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 1–50 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
On the Cross-Dataset Generalization of Machine Learning for Network Intrusion DetectionCode1
Explainability and Adversarial Robustness for RNNsCode1
Intrusion Detection for Cyber-Physical Systems using Generative Adversarial Networks in Fog EnvironmentCode1
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
A Novel SDN Dataset for Intrusion Detection in IoT NetworksCode1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
Digital Twin-based Intrusion Detection for Industrial Control SystemsCode1
Euler: Detecting Network Lateral Movement via Scalable Temporal Link PredictionCode1
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection SystemsCode1
Kairos: Practical Intrusion Detection and Investigation using Whole-system ProvenanceCode1
Machine learning on knowledge graphs for context-aware security monitoringCode1
netFound: Foundation Model for Network SecurityCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
A flow-based IDS using Machine Learning in eBPFCode1
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
Efficient Deep CNN-BiLSTM Model for Network Intrusion DetectionCode1
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection SystemsCode1
Federated PCA on Grassmann Manifold for IoT Anomaly DetectionCode1
Improving Transferability of Network Intrusion Detection in a Federated Learning SetupCode1
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack DetectionCode1
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion DetectionCode1
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput ApplicationsCode1
MSTREAM: Fast Anomaly Detection in Multi-Aspect StreamsCode1
MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of VehiclesCode1
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
A new Deep Learning Based Intrusion Detection System for Cloud SecurityCode1
An Intrusion Detection System based on Deep Belief NetworksCode1
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
ARGUS: Context-Based Detection of Stealthy IoT Infiltration AttacksCode1
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion DetectionCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Continual Learning with Strategic Selection and Forgetting for Network Intrusion DetectionCode1
Cyber Attack Detection thanks to Machine Learning AlgorithmsCode1
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural NetworkCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven AnalysisCode1
Exploring QUIC Dynamics: A Large-Scale Dataset for Encrypted Traffic AnalysisCode1
FedMSE: Federated learning for IoT network intrusion detectionCode1
Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT AlgorithmsCode1
Open-Source Framework for Encrypted Internet and Malicious Traffic ClassificationCode1
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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