Network Intrusion Detection
6개 벤치마크 · 논문 328편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
Deep Anomaly Detection with Deviation Networks
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
Papers
From Network Intrusion Detection to Blockchain-Backed Endpoint Detection and Response: Mapping the Landscape of Decentralized Detection-and-Response Architectures
While the literature on blockchain-assisted intrusion detection and prevention systems (IDS/IPS) for Internet of Things (IoT) and Industrial Internet of Things (IIoT) networks is mature, existing systematic reviews suffe…
Network Intrusion DetectionRobust Unsupervised Network Intrusion Detection via Federated Learning with Selective Aggregation under Anomalous Sample Contamination
Network intrusion detection systems (NIDS) have become essential for Internet of Things (IoT) environments, as malware targeting IoT devices continues to evolve in sophistication. Unsupervised learning approaches offer a…
Network Intrusion DetectionFederated LearningAnomaly DetectionHarnessing Disagreement: Detecting Correlated Agreement Blindness in Multi-Agent Triage
Disagreement-triggered escalation can create a structural blind spot in multi-agent arbitration: as base learners improve, they tend to converge, weakening safety monitoring where correlated failures concentrate. We term…
Network Intrusion DetectionEnhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN
Recent advancements in the Internet of Things (IoT) emphasize the urgent need for advanced network security, as IoT networks feature dynamic topologies, imbalanced traffic, and complex attack patterns. Unlike general IT …
Network Intrusion DetectionMulti-Level Distributional Entropy for Explainable Network Intrusion Detection
Machine learning network intrusion detection systems (IDS) rely on aggregate flow statistics that discard distributional structure, while established entropy measures require raw packet sequences unavailable in pre-aggre…
Network Intrusion DetectionDecoherence as Defence and the Magnitude of Noise Regularisation: A Rigorous N -Qubit Theory of Stochastic Quantum Neural Networks for Adversarially Robust Network Intrusion Detection
Stochastic quantum neural networks (SQNNs) encode neuronal activations as qubits, synaptic topology as entanglement, and neural noise through a Lindblad master equation. A recent conference study applied a ring-entangled…
Network Intrusion DetectionAnomaly Detection