A Survey on the Applications of Semi-supervised Learning to Cyber-security

Abstract

Machine Learning's widespread application owes to its ability to develop accurate and scalable models. In cyber-security, where labeled data is scarce, Semi-Supervised Learning (SSL) emerges as a potential solution. SSL excels at tasks challenging traditional supervised and unsupervised algorithms by leveraging limited labeled data alongside abundant unlabeled data. This article presents a comprehensive survey of SSL in cyber-security, focusing on countering diverse cybercrimes, particularly intrusion detection. Despite its potential, a notable research gap persists, with few recent studies comprehensively reviewing SSL's application in cyber-security. This study examines state-of-the-art SSL techniques tailored for cyber-security to address this gap. Relevant methods are identified, and their effectiveness is evaluated to empower researchers and practitioners with insights to enhance cyber-security measures. This work sheds light on SSL's potential in addressing data scarcity in cyber-security domains in addition to outlining new research directions to advance this crucial field. By bridging this research gap, this manuscript paves the way for enhanced cyber-threat detection and mitigation in an increasingly interconnected world.