analysis, are often reactive and insufficient to handle advanced cyber-attacks, zero-day vulnerabilities, and evolving threat landscapes. Cyber Threat Intelligence (CTI) has emerged as a critical approach for proactive cybersecurity, providing actionable insights about potential threats, attack vectors, tactics, and vulnerabilities. By leveraging these insights, organizations can anticipate, prevent, and respond effectively to cyber incidents.
Artificial Intelligence (AI) has transformed the field of CTI by introducing advanced computational techniques that enable automated data processing, real-time threat detection, predictive analytics, and adaptive security measures. AI-driven CTI employs a variety of techniques including Machine Learning (ML) for anomaly detection and classification, Deep Learning (DL) for pattern recognition and malware analysis, and Natural Language Processing (NLP) for extracting intelligence from unstructured data sources such as threat reports, forums, and social media platforms. Reinforcement learning further enhances decision-making by optimizing automated response strategies based on historical attack outcomes.
This paper provides a comprehensive study of AI-based CTI, exploring the methodologies, frameworks, applications, and challenges of integrating AI into cybersecurity intelligence systems. It highlights the practical applications of AI in threat detection, threat prediction, and automated incident response, with illustrative examples and case studies from financial institutions, cloud computing, and healthcare domains. Moreover, the paper discusses the current limitations of AI in CTI, including data quality and quantity issues, adversarial attacks on AI models, false positives and false negatives, and ethical and privacy concerns associated with monitoring user behavior.
Finally, the study examines emerging trends and future directions, such as explainable AI (XAI) for transparent threat analysis, federated learning for collaborative intelligence sharing without compromising privacy, blockchain integration for secure CTI exchange, and continuous adaptive learning systems. The research concludes that AI-enhanced CTI not only strengthens an organization’s cybersecurity posture but also enables proactive defense against increasingly complex and dynamic cyber threats. AI-driven approaches are essential for modern cybersecurity strategies, ensuring rapid, accurate, and intelligent threat intelligence and mitigation in a rapidly evolving digital landscape.},
        keywords = {Cyber Threat Intelligence, Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Anomaly Detection, Cybersecurity},
        month = {November},
        }