Evaluating Machine Learning Models on the Ethereum Blockchain for Android Malware Detection
Android, the most popular mobile operating system, with billions of active users and more than 2 million apps, has motivated advertisers, hackers, fraudsters and cyber-criminals to develop malware of all types for it. In recent years, extensive research has been conducted on malware analysis and detection for Android devices, even though Android has already implemented various security mechanisms to deal with the problem. In this paper, we developed a consortium blockchain network to evaluate various machine learning models for a given malware dataset. A reward is offered using smart contracts as an incentive to the competitors for their work by allowing them to submit solutions through training with selected machine learning models in a secure and trustworthy manner. The analysis of datasets by competitors helps various organizations in the network to enhance or boost their current malware detection or defense tools. The decentralized network provides transparency, enhances security and reduces the cost in managing all relevant data by eliminating third parties. We used DREBIN dataset in the developed framework for initial experiments and the encouraging results are presented.
Advances in Intelligent Systems and Computing
Rana, M. S.,
Sung, A. H.
(2019). Evaluating Machine Learning Models on the Ethereum Blockchain for Android Malware Detection. Advances in Intelligent Systems and Computing, 998, 446-461.
Available at: https://aquila.usm.edu/fac_pubs/16433