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 421–430 of 800 papers

TitleStatusHype
FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic—0
Collaborative Learning for Cyberattack Detection in Blockchain Networks—0
The Cross-evaluation of Machine Learning-based Network Intrusion Detection SystemsCode0
Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection—0
Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection—0
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations—0
NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks—0
Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things—0
Trustworthy Anomaly Detection: A Survey—0
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
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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