Harnessing Machine Learning Algorithms for Proactive Cyber Threat Detection and Real-Time Incident Response in Enterprise Networks

Abstract

The networks supporting business enterprises are becoming increasingly vulnerable to advanced cyber threats, including ransomware, insider threats, and advanced persistent attacks, which necessitate proactive countermeasures. With proactive threat detection and real-time incident response, ML has become a revolutionary way of optimising cybersecurity. The conceptual review synthesises existing frameworks, theoretical models, and algorithmic solutions to indicate how ML may be incorporated into enterprise security architectures. The paper analyzes ML paradigms of interest, including supervised, unsupervised, deep learning, and reinforcement learning, focusing on their conceptual strengths, limitations, and applicability in identifying known and unknown threats. It also explores the architectures of ML-enabled detection systems, including data gathering, feature extraction, model training, ongoing surveillance, and the incorporation of automated responses. Analysis i s presented on conceptual models of real-time incident response, including response orchestration, intelligent decision support, mechanical playbooks, and Security Orchestration, Automation, and Response (SOAR) incorporation. Among the issues the review identifies, there are critical gaps and challenges, including data privacy restrictions, interpretability issues, scalability, adversarial threats, and a lack of integration of conceptual frameworks. It emphasizes the necessity that any proposed model should be empirically validated so that the model becomes practically applicable. The synthesis of these ideas has helped to build a theoretically enlightened vision of ML-enabled cybersecurity. It has highlighted a course of action to construct resilient, adaptive, and predictive enterprise security mechanisms.