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 4150 of 800 papers

TitleStatusHype
Representation Learning for Content-Sensitive Anomaly Detection in Industrial NetworksCode1
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
Protocol Based Deep Intrusion Detection for DoS and DDoS attacks using UNSW-NB15 and Bot-IoT data-setsCode1
Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT AlgorithmsCode1
T-DFNN: An Incremental Learning Algorithm for Intrusion Detection SystemsCode1
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph LearningCode1
TOD: GPU-accelerated Outlier Detection via Tensor OperationsCode1
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
Unveiling the potential of Graph Neural Networks for robust Intrusion DetectionCode1
A new Deep Learning Based Intrusion Detection System for Cloud SecurityCode1
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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