isrjournals
User Sign in    Editor Sign in

data-driven malware detection system for android phones

D. JENIFER GRACE,P. SWETHA,K. ANU ME.,

Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology

ISSN: 2321-3337          Impact Factor:1.521         Volume:6         Issue:3         Year: 09 April,2021         Pages:1575-1580

International Journal of Advanced Research in Computer Science Engineering and Information Technology

Abstract

Permission-based security model of Android restricts applications to access specific resources, but malicious applications can invade more easily in such user-centric pattern. Through the analysis of the Android Permission-based security model and the permission features of Android applications, we establish the permission model to quantify the functional characteristics of the application, and then provide an assessment method in which we use the network visualization techniques and clustering algorithm to determine whether the testing application is potentially malicious application or not so as to help users choose applications before installation. Security testing is known to be a notoriously difficult activity. This is partly because unlike functional testing that aims to show a software system complies with its specification, security testing is a form of negative testing, i.e., showing that a certain behaviour does not exist in the system.

Kewords

Android

Reference

[1]: Hajredin Daku, Pavol Zavarsky, Yasir Malik, “Behavioral-Based Classification and Identification of Ransomware Variants Using Machine Learning”, 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp 1-5, 2018. [2]: Olga Hachinyan, “Detection of Malicious Software on Based on Multiple Equations of API-calls Sequences”, 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp 1-4, 2017. [3]: Krzysztof Cabaj, Piotr Gawkowski, Konrad Grochowski, Alexis Nowikowski, Piotr ̇ Zórawski, “The impact of malware evolution on the analysis methods and infrastructure”, 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp 1-4, 2017. [4]: B. Fisseha Demissie, D. Ghio, M. Ceccato, A. AvanciniIdentifying Android Inter-App Communication Vulnerabilities Using Static and Dynamic Analysis, IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft), 2016.