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
Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes—0
DI-NIDS: Domain Invariant Network Intrusion Detection System—0
Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges—0
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks—0
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—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