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      Linear SVM-Based Android Malware Detection for Reliable IoT Services

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      Journal of Applied Mathematics
      Hindawi Limited

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          Abstract

          Current many Internet of Things (IoT) services are monitored and controlled through smartphone applications. By combining IoT with smartphones, many convenient IoT services have been provided to users. However, there are adverse underlying effects in such services including invasion of privacy and information leakage. In most cases, mobile devices have become cluttered with important personal user information as various services and contents are provided through them. Accordingly, attackers are expanding the scope of their attacks beyond the existing PC and Internet environment into mobile devices. In this paper, we apply a linear support vector machine (SVM) to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.

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          The Internet of things.

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            “Andromaly”: a behavioral malware detection framework for android devices

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              An Overview of Privacy and Security Issues in the Internet of Things

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                Author and article information

                Journal
                Journal of Applied Mathematics
                Journal of Applied Mathematics
                Hindawi Limited
                1110-757X
                1687-0042
                2014
                2014
                : 2014
                :
                : 1-10
                Article
                10.1155/2014/594501
                56895d98-14de-46f3-96c0-2fb2cf7c5d8e
                © 2014

                http://creativecommons.org/licenses/by/3.0/

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