Reproduce “Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence” on OpenResearch

About this paper

Siva Raja Sindiramutty

This review paper conceptualizes autonomous threat hunting as a proactive cybersecurity paradigm, systematically analyzing its evolution, core components, and the integration of state-of-the-art AI techniques. It outlines how AI transforms traditional threat intelligence, details specific machine learning, NLP, and deep learning applications, and discusses challenges like explainability and ethical considerations, along with future directions.

Key insights

• Threat intelligence has evolved from manual, human-intensive analysis to a highly automated, AI-driven discipline, enabling faster processing of vast datasets and early threat detection.
• Autonomous Threat Hunting (ATH) is a proactive paradigm requiring robust data collection, advanced ML algorithms, behavioral analytics, seamless threat intelligence integration, and human-machine collaboration.
• State-of-the-art AI techniques provide distinct capabilities: supervised learning detects known threats, unsupervised learning identifies anomalies, reinforcement learning enables adaptive strategies, NLP extracts intelligence from unstructured data, and deep learning analyzes sequential and image-based threat patterns.

Summary by alphaXiv.