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.

翻译：许多机器学习与数据挖掘算法均依赖于训练数据与测试数据共享相同特征空间和分布的假设。然而，这一假设并不总是成立。例如，在某些场景中，我们需要对一个领域的数据进行分类，却仅有来自另一个领域的充足训练数据可用——后者可能遵循不同的分布。在此类情况下，跨领域成功迁移知识可显著提升学习性能，并减少对大量数据标注工作的需求。迁移学习（TL）因此成为应对这一挑战的前沿框架，尤其在安全相关任务中表现突出。本文旨在综述当前利用迁移学习技术解决安全问题的研究进展，讨论其在安全领域应用中现存的研究空缺，并探索TL辅助安全解决方案背景下潜在的未来研究方向及衍生问题。