Machine Learning in Cyber Security: A Comprehensive Review

The escalating sophistication and frequency of cyber threats have necessitated the development of intelligent and adaptive cybersecurity solutions. Traditional security measures are increasingly inadequate against modern cyber attacks that leverage advanced techniques and exploit zero-day vulnerabilities. Machine learning (ML) has emerged as a transformative technology in cybersecurity, offering automated threat detection, pattern recognition, and predictive capabilities that significantly enhance defensive mechanisms. This comprehensive survey systematically reviews current machine learning techniques applied to cybersecurity, analyzing their effectiveness across various attack categories including network-based attacks, malware, social engineering, web application vulnerabilities, password attacks, and IoT-based threats. Our critical analysis reveals that ensemble methods and deep learning approaches achieve detection accuracies exceeding 99% in controlled environments, with Decision Tree classifiers, Random Forest, and LSTM networks showing particular promise. However, significant gaps exist in real-world deployment, adversarial robustness, and cross-domain generalization. Key findings indicate that while ML-driven solutions demonstrate superior performance over traditional signaturebased methods, challenges remain in handling concept drift, reducing false positive rates, and ensuring model interpretability. This survey contributes a structured taxonomy of ML applications in cybersecurity, identifies critical research gaps, and proposes future research directions focusing on explainable AI, federated learning, and quantum-resistant security mechanisms.