Blockchain Based Detection of Android Malware using Ranked Permissions
Siddhant Gupta1, Siddharth Sethi2, Srishti Chaudhary3, Anshul Arora4

1Siddhant Gupta*, Discipline of Mathematics and Computing, Delhi Technological University, Delhi, India.
2Siddharth Sethi, Discipline of Mathematics and Computing, Delhi Technological University, Delhi, India.
3Srishti Chaudhary, Discipline of Mathematics and Computing, Delhi Technological University, Delhi, India.
4Anshul Arora, Discipline of Mathematics and Computing, Delhi Technological University, Delhi, India.

Manuscript received on May 21, 2021. | Revised Manuscript received on May 10, 2021. | Manuscript published on June 30, 2021. | PP: 68-75 | Volume-10 Issue-5, June 2021. | Retrieval Number:  100.1/ijeat.E25930610521 | DOI: 10.35940/ijeat.E2593.0610521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Android mobile devices are a prime target for a huge number of cyber-criminals as they aim to create malware for disrupting and damaging the servers, clients, or networks. Android malware are in the form of malicious apps, that get downloaded on mobile devices via the Play Store or third-party app markets. Such malicious apps pose serious threats like system damage, information leakage, financial loss to user, etc. Thus, predicting which apps contain malicious behavior will help in preventing malware attacks on mobile devices. Identifying Android malware has become a major challenge because of the ever-increasing number of permissions that applications ask for, to enhance the experience of the users. And most of the times, permissions and other features defined in normal and malicious apps are generally the same. In this paper, we aim to detect Android malware using machine learning, deep learning, and natural language processing techniques. To delve into the problem, we use the Android manifest files which provide us with features like permissions which become the basis for detecting Android malware. We have used the concept of information value for ranking permissions. Further, we have proposed a consensus-based blockchain framework for making more concrete predictions as blockchain have high reliability and low cost. The experimental results demonstrate that the proposed model gives the detection accuracy of 95.44% with the Random Forest classifier. This accuracy is achieved with top 45 permissions ranked according to Information Value.
Keywords: Blockchain, Intrusion Detection, Mobile Malware, Mobile Network, Mobile Security.