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 741750 of 800 papers

TitleStatusHype
A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection SystemsCode0
Data Distribution ValuationCode0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Learning Neural Representations for Network Anomaly DetectionCode0
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput ComputingCode0
LuNet: A Deep Neural Network for Network Intrusion DetectionCode0
Behavioural Reports of Multi-Stage MalwareCode0
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network SecurityCode0
ROSpace: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical SystemCode0
A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion DetectionCode0
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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