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

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Showing 401–410 of 800 papers

TitleStatusHype
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
A model for multi-attack classification to improve intrusion detection performance using deep learning approaches—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
A multiagent based framework secured with layered SVM-based IDS for remote healthcare systems—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—0
An Adversarial Approach for Explainable AI in Intrusion Detection Systems—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

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