Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection

arXiv:2404.04188v1 Announce Type: cross
Abstract: The growing cybersecurity threats make it essential to use high-quality data to train Machine Learning (ML) models for network traffic analysis, without noisy or missing data. By selecting the most relevant features for cyber-attack detection, it is possible to improve both the robustness and computational efficiency of the models used in a cybersecurity system. This work presents a feature selection and consensus process that combines multiple methods and applies them to several network datasets. Two …

abstract analysis arxiv computational cs.cr cs.lg cs.ni cyber cybersecurity cybersecurity threats data detection efficiency feature features feature selection machine machine learning network quality quality data robust robustness threats traffic traffic analysis train type