Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2014, Article ID 594501, 10 pages
Research Article

Linear SVM-Based Android Malware Detection for Reliable IoT Services

1Department of Computer Science, Kangwon National University, 1 Kangwondaehak-gil, Gangwon-do 200-701, Republic of Korea
2Department of Computer and Information Science, Korea University, 2511 Sejong-ro, Sejong-si 339-770, Republic of Korea

Received 31 January 2014; Accepted 22 July 2014; Published 3 September 2014

Academic Editor: Young-Sik Jeong

Copyright © 2014 Hyo-Sik Ham et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [6 citations]

The following is the list of published articles that have cited the current article.

  • Jiawei Su, Vargas Danilo Vasconcellos, Sanjiva Prasad, Sgandurra Daniele, Yaokai Feng, and Kouichi Sakurai, “Lightweight Classification of IoT Malware Based on Image Recognition,” 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), pp. 664–669, . View at Publisher · View at Google Scholar
  • R. Soundar Raja James, A. Albasir, K. Naik, M. Y. Dabbagh, P. Dash, M. Zamani, and N. Goel, “Detection of anomalous behavior of smartphones using signal processing and machine learning techniques,” 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 1–7, . View at Publisher · View at Google Scholar
  • Abdulaziz Y. Barnawi, and Ismail M. Keshta, “Energy Management in Wireless Sensor Networks Based on Naive Bayes, MLP, and SVM Classifications: A Comparative Study,” Journal of Sensors, vol. 2016, pp. 1–12, 2016. View at Publisher · View at Google Scholar
  • Paul McNeil, Sachin Shetty, Divya Guntu, and Gauree Barve, “SCREDENT: Scalable Real-time Anomalies Detection and Notification of Targeted Malware in Mobile Devices,” Procedia Computer Science, vol. 83, pp. 1219–1225, 2016. View at Publisher · View at Google Scholar
  • Aohui Wang, Ruigang Liang, Xiaokang Liu, Yingjun Zhang, Kai Chen, and Jin Li, “An Inside Look at IoT Malware,” Industrial IoT Technologies and Applications, vol. 202, pp. 176–186, 2017. View at Publisher · View at Google Scholar
  • Parwinder Kaur Dhillon, and Sheetal Kalra, “A lightweight biometrics based remote user authentication scheme for IoT services,” Journal of Information Security and Applications, 2017. View at Publisher · View at Google Scholar