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.

机器学习的广泛应用归功于其开发准确且可扩展模型的能力。在标记数据稀缺的网络安全领域，半监督学习（SSL）成为一种潜在的解决方案。 SSL 通过利用有限的标记数据和大量的未标记数据，擅长挑战传统监督和无监督算法的任务。本文对 SSL 在网络安全中的应用进行了全面的调查，重点关注打击各种网络犯罪，特别是入侵检测。尽管具有潜力，但仍然存在显着的研究差距，最近很少有研究全面审查 SSL 在网络安全中的应用。本研究探讨了专为网络安全量身定制的最先进的 SSL 技术，以弥补这一差距。确定相关方法并评估其有效性，以使研究人员和从业者能够获得增强网络安全措施的见解。这项工作揭示了 SSL 在解决网络安全领域数据稀缺问题方面的潜力，并概述了推进这一关键领域的新研究方向。通过弥合这一研究差距，这份手稿为在日益互联的世界中增强网络威胁检测和缓解铺平了道路。