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 481–490 of 800 papers

TitleStatusHype
Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic ApproachCode0
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System—0
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems—0
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection—0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification—0
A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks—0
Intrusion Detection using Spatial-Temporal features based on Riemannian Manifold—0
Orthogonal variance-based feature selection for intrusion detection systems—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks—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