Internet of Things (IoT) devices are increasingly targeted by rapidly evolving malware, yet collaborative detection remains challenged by privacy leakage, noisy and imbalanced training data, and weak integrity guarantees when sharing model updates. This paper presents Mal-Fedchain, a secure and privacy-preserving framework for image-based IoT malware detection and prevention that couples federated learning with blockchain and honeypot-assisted behavioral monitoring, targeting Linux-capable IoT gateway devices. Portable Executable (PE) binaries are transformed into grayscale images using a corrected fixed-width byte-mapping pipeline stabilized by an information-maximizing GAN (IMGAN). A bi-level preprocessing pipeline applies two-sided weighted sparse representation (T-WSR) denoising—designed to selectively suppress zero-padding artifacts, high-entropy packed regions, and sparse opcode noise while preserving discriminative section-boundary texture—followed by geometric augmentation to mitigate class imbalance. Malware detection and family attribution are performed using a residual capsule-based network (RBCN) that fuses discriminative visual representations with PE-header features via concatenation, improving robustness against polymorphism and obfuscation. A formal threat model governs three adversary classes: a semi-honest aggregation server, a bounded fraction of malicious clients (up to 30%), and a passive eavesdropper. To enable collaboration without exposing raw data, clients train locally and share only MemCbar-encrypted updates; a permissioned Hyperledger Fabric blockchain ledger records hashed updates and security events to provide integrity, traceability, and tamper resistance. A file-system-integrated honeypot captures evasive behaviors and logs auditable evidence to strengthen prevention. Experiments on the Malimg dataset across five ablation configurations demonstrate that the corrected RBCN pipeline achieves 93.52% accuracy, 92.40% precision, 93.52% recall, 92.52% F-measure, MCC of 0.9245, and AUC of 0.9976 in its centralized configuration, and 65.62% accuracy with AUC of 0.9840 in the full federated configuration with five clients and eight communication rounds, substantially outperforming all baselines across all reported metrics.
Sirhan et al. (Thu,) studied this question.