Android Malicious Apps Detection and Notification to Prevent Malware Using New Framework

Authors(2) :-Rohit Sarjerao Raut, Nishita Nitesh Patil

The attractiveness and openness of android makes markets targets for malware attacks and causes number of malware instances original hidden behind the large number of applications that seriously harmful to user privacy and security. Due to the popularity of android operating system and use of internet, android application developers are attracted towards cyber crime. For example, any person sends message to another person using internet to install particular application and that could be malicious. Malware is employed intentionally to cause harm to system by gaining confidential information from the device and modifying file contents. To prevent user privacy and provide security to user data by notifying them about malicious applications SVM with a linear classifier is used to differentiate between benign and malicious applications. For feature extraction and selection Fest tool will be used.

Authors and Affiliations

Rohit Sarjerao Raut
ME Student, Dept. of CSE, Ashokrao Mane Group of Institution vathar tarf vadgaon, Kolhapur, India
Nishita Nitesh Patil
Assistant Professor, Dept. of CSE, Ashokrao Mane Group of Institution vathar tarf vadgaon, Kolhapur, India

SVM, Malicious, feature extraction, Fest.

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Publication Details

Published in : Volume 3 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 480-483
Manuscript Number : IJSRSET1733123
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Rohit Sarjerao Raut, Nishita Nitesh Patil, " Android Malicious Apps Detection and Notification to Prevent Malware Using New Framework, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 3, pp.480-483, May-June-2017.
Journal URL : http://ijsrset.com/IJSRSET1733123

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