Paper Title : SIGNIFICANT PERMISSION IDENTICATION FOR ANDRIOD MALWARE DETECTION

ISSN : 2394-2231
Year of Publication : 2022

10.5281/zenodo.6409997

Authors: Mr.S.Sambasivam,M.C.A..,M.Phil.., V.Santhosh Kumar

         



MLA Style: SIGNIFICANT PERMISSION IDENTICATION FOR ANDRIOD MALWARE DETECTION " Mr.S.Sambasivam,M.C.A..,M.Phil.., V.Santhosh Kumar " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: SIGNIFICANT PERMISSION IDENTICATION FOR ANDRIOD MALWARE DETECTION " Mr.S.Sambasivam,M.C.A..,M.Phil.., V.Santhosh Kumar " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
The global pervasiveness of smartphones has prompted the development of millions of free and commercially available applications. These applications allow users to perform various activities, such as communicating, gaming, and completing financial and educational tasks. These commonly used devices often store sensitive private information and, consequently, have been increasingly targeted by harmful malicious software. The alarming growth rate of malicious apps has become a serious issue that sets back the prosperous mobile ecosystem. A recent report indicates that a new malicious app for Android is introduced every 10 s. Android allows users to install applications from unverified sources such as third-party app stores and file-sharing websites. The malware infection issue has been so serious that a recent report indicates that 97% of all mobile malware target Android devices to combat this serious malware campaign, we need a scalable malware detection approach that can effectively and efficiently identify malware apps. Numerous malware detection tools have been developed, including system-level and network level approaches. However, scaling the detection for a large bundle of apps remains a challenging task. This project introduces Significant Permission IDentification (SigPID), a malware detection system based on permission usage analysis to cope with the rapid increase in the number of Android malware. Instead of extracting and analyzing all Android permissions, this project develop three levels of pruning by mining the permission data to identify the most significant permissions that can be effective in distinguishing between benign and malicious apps. SigPID then utilizes machine-learningbased classification methods to classify different families of malware and benign apps. This project identifies dangerous permission list, benign permission list and reduce nonsensitive permissions and apply SVM classification on the new data set. The project is designed using R Studio. The coding language used is R.

Reference
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Keywords
— SIGNIFICANT PERMISSION IDENTICATION FOR ANDRIOD MALWARE DETECTION