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

Showing 341350 of 800 papers

TitleStatusHype
Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice0
Experimental Review of Neural-based approaches for Network Intrusion Management0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks0
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning0
Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes0
Explainable Intrusion Detection Systems Using Competitive Learning Techniques0
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13Unverified
2K-Nearest NeighborsAccuracy (%)98.07Unverified
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1MSTREAM-PCAAUC0.94Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9Unverified