  The widespread significance of Android IoT devices is due to its flexibility
and hardware support features which revolutionized the digital world by
introducing exciting applications almost in all walks of daily life, such as
healthcare, smart cities, smart environments, safety, remote sensing, and many
more. Such versatile applicability gives incentive for more malware attacks. In
this paper, we propose a framework which continuously aggregates multiple user
trained models on non-overlapping data into single model. Specifically for
malware detection task, (i) we propose a novel user (local) neural network
(LNN) which trains on local distribution and (ii) then to assure the model
authenticity and quality, we propose a novel smart contract which enable
aggregation process over blokchain platform. The LNN model analyzes various
static and dynamic features of both malware and benign whereas the smart
contract verifies the malicious applications both for uploading and downloading
processes in the network using stored aggregated features of local models. In
this way, the proposed model not only improves malware detection accuracy using
decentralized model network but also model efficacy with blockchain. We
evaluate our approach with three state-of-the-art models and performed deep
analyses of extracted features of the relative model.
