The rising complexity of cyber threats calls for more sophisticated and automated detection mechanisms. This paper provides a thorough review 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. We evaluate widely used algorithms such as Random Forest, Gradient Boosting, Support Vector Machines (SVM), Decision Trees, Naïve Bayes, and K-Nearest Neighbors
based on key metrics like accuracy, detection rate, and computational efficiency. Our 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 practical guide for selecting effective ML-based
detection tools and points toward future integrations of ML
and DL for stronger cyber defense.