In this paper, we explore transferability in learning between different attack classes in a network intrusion detection setup. We evaluate transferability of attack classes by training a deep learning model with a specific attack class and testing it on a separate attack class. We observe the effects of real and synthetically generated data augmentation techniques on transferability. We investigate the nature of observed transferability relationships, which can be either symmetric or asymmetric. We also examine explainability of the transferability relationships using the recursive feature elimination algorithm. We study data preprocessing techniques to boost model performance. The code for this work can be found at https://github.com/ghosh64/transferability.

翻译：本文探讨了网络入侵检测场景下不同攻击类别之间的学习可迁移性。我们通过使用特定攻击类别训练深度学习模型，并在另一攻击类别上进行测试，来评估攻击类别的可迁移性。我们观察了真实数据增强技术和合成数据增强技术对可迁移性的影响，并研究了所观察到的可迁移性关系的性质——这些关系既可能具有对称性也可能具有非对称性。我们还利用递归特征消除算法检验了可迁移性关系的可解释性，并研究了提升模型性能的数据预处理技术。本研究的代码可在https://github.com/ghosh64/transferability获取。