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Android Malware Detection Using Mobile Traffic Classification

Credit to Download: 1 | Page Numbers 9 | Abstract Views: 185
Year: 2016
COI code: ITCC03_031
Paper Language: English

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Authors Android Malware Detection Using Mobile Traffic Classification

Masoumeh Ghasemi - Department of Information Technology, Electronic Branch, Islamic Azad University (IAU), Tehran, Iran
Arash Habibi Lashkari - Department of Information Technology, Electronic Branch, Islamic Azad University (IAU), Tehran, Iran


Due to the dramatically growing development of mobile technology and its applications in all daily activities, especially banking and e-commerce, without a doubt mobile malware is one of the most serious and common threats in the electronic world. These threats are using smart techniques and are able to damage users’ data and communications. In the wake of rapidly increasing use of smartphones and boundless its applications, this research is focusing on different family of Android malware by analysis their generated traffic. Two different machine learning algorithms and feature selection techniques have been selected to find the best set of features for detecting the malware traffics. Two feature selection algorithms namely SubsetEval and Infogain have been used for reducing number of features and then two machine learning algorithms KNN and J.84 have been selected for analyses and calculate the accuracy of proposed method. The main objective of this research is reducing the time and also eliminate the detection process.


malware, smartphones, classification, feature selection, machine learning algorithms, traffic analysis

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COI code: ITCC03_031

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Ghasemi, Masoumeh & Arash Habibi Lashkari, 2016, Android Malware Detection Using Mobile Traffic Classification, 3rd International Conference on Computer, Electrical and Telecommunications, تربت حيدريه, دانشگاه تربيت حيدريه, the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
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