IoTMalware: Android IoT Malware Detection based on Deep Neural Network and Blockchain Technology

by   Rajesh Kumar, et al.

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.


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