AUTHORS: Komal Naranga, Kingkarn Sookhanaphibarna, and Prasong Praneetpolgrangb

ABSTRACT: This research presents a model for malware detection on mobile operating system based on analyzing the operation codes. The research processes are as follows: (1) collecting of both malicious and benign codes on android operating system, (2) extracting features based on the distribution of n-grams frequency where the parameter n = 3 is used, and (3) constructing a model for classification the malicious codes using the extracted features for both malicious and benign codes. In the experiment, 304 malicious codes and 553 benign codes were using to construct the model. The experiment shows that the model achieved more than 85.52% accuracy. For the sensitivity and specificity, the model achieved 71.26% and 90.52%, respectively.

Keywords: Feature extraction, Term Frequency, Malicious code detection, Support Vector Machine



MLA   Narang, Komal, Kingkarn Sookhanaphibarn, and Prasong Praneetpolgrang. "Android Antivirus Scanner by Analyzing Operation Codes." Applied Mechanics and Materials. Vol. 781. Trans Tech Publications, 2015.
APA Narang, K., Sookhanaphibarn, K., & Praneetpolgrang, P. (2015). Android Antivirus Scanner by Analyzing Operation Codes. In Applied Mechanics and Materials (Vol. 781, pp. 145-148). Trans Tech Publications.
ISO 690   NARANG, Komal; SOOKHANAPHIBARN, Kingkarn; PRANEETPOLGRANG, Prasong. Android Antivirus Scanner by Analyzing Operation Codes. In: Applied Mechanics and Materials. Trans Tech Publications, 2015. p. 145-148.