Smart Defenders: ML Techniques Powering Intrusion Detection Systems

Over the past decade, machine learning (ML) has become a vital tool in cybersecurity, transforming the detection and mitigation of cyber threats. As cyber-attacks have evolved, traditional security methods have struggled to keep up. This paper investigates the development of ML techniques in cybersecurity from 2013 to 2023, focusing on their effectiveness in threat detection, intrusion detection, malware classification, and intrusion detection systems. It reviews both supervised and unsupervised learning models, as well as ensemble learning methods used in various cybersecurity applications. Additionally, the paper explores challenges such as data privacy, adversarial attacks, and evolving cyber threats. Through this review, we provide insights into current trends, limitations of existing models, and potential future directions for research and development in this rapidly evolving field. The study also highlights the growing need for new approaches, such as federated learning, which enhances security while maintaining data privacy in distributed environments.