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 8190 of 800 papers

TitleStatusHype
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network SecurityCode0
A Generative Model Based Honeypot for Industrial OPC UA CommunicationCode0
Individual Packet Features are a Risk to Model Generalisation in ML-Based Intrusion DetectionCode0
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved FeaturesCode0
A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion DetectionCode0
Fragments Expert A Graphical User Interface MATLAB Toolbox for Classification of File FragmentsCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Evaluating the Performance of Machine Learning-Based Classification Models for IoT Intrusion DetectionCode0
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber SecurityCode0
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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