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 331–340 of 800 papers

TitleStatusHype
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection—0
Real-time Regular Expression Matching—0
Forensic Data Analytics for Anomaly Detection in Evolving Networks—0
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection—0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection System—0
Towards Reliable Rare Category Analysis on Graphs via Individual CalibrationCode0
Man-in-the-Middle Intrusion Detection Based on CNN-LSTM Model—0
A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks—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