SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

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Showing 1–25 of 800 papers

TitleStatusHype
LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of VehiclesCode2
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of VehiclesCode2
Simplified and Secure MCP Gateways for Enterprise AI IntegrationCode1
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion DetectionCode1
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven AnalysisCode1
Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless NetworksCode1
Continual Learning with Strategic Selection and Forgetting for Network Intrusion DetectionCode1
FedMSE: Federated learning for IoT network intrusion detectionCode1
Exploring QUIC Dynamics: A Large-Scale Dataset for Encrypted Traffic AnalysisCode1
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language ModelCode1
Real-time Event Recognition of Long-distance Distributed Vibration Sensing with Knowledge Distillation and Hardware AccelerationCode1
Federated PCA on Grassmann Manifold for IoT Anomaly DetectionCode1
PolyLUT-Add: FPGA-based LUT Inference with Wide InputsCode1
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural NetworksCode1
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
On the Cross-Dataset Generalization of Machine Learning for Network Intrusion DetectionCode1
Improving Transferability of Network Intrusion Detection in a Federated Learning SetupCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion DetectionCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
netFound: Foundation Model for Network SecurityCode1
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack DetectionCode1
PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based InferenceCode1
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified