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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 800 papers

TitleStatusHype
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
Show:102550
← PrevPage 2 of 32Next →
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