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      TinyDroid: A Lightweight and Efficient Model for Android Malware Detection and Classification

      1 , 1 , 1 , 1 , 2
      Mobile Information Systems
      Hindawi Limited

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          Abstract

          With the popularity of Android applications, Android malware has an exponential growth trend. In order to detect Android malware effectively, this paper proposes a novel lightweight static detection model, TinyDroid, using instruction simplification and machine learning technique. First, a symbol-based simplification method is proposed to abstract the opcode sequence decompiled from Android Dalvik Executable files. Then, N-gram is employed to extract features from the simplified opcode sequence, and a classifier is trained for the malware detection and classification tasks. To improve the efficiency and scalability of the proposed detection model, a compression procedure is also used to reduce features and select exemplars for the malware sample dataset. TinyDroid is compared against the state-of-the-art antivirus tools in real world using Drebin dataset. The experimental results show that TinyDroid can get a higher accuracy rate and lower false alarm rate with satisfied efficiency.

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          Most cited references15

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          Significant Permission Identification for Machine Learning Based Android Malware Detection

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            Opcode sequences as representation of executables for data-mining-based unknown malware detection

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              • Article: not found

              The Evolution of Android Malware and Android Analysis Techniques

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                Author and article information

                Journal
                Mobile Information Systems
                Mobile Information Systems
                Hindawi Limited
                1574-017X
                1875-905X
                October 17 2018
                October 17 2018
                : 2018
                : 1-9
                Affiliations
                [1 ]College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
                [2 ]College of Information Engineering, Central University of Finance and Economics, Beijing 100081, China
                Article
                10.1155/2018/4157156
                5dda9a79-4791-4dfa-971b-e1005703d81c
                © 2018

                http://creativecommons.org/licenses/by/4.0/

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