Enhancing Cyber Threat Detection and Response with Machine Learning: A Comparative Analysis of ML Techniques for Real-Time Monitoring and Incident Response
Kinjol Saha, Sudipto Roy Pritom · Zenodo (CERN European Organization for Nuclear Research) · 2023
Organizations struggle to recognise and respond to cyber attacks effectively because of their sophistication and frequency, which are both rising. The dynamic nature of contemporary cyber threats is proving difficult to combat with traditional security measures and rule-based systems. The use of machine learning (ML) approaches to improve cyber threat detection and response capabilities is explored in this research article. In the context of cyber security, it gives a comparative examination of various ML algorithms and models used for real-time monitoring and incident response. For the purpose of detecting cyber threats, the study examines the efficacy of supervised learning, unsupervised learning, reinforcement learning, and deep learning techniques. Additionally, it looks at how to leverage ML-based incident response systems, including automated response actions, threat intelligence integration, and event detection and classification.