Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection

arXiv:2404.15382v1 Announce Type: new
Abstract: In recent years, there has been a growing interest in using Machine Learning (ML), especially Deep Learning (DL) to solve Network Intrusion Detection (NID) problems. However, the feature distribution shift problem remains a difficulty, because the change in features' distributions over time negatively impacts the model's performance. As one promising solution, model pretraining has emerged as a novel training paradigm, which brings robustness against feature distribution shift and has proven to be successful in Computer …

abstract arxiv change cs.ai cs.lg cs.ni deep learning detection distribution feature features however impacts machine machine learning network pretraining shift solve type