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 71–80 of 800 papers

TitleStatusHype
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Cyber Attack Detection thanks to Machine Learning AlgorithmsCode1
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks—0
AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks—0
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection—0
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
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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