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 451–460 of 800 papers

TitleStatusHype
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector—0
Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats—0
Towards Explainable Meta-Learning for DDoS Detection—0
Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks—0
IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset—0
Collaborative Learning for Cyberattack Detection in Blockchain Networks—0
FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic—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
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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