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 321–330 of 800 papers

TitleStatusHype
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—0
Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks—0
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems—0
Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems—0
DI-NIDS: Domain Invariant Network Intrusion Detection System—0
Enhancing sensor attack detection in supervisory control systems modeled by probabilistic automata—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection—0
Ensemble learning techniques for intrusion detection system in the context of cybersecurity—0
A review of Federated Learning in Intrusion Detection Systems for IoT—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