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1. A Knowledge Transfer
Supervised Federated Learning for IoT
Malware Detection
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 base
leading to significant privacy risks. One promising solution is to leverage
federated learning (FL) to enable distributed on
centralizing the private user records. However, it is non
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,
A Knowledge Transfer-Based Semi
Supervised Federated Learning for IoT
Malware Detection
As the demand for Internet of Things (IoT) technologies continues to grow, IoT
devices have been viable targets for malware infections. Although deep
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
supervised federated IoT malware detection framework based on
technologies, named by FedMalDE. Specifically,
Based Semi-
Supervised Federated Learning for IoT
As the demand for Internet of Things (IoT) technologies continues to grow, IoT
devices have been viable targets for malware infections. Although deep
based malware detection has achieved great success, the detection
d on the collected user records, thereby
leading to significant privacy risks. One promising solution is to leverage
device training without
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
supervised federated IoT malware detection framework based on
technologies, named by FedMalDE. Specifically,

2. 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.