Android Malware Detection Using Auto-Regressive Moving-Average Model 


Vol. 40,  No. 8, pp. 1551-1559, Aug.  2015


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  Abstract

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.

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  Cite this article

[IEEE Style]

H. Kim and M. Choi, "Android Malware Detection Using Auto-Regressive Moving-Average Model," The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 8, pp. 1551-1559, 2015. DOI: .

[ACM Style]

Hwan-Hee Kim and Mi-Jung Choi. 2015. Android Malware Detection Using Auto-Regressive Moving-Average Model. The Journal of Korean Institute of Communications and Information Sciences, 40, 8, (2015), 1551-1559. DOI: .

[KICS Style]

Hwan-Hee Kim and Mi-Jung Choi, "Android Malware Detection Using Auto-Regressive Moving-Average Model," The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 8, pp. 1551-1559, 8. 2015.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880