Transfer Learning for Security: Challenges and Future Directions

TL;DR

This paper reviews how transfer learning can address domain differences in security applications, highlighting current challenges, research gaps, and future research directions to improve security solutions.

Contribution

It provides a comprehensive overview of transfer learning applications in security, identifying key challenges and proposing future research directions.

Findings

Transfer learning improves security tasks across different domains.

Research gaps include domain adaptation and data privacy issues.

Future directions involve developing robust TL models for security.

Abstract

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.