The paper investigates how to properly validate candidate models before promoting them to replace incumbent classifiers in adaptive network intrusion detection systems. It demonstrates that promotion decisions can be biased by how challengers are constructed and the amount of evidence they receive, and that using self‑contained challenger pipelines and sufficient candidate evidence reduces apparent promotion harm. The study also shows that policy rankings shift with candidate comparability and that no single update policy dominates across benchmarks.
By Roberto Fern\'andez-Barrios, Iker Pastor-L\'opez, Amaia Pikatza-Huerga, Pablo Garc\'ia Bringas
arXiv:2606. 12075v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks.
By Mayank Raj, Nathaniel D. Bastian, Lance Fiondella, Gokhan Kul
arXiv:2608. 05199v1 Announce Type: cross Abstract: Autonomous security agents operate as staged pipelines, such as classifying network traffic and then attributing attacks to a specific technique.
By Zhenpeng Li
The study compares XGBoost and RoBERTa‑LoRA for network intrusion detection across three evaluation axes: same‑dataset performance, cross‑dataset transfer, and adversarial evasion. Both models perform similarly on the same dataset, but XGBoost outperforms RoBERTa‑LoRA by 15 F1 points and 25 balanced accuracy points when transferred to a different network, while RoBERTa‑LoRA wins by about 17 F1 points under adversarial evasion. Feature‑leakage ablation shows that cross‑dataset transfer improvements are non‑monotonic and directional, suggesting leakage is spread across features rather than isolated.
"whyItMatters":"The findings demonstrate that a model’s superiority depends on the specific robustness axis evaluated, underscoring the need for multi‑axis, multi‑metric testing in network intrusion detection research."
By Muhammad Ebad Atif, Muhammad Haider Ali
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:2608. 15761v1 Announce Type: cross Abstract: Edge-IIoTset is the reference benchmark for machine-learning intrusion detection in the industrial Internet of Things, and results reported on it cluster above 99%.
By Mostafa M. Galal
arXiv:2602. 14161v2 Announce Type: replace Abstract: Detecting prompt injection, jailbreak attacks, and harmful requests is critical for deploying LLM-based agents safely, yet current evaluation practices in this literature overestimate generalization.
By Max Fomin
arXiv:2606. 01437v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) are highly susceptible to adversarial perturbations, leading to extensive research on robustness for safety-critical applications.
By Daniel Sadig, Mohammadreza Maleki, Hamed Karimi, Reza Samavi
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:2606. 20502v1 Announce Type: cross Abstract: Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved.
By Arastoo Zibaeirad, Marco Vieira
The paper addresses the lack of statistical validity in language‑model‑based network intrusion detection. It introduces traffic‑aware conformal prediction, which calibrates on attacker‑expected traffic to restore coverage guarantees, and further mitigates adaptive attacks by removing attacker‑controllable features, achieving exact pathwise coverage. Experiments on three benchmarks show that this approach maintains coverage while incurring a modest accuracy cost.
By Zhenpeng Li