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Recent literature highlights a significant cross-impact between transferlearning and cybersecurity. Many studies have been conducted on using transferlearning to enhance security, leading to various applications in differentcybersecurity tasks. However, previous research is focused on specific areas ofcybersecurity. This paper presents a comprehensive survey of transfer learningapplications in cybersecurity by covering a wide range of domains, identifyingcurrent trends, and shedding light on under-explored areas. The surveyhighlights the significance of transfer learning in addressing critical issuesin cybersecurity, such as improving detection accuracy, reducing training time,handling data imbalance, and enhancing privacy preservation. Additionalinsights are provided on the common problems solved using transfer learning,such as the lack of labeled data, different data distributions, and privacyconcerns. The paper identifies future research directions and challenges thatrequire community attention, including the need for privacy-preserving models,automatic tools for knowledge transfer, metrics for measuring domainrelatedness, and enhanced privacy preservation mechanisms. The insights androadmap presented in this paper will guide researchers in further advancingtransfer learning in cybersecurity, fostering the development of robust andefficient cybersecurity systems to counter emerging threats and protectsensitive information. To the best of our knowledge, this paper is the first ofits kind to present a comprehensive taxonomy of all areas of cybersecurity thatbenefited from transfer learning and propose a detailed future roadmap to shapethe possible research direction in this area.

Systemization of Knowledge (SoK)- Cross Impact of Transfer Learning in Cybersecurity: Offensive, Defensive and Threat Intelligence Perspectives