Transfer Learning for Security: Challenges and Future Directions
Adrian Shuai Li Arun Iyengar Ashish Kundu Elisa Bertino
Many machine learning and data mining algorithms rely on the assumption that
the training and testing data share the same feature space and distribution.
However, this assumption may not always hold. For instance, there are
situations where we need to classify data in one domain, but we only have
sufficient training data available from a different domain. The latter data may
follow a distinct distribution. In such cases, successfully transferring
knowledge across domains can significantly improve learning performance and
reduce the need for extensive data labeling efforts. Transfer learning (TL) has
thus emerged as a promising framework to tackle this challenge, particularly in
security-related tasks. This paper aims to review the current advancements in
utilizing TL techniques for security. The paper includes a discussion of the
existing research gaps in applying TL in the security domain, as well as
exploring potential future research directions and issues that arise in the
context of TL-assisted security solutions.