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 211–220 of 800 papers

TitleStatusHype
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural NetworksCode1
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
usfAD Based Effective Unknown Attack Detection Focused IDS Framework—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron—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