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 791–800 of 800 papers

TitleStatusHype
Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks—0
Machine Learning Techniques for Intrusion Detection—0
A Review of Machine Learning based Anomaly Detection Techniques—0
The Dendritic Cell Algorithm for Intrusion Detection—0
Immune System Approaches to Intrusion Detection - A Review (ICARIS)—0
A SVM and K-means clustering based fast and efficient intrusion detection system—0
Predicting Network Attacks Using Ontology-Driven Inference—0
Fast Feature Reduction in intrusion detection datasets—0
A survey on deep packet inspection for intrusion detection systems—0
Intrusion Detection Systems Using Adaptive Regression Splines—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