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 521530 of 800 papers

TitleStatusHype
DeepAuditor: Distributed Online Intrusion Detection System for IoT devices via Power Side-channel Auditing0
Zero-shot learning approach to adaptive Cybersecurity using Explainable AI0
Artificial Neural Network for Cybersecurity: A Comprehensive Review0
Intrusion Detection and Localization for Networked Embedded Control Systems0
Detecting message modification attacks on the CAN bus with Temporal Convolutional NetworksCode0
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions0
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection0
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services0
A concise method for feature selection via normalized frequencies0
FlexParser -- the adaptive log file parser for continuous results in a changing world0
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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