Federated Contrastive Learning Framework for Proactive Malware Signature Generation in IoT Enabled Edge Environments

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Abstract
Malware signatures have been generated traditionally and passively, heavily relying on centralized training and post Infection analysis, delaying containment, and unable to generalize across the heterogeneous IoT ecosystems. Thus, we present a privacy-preserving federated contrastive learning framework to preemptive malware signature generation for IoT-driven edge environments. It entails five innovative analytical methods: Federated Adversarial Contrastive Distillation (FACD) to boost resilience against polymorphic variants; Federated Temporal Evolution Contrastive Graphs (F-TECG) to capture dynamics of malware propagation; Federated Multi-Modal Contrastive Fusion (F-MMCF) to unify traffic, binary, and memory features into an all-purpose embedding space; Federated Contrastive Reinforcement Malware Anticipation (F-CRMA), which anticipates malwares' evolving attack states; Federated Contrastive Meta-Learning for Zero-Day Malware (F-CMZ), achieving rapid few-shot adaptation. Each embeds a firm in a unified data flow, where the raw IoT traffic and malware traces are gradually converted into resilient and anticipatory, and temporally aware embeddings before being collected by MEC nodes. The proposed architecture solves, at the same time, durability, early detection latency, modality generalization, proactive anticipation, and zero-day resilience. Such experimental analysis shows improvements from the ground up, including an adversarial robustness gain of 22%, a reduction of 35% detection latency, and a 25% increase in zero-day accuracy. This work incubates a foundation on proactive malware defense, given federated, scalable, and pre-emptive protection mechanisms for the IoT-centric edge infrastructures being developed in future sets.

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