HIGHLIGHTS
- who: Yair Meidan and collaborators from the Centroid distance have published the paper: CADeSH: Collaborative Anomaly Detection for Smart Homes, in the Journal: (JOURNAL)
- what: To overcome this the authors propose a two-step collaborative anomaly detection method which first uses an autoencoder to differentiate frequent (‘benign') and infrequent (possibly ‘malicious') traffic flows. To address these shortcomings, in this research the authors propose incorporating three enhancements for anomaly detection, especially when it is used for IoT-related attack detection in smart homes. The third enhancement the authors propose is fine-tuning and extending the set . . .
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