  The Internet of Things (IoT) has been revolutionizing this 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. This paper proposes a new framework based on the blockchain and deep
learning model to provide more security for Android IoT devices. Moreover, our
framework is capable to find the malware activities in a real-time environment.
The proposed deep learning model analyzes various static and dynamic features
extracted from thousands of feature of malware and benign apps that are already
stored in blockchain distributed ledger. The multi-layer deep learning model
makes decisions by analyzing the previous data and follow some steps. Firstly,
it divides the malware feature into multiple level clusters. Secondly, it
chooses a unique deep learning model for each malware feature set or cluster.
Finally, it produces the decision by combining the results generated from all
cluster levels. Furthermore, the decisions and multiple-level clustering data
are stored in a blockchain that can be further used to train every specialized
cluster for unique data distribution. Also, a customized smart contract is
designed to detect deceptive applications through the blockchain framework. The
smart contract verifies the malicious application both during the uploading and
downloading process of Android apps on the network. Consequently, the proposed
framework provides flexibility to features for run-time security regarding
malware detection on heterogeneous IoT devices. Finally, the smart contract
helps to approve or deny to uploading and downloading harmful Android
applications.
