A Knowledge Transfer-based Semi-Supervised Federated Learning for IoT Malware Detection
Document Type
Article
Publication Date
5-10-2022
Department
Department of Electrical and Computer Engineering
Abstract
As the demand for Internet of Things (IoT) technologies continues to grow, IoT devices have been viable targets for malware infections. Although deep learning-based malware detection has achieved great success, the detection models are usually trained based on the collected user records, thereby leading to significant privacy risks. One promising solution is to leverage federated learning (FL) to enable distributed on-device training without centralizing the private user records. However, it is non-trivial for IoT users to label these records, where the quality and the trustworthiness of data labeling are hard to guarantee. To address the above issues, this paper develops a semi-supervised federated IoT malware detection framework based on knowledge transfer technologies, named by FedMalDE. Specifically, FedMalDE explores the underlying correlation between labeled and unlabeled records to infer labels towards unlabeled samples by the knowledge transfer mechanism. Moreover, a specially designed subgraph aggregated capsule network (SACN) is used to efficiently capture varied malicious behaviors. The extensive experiments conducted on real-world data demonstrate the effectiveness of FedMalDE in detecting IoT malware and its sufficient privacy and robustness guarantee.
Publication Title
IEEE Transactions on Dependable and Secure Computing
Recommended Citation
Pei, X.,
Deng, X.,
Tian, S.,
Zhang, L.,
&
Xue, K.
(2022).
A Knowledge Transfer-based Semi-Supervised Federated Learning for IoT Malware Detection.
IEEE Transactions on Dependable and Secure Computing.
http://doi.org/10.1109/TDSC.2022.3173664
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16155