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Security and Communication Networks
Volume 2018, Article ID 7247095, 16 pages
https://doi.org/10.1155/2018/7247095
Research Article

Detecting Malware with an Ensemble Method Based on Deep Neural Network

Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China

Correspondence should be addressed to Yong Qi; nc.ude.utjx@yiq

Received 18 August 2017; Revised 3 December 2017; Accepted 6 February 2018; Published 12 March 2018

Academic Editor: Zonghua Zhang

Copyright © 2018 Jinpei Yan 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 [10 citations]

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

  • Jiaqi Yan, Guanhua Yan, and Dong Jin, “Classifying Malware Represented as Control Flow Graphs using Deep Graph Convolutional Neural Network,” 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 52–63, . View at Publisher · View at Google Scholar
  • Kehuan Zhang, Hui Yin, Jingfu Zou, Jixin Zhang, and Zheng Qin, “Dalvik Opcode Graph Based Android Malware Variants Detection Using Global Topology Features,” IEEE Access, vol. 6, pp. 51964–51974, 2018. View at Publisher · View at Google Scholar
  • Mert Nar, Arzu Gorgulu Kakisim, Necmettin Carkaci, Melek Nurten Yavuz, and Ibrahim Sogukpinar, “Analysis and Comparison of Opcode-based Malware Detection Approaches,” UBMK 2018 - 3rd International Conference on Computer Science and Engineering, pp. 498–503, 2018. View at Publisher · View at Google Scholar
  • Sitalakshmi Venkatraman, and Mamoun Alazab, “Use of Data Visualisation for Zero-Day Malware Detection,” Security and Communication Networks, vol. 2018, pp. 1–13, 2018. View at Publisher · View at Google Scholar
  • Danial Javaheri, Mehdi Hosseinzadeh, and Amir Masoud Rahmani, “Detection and elimination of spyware and ransomware by intercepting kernel-level system routines,” IEEE Access, vol. 6, pp. 78321–78332, 2018. View at Publisher · View at Google Scholar
  • Qian Li, Qingyuan Hu, Youshui Lu, Yue Yang, and Jingxian Cheng, “Multi-level word features based on CNN for fake news detection in cultural communication,” Personal and Ubiquitous Computing, 2019. View at Publisher · View at Google Scholar
  • S. Akarsh, Prabaharan Poornachandran, Vijay Krishna Menon, and K. P. Soman, “A Detailed Investigation and Analysis of Deep Learning Architectures and Visualization Techniques for Malware Family Identification,” Cybersecurity and Secure Information Systems, pp. 241–286, 2019. View at Publisher · View at Google Scholar
  • Dominik Pieczyński, and Czesław Jędrzejek, “Malware detection using black-box neural method,” Advances in Intelligent Systems and Computing, vol. 833, pp. 180–189, 2019. View at Publisher · View at Google Scholar
  • Di Xue, Jingmei Li, Tu Lv, Weifei Wu, and Jiaxiang Wang, “Malware Classification Using Probability Scoring and Machine Learning,” IEEE Access, vol. 7, pp. 91641–91656, 2019. View at Publisher · View at Google Scholar
  • Hoda El Merabet, and Abderrahmane Hajraoui, “A survey of malware detection techniques based on machine learning,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 1, pp. 366–373, 2019. View at Publisher · View at Google Scholar