Modelling A Data Sniffing Malware Detector For Apks

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2019 by IJCOT Journal
Volume - 9 Issue - 6
Year of Publication : 2019
Authors : Oyinloye Oghenerukevwe Elohor, Olatomide Awoyomi
DOI : 10.14445/22492593/IJCOT-V9I6P301

Citation

MLA Style:Oyinloye Oghenerukevwe Elohor, Olatomide Awoyomi "Modelling A Data Sniffing Malware Detector For Apks" International Journal of Computer and Organization Trends 9.6 (2019): 1-8. 

APA Style:Oyinloye Oghenerukevwe Elohor, Olatomide Awoyomi(2019). Modelling A Data Sniffing Malware Detector For Apks. International Journal of Computer and Organization Trends, 9(6), 1-8.

Abstract

Smartphone has experienced rapid growth over the years. Android being the most popular operating system (according to https://gs.statcounter.com, 2019 constituting 76.24% of the statistics of the entire mobile usage) has witnessed a dramatic increase in malwares targeted at the platform as malware creators leverage on its popularity to exhibit malicious activity. As such, Android app marketplaces (googleplay and other third parties) remain at risk of hosting malicious apps that could be dangerous to the users. This calls for a need to pay attention to security issue in order to ensure that users can use their desired application without having a fallback on their privacy or any other means that attackers use hence, in this paper we present an effective approach to alleviate this problem based on machine learning approach using Linear Regression and Support Vector Machine (SVM) for classification. The models was trained with 70% and tested with 30% of the collected dataset and results of experiments are presented to demonstrate the effectiveness of the proposed approach nailing 85.7% accuracy.

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Keywords
Malware, Android, Machine Learning, Linear Regression, Support Vector Machine (SVM).