The paper investigates whether machine learning models for IoT intrusion detection truly learn attack patterns or rely on dataset shortcuts. Using the CyberFlowIoT-GICAP benchmark, the authors evaluate four learning paradigms across different feature sets and split strategies, finding that performance is largely driven by feature representation and that tree-based models can exploit temporal artifacts. The study also highlights asymmetric attack detectability and proposes a four-point protocol checklist for realistic evaluation.
By Uday Shankar Roy, Mahbuba Jahan Minu
The paper studies how an adaptive attacker can evade machine‑learning intrusion detection systems (IDS) deployed on a Raspberry Pi 3B+ by using a Deep Q‑Network (DQN) to learn port‑scan tactics that vary probe timing, TCP flags, and payload size. Three IDS models—XGBoost, a multi‑layer perceptron, and a 1D convolutional neural network—trained on TON_IoT telemetry were evaluated under black‑box, gray‑box, and white‑box feature‑visibility scenarios. Evasion success rates ranged from 61.9 % to 98.3 %, showing that even limited feature knowledge can enable high‑rate evasion and that the impact of feature visibility depends on the specific IDS model.
By Logan Andrew North, Priya Sanjay Kaluskar, Shasi Kumar Ramachandran Prabhu, Peilong Li, Suman Saha
arXiv:2608. 16159v1 Announce Type: cross Abstract: Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS).
By Konstantinos E. Kampourakis, Vasileios Gkioulos, Sokratis Katsikas
arXiv:2608. 05548v1 Announce Type: cross Abstract: Modern vehicles rely on the Controller Area Network (CAN) bus, whose design prioritizes low cost and real-time performance but provides no message authentication or encryption.
By Chandan Hegde, Mukundh R Reddy
arXiv:2608. 11802v1 Announce Type: cross Abstract: Methods to increase the resilience of systems to cyber-attacks become increasingly important.
By Martin Sachenbacher, Martin Leucker, Alexander Weiss, Aliyu Tanko 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:2606. 06261v1 Announce Type: cross Abstract: O-RAN enables a disaggregated baseband stack with programmable functions that communicate over standardized open interfaces.
By Francesco Spinelli, Esteban Municio, Pau Baguer, Gines Garcia-Aviles, Xavier Costa-Perez
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. 18599v1 Announce Type: cross Abstract: The Controller Area Network (CAN) protocol is the primary communication standard for Electronic Control Units (ECUs) in modern vehicles, but its lack of encryption and authentication exposes it to a range of security threats.
By Qiqi Liu, Runhan Song, Lei Cui, Heng Zhang, Yuyan Sun, Limin Sun
arXiv:2607. 00553v1 Announce Type: cross Abstract: Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their suitability for resource-constrained edge deployment.
By MD Azizul Hakim, Md Shihab Uddin, Talha Ibne Anis
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:2606. 03430v1 Announce Type: cross Abstract: Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) deployed in energy infrastructure are vulnerable to model theft attacks, which allow adversaries to create evasive traffic offline.
By Maxime Schwarzer, Laurin Holz, Tobias Huerten, Johannes Loevenich, Thies Moehlenhof, Roberto Rigolin F. Lopes, Veit Hagenmeyer