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 581590 of 800 papers

TitleStatusHype
Machine Learning Applications in Misuse and Anomaly Detection0
PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection0
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion DetectionCode0
Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection0
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection SystemsCode1
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection0
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures0
Fragments-Expert: A Graphical User Interface MATLAB Toolbox for Classification of File Fragments0
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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