"AI-DRIVEN CYBERSECURITY: ENHANCING THREAT DETECTION AND RESPONSE THROUGH MACHINE LEARNING"

Abstract

This study analyses the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) techniques on modern cybersecurity operations, specifically focusing on enhancing threat detection accuracy and expediting incident response mechanisms within complex network environments. For this purpose, various supervised and unsupervised Machine Learning algorithms, including Support Vector Machines, Random Forests, and Anomaly Detection models, are employed and rigorously evaluated. The analysis utilizes diverse cybersecurity datasets, encompassing network traffic logs, endpoint telemetry, and malicious code samples, to train and validate these models. The findings demonstrate a significant improvement in threat detection rates and a substantial reduction in false positives when AI/ML models are integrated into security infrastructures. Specifically, deep learning models exhibit superior performance in identifying novel and sophisticated attack vectors, while anomaly detection techniques prove highly effective in detecting zero-day threats. Furthermore, the study quantifies how ML-driven automation can drastically reduce incident response times, thereby transforming reactive security postures into more proactive and resilient Défense strategies. The results underscore the critical role of AI/ML in building adaptive and intelligent cybersecurity systems capable of combating evolving cyber threats.