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This thesis reviews semi-supervised learning (SSL) methods for anomaly detection in encrypted IoT traffic, focusing on the challenges posed by imbalanced datasets. It highlights a shift from traditional detection methods to behavior-based approaches that leverage metadata and temporal patterns, emphasizing the importance of effective feature extraction strategies. The research identifies gaps in existing literature, such as inconsistent evaluation metrics and the need for standardized benchmarks, while sugg

Author

Aloukik Arora

Language

EN