Autonomous Threat Intelligence Aggregator: Leveraging AI for Real-Time Cyber Threat

Author : Anurag Pathak

Abstract :As cyber threats grow in complexity, traditional security mechanisms struggle to provide timely responses. This paper introduces the Autonomous Threat Intelligence Aggregator (ATIA), an AI-driven system for real-time cyber threat detection, classification, and mitigation. ATIA employs Natural Language Processing (NLP) to extract Indicators of Compromise (IoCs) from unstructured sources, while Machine Learning (ML) models classify risks and enhance threat assessment. Integrated with Security Information and Event Management (SIEM), ATIA automates responses, reducing manual intervention and improv- ing cybersecurity operations. The system incorporates adaptive security mechanisms, a decentralized architecture leveraging federated learning for privacy-preserving collaborative detection, and explainable AI (XAI) for improved interpretability of threat classification. Additionally, adversarial AI defenses are imple- mented to counter sophisticated evasion techniques. Experimental results demonstrate that ATIA significantly improves threat detection accuracy and reduces response time, offering a scalable and proactive approach to modern cybersecurity challenges

Keywords :Cyber Threat Intelligence, AI, Machine Learning, NLP, SIEM, Adaptive Security, Federated Learning, Explainable AI, Zero Trust, Adversarial AI

Conference Name :National Conference on Researches in Science and Technology (NCRST-25)

Conference Place Udaipur India

Conference Date 20th Apr 2025