5518543
doi
10.35940/ijeat.E2593.0610521
oai:zenodo.org:5518543
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Publisher
Siddharth Sethi
Discipline of Mathematics and Computing, Delhi Technological University, Delhi, India,
Srishti Chaudhary
Discipline of Mathematics and Computing, Delhi Technological University, Delhi, India,
Anshul Arora
Discipline of Mathematics and Computing, Delhi Technological University, Delhi, India,
Blockchain Based Detection of Android Malware using Ranked Permissions
Siddhant Gupta
Discipline of Mathematics and Computing, Delhi Technological University, Delhi, India,
issn:2249-8958
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Blockchain, Intrusion Detection, Mobile Malware, Mobile Network, Mobile Security.
<p>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.</p>
Zenodo
2021-06-30
info:eu-repo/semantics/article
5518542
1632232106.507708
527845
md5:7eaa50e535d22ea90ed17f0234d212be
https://zenodo.org/records/5518543/files/E25930610521.pdf
public
2249-8958
Is cited by
issn
International Journal of Engineering and Advanced Technology (IJEAT)
10
5
68-75
2021-06-30