Intrusion Detection Systems
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Related Articles from SNS
FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems
arXiv:2606.03430v1 Announce Type: new 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. Current defences against model extraction rely either on identity-bound query monitoring, which is ineffective against distributed attackers (Sybil), or on prediction poisoning through soft-label perturbation, which is inapplicable to hard-label...
Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models
Announce Type: new Abstract: AI-based intrusion detection systems (IDS) have shown promise in detecting attacks on IoT systems. In this work, we explore the use of foundation models to detect and identify attacks, with a specific focus on RPL-based IoT networks. We study multiple attack types, attack variations, and network configurations, and provide insights into the performance of foundation models for attack identification.
An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks
Announce Type: new Abstract: With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification, dataset integration, and temporal feature learning to enhance detection performance in IoT networks. Using network traffic data, the proposed approach is evaluated on...
NetVAD: Foundation-Model Representation Learning for Identifier-Free Unsupervised Intrusion Detection
Announce Type: new Abstract: Detecting zero-day exploits in production networks requires robust Intrusion Detection Systems (IDS). However, current unsupervised models struggle to match the performance of supervised classifiers, which are trained for specific attacks only. To bridge this gap, we leverage the emerging capabilities of Network Foundation Models.
Rethinking IoT Intrusion Detection: Augmenting Routing Metrics with Radio Features
Announce Type: new Abstract: Machine learning-based intrusion detection systems (IDS) for RPL-based IoT networks often rely solely on routing layer features, which provide only a partial view of network behaviour. In this work, we investigate whether incorporating Transmit (TX) and Receive (RX) radio features alongside the standard RPL feature set can improve detection performance in an LSTM-based IDS. We evaluate the proposed approach across three different attack types, namely...
Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework
arXiv:2606.05710v1 Announce Type: new Abstract: The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities. AI-powered governance and automated decision-making systems are becoming a key part of the operation of critical infrastructure systems, including energy, healthcare, transportation, financial services, and communication...
Quantum-Inspired Reinforcement Learning for Low-Latency Intrusion Detection in V2X and Internet-of-Vehicles Networks
Announce Type: new Abstract: Smart cities increasingly depend on dense edge, IoT, and vehicular networks to deliver critical urban services, including traffic control, connected mobility, infrastructure monitoring, and energy management. In this ecosystem, the Internet of Vehicles (IoV) is central to intelligent transportation, enabling continuous communication among vehicles, roadside infrastructure, and cloud-edge platforms. This connectivity, however, also enlarges the attack surface and...
GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks
Announce Type: new Abstract: Rule-based Intrusion Detection and Prevention Systems (IDPS) offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adaptability to emerging and zero-day threats. Additionally, existing public datasets (e.g., CICIDS2017, UNSW-NB15) focus on traffic classification and provide little structured information to support automatic rule synthesis or prevention logic. To address this gap, we propose Generative...
Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018
Announce Type: new 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. The traditional cybersecurity approaches, including signature-based intrusion detection systems, have become less effective against today's cyber attacks, as they are unable to detect unknown and changing attacks in real time. To...
An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response
Announce Type: new Abstract: University Academic Management Information Systems (ACMIS) are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and academic integrity violations. Traditional rule-based intrusion detection systems are inadequate because many malicious activities are structurally indistinguishable from normal operations. This paper presents an AI-based security agent for ACMIS...