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 641–650 of 800 papers

TitleStatusHype
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response—0
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things—0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems—0
AutoIDS: Auto-encoder Based Method for Intrusion Detection System—0
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey—0
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization—0
Intrusion Detection using Sequential Hybrid Model—0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors—0
WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning—0
Kernel density estimation based sampling for imbalanced class distribution—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