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
this https URL

探索网络入侵检测设置中不同攻击类别之间的学习可转移性，通过训练深度学习模型以特定攻击类别，测试其于另一攻击类别。观察真实和合成数据增强技术对转移性的影响，研究转移关系的性质，可能是对称或非对称的。使用递归特征消除算法来解释转移关系，研究数据预处理技术以提高模型性能。