arXiv:2608. 11291v1 Announce Type: cross Abstract: Intrusion detection systems (IDS) and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods.
With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response. In this paper, we propose a reinforcement learning-based dynamic cyber defense framework.
arXiv:2608. 12190v1 Announce Type: cross Abstract: With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response.
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...
The paper introduces a new method for generating universal adversarial perturbations (UAPs) against deep reinforcement learning (DRL)-based intrusion detection systems (IDS). It leverages Probabilistic Robustness (PR) as a post‑hoc metric to guide UAP creation, integrating PR directly into the optimization objective. The authors further develop PX‑UAP, which incorporates explainable AI (XAI) to shape perturbations within realistic domain constraints, and provide a theoretical analysis of its design. Experiments show PX‑UAP outperforms existing UAP techniques in attack effectiveness.
arXiv:2606. 05714v1 Announce Type: cross Abstract: Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors including healthcare, finance, transportation, energy and government systems is growing.
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.
arXiv:2603. 17717v4 Announce Type: replace-cross Abstract: Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS).
arXiv:2605. 13922v2 Announce Type: replace-cross Abstract: During thDuring the last few years, the term Mechanistic Interpretability, a specific area, under the umbrella of explainable artificial intelligence (XAI), has been introduced, to explain the decisions made by complex machine learning (ML) models in critical systems like UAV intrusion detection systems (UAVIDS).
JEV-IDS is an open experimental general network intrusion detection system that uses the Jev System One Model to detect zero‑day intrusions even when labeled data are scarce. The system processes one flow per request and asks the model two questions: a binary attack probability and a finite‑choice traffic category. In tests on a 300‑flow NSL‑KDD pilot split, JEV-IDS achieved an F1‑score of 0.859, precision of 0.941, recall of 0.790, and a novel‑attack recall of 0.838, while being 4.8 times faster and 3.8 times cheaper than GPT‑5.6 Luna and producing 15 times fewer false alarms than a low‑data Random Forest.
arXiv:2607. 17105v1 Announce Type: cross Abstract: It is crucial to safeguard computer networks from evolving network security threats and unknown cyberattacks.
arXiv:2607. 01305v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments.
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.