Computational Intelligence for Cyber Defense: A Review

Abstract

Cyber-attacks pose a continuous and escalating threat, demanding ever-more sophisticated defense mechanisms. Traditional signature-based detection methods struggle to adapt to the rapidly evolving attack landscape.[1] This paper explores the potential of machine learning (ML) as a powerful tool for cyber-attack detection. Review recent advancements in the field. The reviewed research highlights the effectiveness of various ML techniques, including deep learning models,   for identifying anomalies and patterns indicative of malicious activity. The paper also discusses key challenges, such as the need for high-quality training datasets and the computational demands of certain ML algorithms. Finally, we explore promising future directions, including the integration of ML with other security solutions and the development of self-learning models that can autonomously adapt to evolving threats.[7] By leveraging the power of machine learning, we can significantly enhance our ability to detect and prevent cyber-attacks, safeguarding our digital infrastructure and fostering a more secure online environment.