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 291–300 of 800 papers

TitleStatusHype
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction—0
Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs—0
Deep Learning-based Embedded Intrusion Detection System for Automotive CAN—0
Quantised Neural Network Accelerators for Low-Power IDS in Automotive Networks—0
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN—0
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs—0
A Lightweight FPGA-based IDS-ECU Architecture for Automotive CAN—0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems—0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space—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