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Linear SVM-Based Android Malware Detection for Reliable IoT Services

Author

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  • Hyo-Sik Ham
  • Hwan-Hee Kim
  • Myung-Sup Kim
  • Mi-Jung Choi

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.

Suggested Citation

  • Hyo-Sik Ham & Hwan-Hee Kim & Myung-Sup Kim & Mi-Jung Choi, 2014. "Linear SVM-Based Android Malware Detection for Reliable IoT Services," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-10, September.
  • Handle: RePEc:hin:jnljam:594501
    DOI: 10.1155/2014/594501
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    Cited by:

    1. Ritika Raj Krishna & Aanchal Priyadarshini & Amitkumar V. Jha & Bhargav Appasani & Avireni Srinivasulu & Nicu Bizon, 2021. "State-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions," Sustainability, MDPI, vol. 13(16), pages 1-46, August.
    2. Mohammed Talal & A. A. Zaidan & B. B. Zaidan & O. S. Albahri & M. A. Alsalem & A. S. Albahri & A. H. Alamoodi & M. L. M. Kiah & F. M. Jumaah & Mussab Alaa, 2019. "Comprehensive review and analysis of anti-malware apps for smartphones," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(2), pages 285-337, October.
    3. Sharfah Ratibah Tuan Mat & Mohd Faizal Ab Razak & Mohd Nizam Mohmad Kahar & Juliza Mohamad Arif & Salwana Mohamad & Ahmad Firdaus, 2021. "Towards a systematic description of the field using bibliometric analysis: malware evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2013-2055, March.
    4. Iqbal H. Sarker, 2023. "Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects," Annals of Data Science, Springer, vol. 10(6), pages 1473-1498, December.
    5. Sherif El-Gendy & Mahmoud Said Elsayed & Anca Jurcut & Marianne A. Azer, 2023. "Privacy Preservation Using Machine Learning in the Internet of Things," Mathematics, MDPI, vol. 11(16), pages 1-35, August.

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