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Android malware identification using machine learning techniques

Credit to Download: 1 | Page Numbers 14 | Abstract Views: 45
Year: 2020
COI code: RSETCONF02_014
Paper Language: English

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Authors Android malware identification using machine learning techniques

  Masoomeh Beitsayahi - Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran


Today, due to the increase in malware and the level of access to information onmobile phones, it is necessary to identify the types of malware. In this research we try toidentify Android malware, at first collected 1804 unsupervised data from Google Play andthird-party markets. And after initial reverse engineering and decompiling them and identifyingdangerous permissions and collecting suspicious data from them, then we have begun tocategorize them. And after thoroughly examining them and identifying a few batches of databotnets, they became supervised to implement machine learning algorithms on them as labels.At the data collection stage, all categories of Google Play data were extracted. Finally, due tothe different number of labels(not balance) in each data set, and in order to compare the workwith previous tasks, the accuracy of their work and then the kappa criterion for work evaluationwere considered and the results reported.


android malware_ machine learning_ botnet detection_ malware detection

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COI code: RSETCONF02_014

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Beitsayahi, Masoomeh, 2020, Android malware identification using machine learning techniques, 2nd International Conference on Advanced Research in Science, Engineering and Technology, تهران, شركت همايش آروين البرز, 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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The University/Research Center Information:
Type: Azad University
Paper No.: 3481
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.

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