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 5160 of 800 papers

TitleStatusHype
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
MSTREAM: Fast Anomaly Detection in Multi-Aspect StreamsCode1
A flow-based IDS using Machine Learning in eBPFCode1
netFound: Foundation Model for Network SecurityCode1
Digital Twin-based Intrusion Detection for Industrial Control SystemsCode1
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet DataCode1
PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based InferenceCode1
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion DetectorsCode1
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural NetworkCode1
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection SystemsCode1
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Benchmark Results

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