Date of Award
8-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering (Holcomb Dept. of)
Committee Chair/Advisor
Dr. Fatemeh Afghah
Committee Member
Dr. Richard Brooks
Committee Member
Dr. Yongkai Wu
Abstract
With the rapid growth of Internet of Things (IoT) devices across various sectors, detecting anomalies in such systems has become increasingly challenging. IoT environments produce diverse and evolving data streams, often lacking labeled examples, which limits the effectiveness of traditional machine learning models. These models typically require frequent retraining and struggle to adapt to new deployment conditions. This thesis proposes a flexible, privacy-aware framework for anomaly detection in multivariate time series data generated by heterogeneous IoT systems. The approach integrates a long short-term memory variational autoencoder (LSTM-VAE) with contrastive learning and adversarial adaptation, enabling the model to generalize across domains, even in few-shot or zero-shot situations where labeled target data is unavailable. A central contribution of this work is the use of domain-specific adapter layers that allow the model to adapt to new environments without needing access to the raw target data, thus preserving privacy. Additionally, the framework segments traffic based on destination IP addresses instead of fixed time windows, retaining communication context and enhancing the relevance of input sequences. The proposed method is evaluated using real-world IoT datasets from industrial, civilian, and military settings. Results demonstrate improved anomaly detection accuracy and strong cross-domain adaptability. Overall, this thesis introduces an effective, scalable, and privacy-respecting solution for securing dynamic IoT networks, capable of addressing the limitations of conventional methods and defending against emerging cyber threats.
Recommended Citation
Rezakhani, Mahshid, "A Generalizable and Privacy-Preserving Framework for Anomaly Detection in Heterogeneous IoT Environments" (2025). All Theses. 4575.
https://open.clemson.edu/all_theses/4575