IoT Phantom-Delay Attacks: Demystifying and Exploiting IoT Timeout Behaviors

Fu, Chenglong; Zeng, Qiang; Chi, Haotian; Du, Xiaojiang; Valluru, Siva Likitha

doi:10.1109/DSN53405.2022.00050

Abstract The use of IoT devices has significantly increased in recent years, but there have been growing concerns about the security and privacy issues associated with these IoT devices. A recent trend is to use deep network models to classify attack and benign traffic. A traditional approach is to train the models using centrally stored data collected from all the devices in the network. However, this framework raises concerns around data privacy and security. Attacks on the central server can compromise the data and expose sensitive information. To address the issues of data privacy and security, federated learning is now a widely studied solution in the research community. In this paper, we explore and implement federated learning techniques to detect attack traffic in the IoT network. We use Deep Neural Networks on the labeled dataset and Autoencoder on the unlabeled dataset in a federated framework. We implement different model aggregation algorithms such as FedSGD, FedAvg, and FedProx for federated learning. We compare the performance of these federated learning models with the models in a centralized framework and study which aggregation algorithm for the global model yields the best performance for detecting attack traffic in the IoT network.

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