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 51–75 of 800 papers

TitleStatusHype
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
Efficient Deep CNN-BiLSTM Model for Network Intrusion DetectionCode1
A flow-based IDS using Machine Learning in eBPFCode1
Simplified and Secure MCP Gateways for Enterprise AI IntegrationCode1
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection SystemsCode1
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier DetectionCode1
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection SystemsCode1
Euler: Detecting Network Lateral Movement via Scalable Temporal Link PredictionCode1
FedMSE: Federated learning for IoT network intrusion detectionCode1
Explainability and Adversarial Robustness for RNNsCode1
Exploring QUIC Dynamics: A Large-Scale Dataset for Encrypted Traffic AnalysisCode1
Federated PCA on Grassmann Manifold for IoT Anomaly DetectionCode1
Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT AlgorithmsCode1
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack DetectionCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
A Novel SDN Dataset for Intrusion Detection in IoT NetworksCode1
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
netFound: Foundation Model for Network SecurityCode1
ARGUS: Context-Based Detection of Stealthy IoT Infiltration AttacksCode1
Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion DetectionCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language ModelCode1
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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