AI and Machine Learning for Cybersecurity: Emerging Techniques, Applications, and Research Challenges

Abstract

The rapid growth of digital networks, cloud computing, Internet of Things (IoT), and connected applications has significantly increased the complexity and frequency of cybersecurity threats. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful technologies for identifying malicious activities, detecting anomalies, predicting attacks, and automating security responses. This review provides a comprehensive examination of AI- and ML-based cybersecurity techniques, including supervised learning, unsupervised learning, deep learning, reinforcement learning, and ensemble methods. Their applications in intrusion detection, malware classification, phishing detection, botnet identification, network traffic analysis, and vulnerability assessment are discussed. The review further analyzes the benefits and limitations of traditional machine learning compared with deep learning approaches in dynamic cybersecurity environments. Key challenges involving adversarial attacks, data imbalance, privacy, model explainability, computational requirements, and the availability of high-quality datasets are examined. Emerging approaches such as federated learning, explainable AI, and automated threat intelligence are also explored. Finally, future research directions are identified for developing adaptive, trustworthy, and autonomous cybersecurity systems capable of responding to increasingly sophisticated cyber threats.