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 271–280 of 800 papers

TitleStatusHype
Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice—0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
usfAD Based Effective Unknown Attack Detection Focused IDS Framework—0
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis—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
An Adversarial Robustness Benchmark for Enterprise Network 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