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Found 3 papers, 1 papers with code
Graph Neural Networks for Jamming Source Localization
Graph-based learning has emerged as a transformative approach for modeling complex relationships across diverse domains, yet its potential in wireless security remains largely unexplored.
Learning Compact and Robust Representations for Anomaly Detection
We address these limitations by proposing a contrastive pretext task for anomaly detection that enforces three key properties: (1) compact ID clustering to reduce intra-class variance, (2) inlier-outlier separation to enhance inter-class separation, and (3) outlier-outlier separation to maintain diversity among synthetic outliers and prevent representation collapse.
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection
The effectiveness of network intrusion detection systems, predominantly based on machine learning, are highly influenced by the dataset they are trained on.
Ranked #1 on
Anomaly Detection
on TII-SSRC-23
