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
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of VehiclesCode2
LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of VehiclesCode2
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
A Novel SDN Dataset for Intrusion Detection in IoT NetworksCode1
Cyber Attack Detection thanks to Machine Learning AlgorithmsCode1
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural NetworkCode1
An Intrusion Detection System based on Deep Belief NetworksCode1
A flow-based IDS using Machine Learning in eBPFCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
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
Continual Learning with Strategic Selection and Forgetting for Network Intrusion DetectionCode1
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
Digital Twin-based Intrusion Detection for Industrial Control SystemsCode1
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
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
A new Deep Learning Based Intrusion Detection System for Cloud SecurityCode1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
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
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
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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