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 21–30 of 800 papers

TitleStatusHype
SecCAN: An Extended CAN Controller with Embedded Intrusion DetectionCode0
CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data—0
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the EdgeCode0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
Simultaneous Intrusion Detection and Localization Using ISAC Network—0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability—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