arXiv:2609.36039v1 Announce Type: cross
Abstract: Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing s...
By Yufeng Xin, Bryant Goseland, Mohamed Rahouti
arXiv:2606. 07716v1 Announce Type: cross Abstract: Adversarial attacks pose a serious and growing threat to Machine Learning (ML)-based Intrusion Detection Systems (IDS), where imperceptible perturbations to network flow features can systematically mislead classifiers into accepting malicious traffic as benign.
By Maryam Zaman, Muhammad Khuram Shahzad
arXiv:2607. 13203v1 Announce Type: cross Abstract: False alarms remain a major barrier to deploying network intrusion detection systems (NIDS).
By Abu Fuad Ahmad, Istiaque Ahmed
arXiv:2606. 11098v1 Announce Type: cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017.
By Zach Moczkodan (Royal Military College of Canada, Kingston, Canada), Hany Ragab (Royal Military College of Canada, Kingston, Canada)
arXiv:2606. 00155v1 Announce Type: cross Abstract: Modern network intrusion detection systems (NIDS) are caught in a structural contradiction: the protocols carrying the highest threat intelligence are precisely those encrypted under TLS 1.
By Vivek Kumar Sharma
Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017. However, many existing studies neither supply their temporal modules with genuine sequence inputs nor evaluate under realistic, leakage-free conditions, making it unclear whether reported gains arise from true sequence-modeling capability.