A Review of Machine Learning Algorithms for Cyberattack Detection: Performance Evaluation Across Attack Types and Perspectives on Deep Learning

The escalating complexity of cyber threats necessitates the development of more sophisticated and automated detection mechanisms. This paper provides a comprehensive survey and performance comparison of traditional machine learning algorithms for detecting major cyberattacks, including Distributed Denial of Service (DDoS), phishing, malware, ransomware, SQL injection, zero-day exploits, and Man-in-the-Middle (MitM) attacks. The performance of widely used algorithms, such as Random Forest, Gradient Boosting, Support Vector Machines (SVM), Decision Trees, Naïve Bayes, and K-Nearest Neighbors, is evaluated based on key metrics including accuracy, detection rate, and computational efficiency. The findings indicate that ensemble methods, particularly Random Forest and Gradient Boosting, consistently achieve high performance across diverse attack scenarios, whereas simpler models often struggle with complex or evolving threats. The study also discusses the emerging role of deep learning in cybersecurity, highlighting its potential for advanced threat detection alongside current challenges such as high computational demands and data dependencies. This review serves as a valuable resource and a practical guide for researchers and practitioners seeking to select effective ML-based detection tools, while also pointing toward the future integration of ML and DL for more robust cyber defense.