Twin Auto-Encoder Model for Learning Separable Representation in Cyberattack Detection

arXiv:2403.15509v1 Announce Type: cross
Abstract: Representation Learning (RL) plays a pivotal role in the success of many problems including cyberattack detection. Most of the RL methods for cyberattack detection are based on the latent vector of Auto-Encoder (AE) models. An AE transforms raw data into a new latent representation that better exposes the underlying characteristics of the input data. Thus, it is very useful for identifying cyberattacks. However, due to the heterogeneity and sophistication of cyberattacks, the representation of AEs …

abstract arxiv auto cs.ai cs.cr cs.lg cyberattack data detection encoder pivotal raw representation representation learning role success twin type vector