Modelling A Data Sniffing Malware Detector For Apks

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


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.


Smartphone has experienced rapid growth over the years. Android being the most popular operating system (according to, 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.


[1] Alain Pimentel (2015). Detecting android malware by using a machine learning ensemble method. c1fd9eb245d367007d.pdf
[2] ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab and Djedjiga Mouheb (2018) MalDozer: Automatic framework for android malware detection using deep learning. Digital Investigation 24 (2018) S48 - S59
[3] Fairuz Amalina Narudin, Ali Feizollah, Nor Badrul Anuar and Abdullah Gani (2014). Evaluation of machine learning classifiers for mobile malware detection. 859.pdf
[4] Hyunjae Kang, Jae-wook Jang, Aziz Mohaisen and Huy Kang Kim (2015). Detecting and classifying android malware using static analysis along with creator information. International Journal of Distributed Sensor Networks Volume 2015, Article ID 479174
[5] Iker Burguera, Urko Zurutuza and Simin Nadjm-Tehrani (2011). Behavior-based malware detection system for android. droid_Behavior- Based_Malware_Detection_System_for_Android
[6] Mahmudur Rahman, Mizanur Rahman, Bogdan Carbunar, Duen Horng Chau (2017). FairPlay: Fraud and Malware Detection in Google Play. h_Rank_Fraud_and_Malware_Detection_in_Google_Play
[7] Marko Dimjaševic, Simone Atzeni, Zvonimir Rakamaric and Ivo Ugrina (2016). Evaluation of android malware detection based on system calls. pdf/iwspa2016-daur.pdf
[8] Nguyen Viet Duc, Pham Thanh Giang and Pham Minh V (2015). Permission analysis for android malware detection. ssion_Analysis_for_Android_Malware_Detection
[9] Quan Qian, Jing Cai, Mengbo Xie and Rui Zhan (2016). Malicious Behavior Analysis For Android Application. International Journal of Network Security, Vol.18 No.1, PP.182-192, Jan. 2016
[10] Ryo Sato, Daiki Chiba and Shigeki Goto (2013). Detecting Android Malware By Analyzing Manifest Files ting_Android_Malware_by_Analyzing_Manifest_Files
[11] Sanya Chaba, Rahul Kumar, Rohan Pant, Mayank Dave (2018). Malware Detection Approach For Android Systems Using System Call Logs
[12] Shuang Liang, Xiaojiang Du, Chiu C. Tan, Wei Yu (2016). An effective online scheme for detecting android malware. International Journal of Distributed Sensor Networks Volume 2015, Article ID 479174
[13] Suleiman Y. Yerima, Sakir Sezer and Igor Muttik (2014).0020Android Malware Detection Using Parallel Machine Learning Classifiers. 8th International Conference on Next Generation Mobile Applications, Services and Technologies, (NGMAST 2014), 10-14 Sept., 2014
[14] Suleiman Y. Yerima, Sakir Sezer and Igor Muttik (2015). High Accuracy Android Malware Detection Using Ensemble Learning. _Accuracy_Android_Malware_Detection_Using_Ensemble _Learning
[15] Yonghong Huang, Utkarsh Verma, Celeste Fralick, Gabriel Infante-Lopez, Brajesh Kumar and Carl Woodward (2019). Malware Evasion Attack and Defense.
[16] mobile/worldwide

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