Using Machine Learning to Detect Cyber Attacks

Abstract

With the relentless growth in cyber threats, the imperative to fortify digital systems against malicious activities has become paramount. Leveraging the capabilities of machine learning (ML) has emerged as a pivotal strategy for bolstering cybersecurity defenses. This paper provides an in-depth exploration of the application of ML techniques in the realm of cyber attack detection. The study delves into the intricacies of feature selection, data preprocessing, and model evaluation techniques, pivotal components in refining the accuracy and efficiency of ML-based cybersecurity systems. The paper illustrates the practical implementation of these ML models across diverse cyber attack scenarios, showcasing their effectiveness in identifying and mitigating threats.