Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

TLDR

Machine learning is increasingly important in cybersecurity, addressing challenges through methods such as deep learning, SVMs, and Bayesian classification to detect hidden trends in network data and mitigate attacks. The survey aims to evaluate how machine learning can make malware detection more actionable, scalable, and effective, assessing its implementation on cybersecurity data and its effectiveness against rising malware threats. The authors review machine learning techniques applied to cybersecurity data, focusing on how they secure systems. The review discusses how machine learning mitigates existing threats, while highlighting shortcomings of state‑of‑the‑art models and the evolution of attack patterns over the past decade.

Abstract

Machine learning is of rising importance in cybersecurity. The primary objective of applying machine learning in cybersecurity is to make the process of malware detection more actionable, scalable and effective than traditional approaches, which require human intervention. The cybersecurity domain involves machine learning challenges that require efficient methodical and theoretical handling. Several machine learning and statistical methods, such as deep learning, support vector machines and Bayesian classification, among others, have proven effective in mitigating cyber-attacks. The detection of hidden trends and insights from network data and building of a corresponding data-driven machine learning model to prevent these attacks is vital to design intelligent security systems. In this survey, the focus is on the machine learning techniques that have been implemented on cybersecurity data to make these systems secure. Existing cybersecurity threats and how machine learning techniques have been used to mitigate these threats have been discussed. The shortcomings of these state-of-the-art models and how attack patterns have evolved over the past decade have also been presented. Our goal is to assess how effective these machine learning techniques are against the ever-increasing threat of malware that plagues our online community.