AI-Powered Threat Detection and Mitigation System: A Literature Survey | IJCT Volume 12 – Issue 6 | IJCT-V12I6P33
International Journal of Computer Techniques
ISSN 2394-2231
Author
A.MEENA , ARHAM NOORAIN ,
SNEHA. S , SOUMYA U GANJI , Dr. Guruprasad YK
Abstract
The rapidly evolving landscape of cyber threats, characterized by increasingly sophisticated Advanced Persistent Threats (APTs) and zero-day exploits, has exposed the critical limitations of traditional, signature-based defense mechanisms. These reactive systems struggle to identify novel attacks and adapt to the dynamic tactics of modern adversaries. This project, “AI-Powered Threat Detection and Mitigation System,” aims to explore and develop a proactive defense framework that leverages Artificial Intelligence (AI) and Machine Learning (ML) to enhance threat detection accuracy, enable predictive capabilities, and facilitate automated mitigation responses.
By synthesizing findings from contemporary research, this survey investigates the application of core AI methodologies—including supervised and unsupervised learning, deep learning, and ensemble methods—within cybersecurity. The project seeks to design a system that moves beyond mere detection, integrating real-time analysis, threat intelligence extraction, and automated countermeasures into a cohesive unit. The ultimate goal is to contribute to a paradigm shift in cybersecurity, from a reactive to a proactive and adaptive posture, thereby strengthening organizational resilience against complex cyber threats.
Keywords
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Conclusion
The escalating sophistication of cyber threats, particularly APTs, has rendered traditional, signaturebased cybersecurity measures insufficient. This literature survey has synthesized current research, confirming that Artificial Intelligence and Machine Learning are not merely enhancements but essential components for a modern, proactive defense strategy. While significant progress has been made in applying AI to threat detection, critical gaps remain in creating unified, explainable, and actionable systems that seamlessly integrate detection with automated mitigation.
The proposed “AI-Powered Threat Detection and Mitigation System” is positioned to address these gaps directly. By leveraging a hybrid AI model, automated response mechanisms, and a focus on explainability and usability, it aims to empower organizations to transition from a reactive to a proactive cybersecurity posture. As the digital threat landscape continues to evolve, such integrated and intelligent systems will be paramount in building resilient and secure digital infrastructures.
References
1.Z. Wang, “Artificial Intelligence in Cybersecurity Threat Detection,” International Journal of Computer Science and Information Technology, vol. 4, no. 1, pp. 203-209, 2024.
2.T. Mori, “AI for cyber resilience in critical infrastructure,” Journal of Cyber Resilience, 2023.
3.B. Pulyala, “AI-powered SIEM for proactive threat hunting and risk mitigation,” Infosec Journal, 2024.
4.V. Labu and M. Ahammed, “Next-gen cyber threat detection/mitigation using AI/ML,” in Proc. Int. Conf. on Comp. Security, 2024.
5.B. Balantrapu, “AI for predictive cyber threat intelligence (CTI),” IEEE Security & Privacy, 2024.
6.M. Aldawsari and S. Kouchay, “Integrating AI/ML into cloud security frameworks,” Cloud Security Review, 2024.
7.Venkoba et al., “General review of AI for threat detection/prevention,” ACM Computing Surveys, 2024.
8.Niveditha et al., “Predictive analytics using AI for cyber threat intelligence,” Journal of AI Research, 2024.
9.LeBlanc, “Detecting APTs using threat modeling and meta-detections,” SANS Institute Whitepaper, 2025.
10.Kumar et al., “Overview of AI (specifically ML) in cybersecurity for threat detection and response,” Cybersecurity Journal, 2023.
How to Cite This Paper
A.MEENA , ARHAM NOORAIN ,
SNEHA. S , SOUMYA U GANJI , Dr. Guruprasad YK (2025). AI-Powered Threat Detection and Mitigation System: A Literature Survey. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
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