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 271–280 of 800 papers

TitleStatusHype
ARLIF-IDS -- Attention augmented Real-Time Isolation Forest Intrusion Detection System—0
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network—0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems—0
ECU Identification using Neural Network Classification and Hyperparameter Tuning—0
Are We There Yet? Unraveling the State-of-the-Art Graph Network Intrusion Detection Systems—0
A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks—0
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges—0
Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid—0
A Review of Machine Learning based Anomaly Detection Techniques—0
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs—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