Deceive Intrusion Detection System with GAN and Function-Preserving on Adversarial Samples in SDN-enabled networks

RESEARCH CREW
16:11 08/11/2020

Deceive Intrusion Detection System with GAN and Function-Preserving on Adversarial Samples in SDN-enabled networks (*selected as an Excellent Young Research Award at VANJ 2020 conference) (abstract only)

TIN LIÊN QUAN
Malware threatens cybersecurity by enabling data theft, unauthorized access, and extortion. Traditional malware detection systems (MDS) struggle with the increasing volume and complexity of malware. While machine learning (ML) and deep learning (DL) offer promising solutions, they remain vulnerable to adversarial attacks that evade detection. Recent research focuses on developing...