Lightweight Classification of IoT Malware Based on Image Recognition

Jiawei Su, Vargas Danilo Vasconcellos, Sanjiva Prasad, Sgandurra Daniele, Yaokai Feng, Kouichi Sakurai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Citations (Scopus)

Abstract

The Internet of Things (IoT) is an extension of the traditional Internet, which allows a very large number of smart devices, such as home appliances, network cameras, sensors and controllers to connect to one another to share information and improve user experiences. IoT devices are micro-computers for domain-specific computations rather than traditional functionspecific embedded devices. This opens the possibility of seeing many kinds of existing attacks, traditionally targeted at the Internet, also directed at IoT devices. As shown by recent events, such as the Mirai and Brickerbot botnets, DDoS attacks have become very common in IoT environments as these lack basic security monitoring and protection mechanisms. In this paper, we propose a novel light-weight approach for detecting DDos malware in IoT environments. We extract the malware images (i.e., a one-channel gray-scale image converted from a malware binary) and utilize a light-weight convolutional neural network for classifying their families. The experimental results show that the proposed system can achieve 94:0% accuracy for the classification of goodware and DDoS malware, and 81:8% accuracy for the classification of goodware and two main malware families.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
EditorsClaudio Demartini, Sorel Reisman, Ling Liu, Edmundo Tovar, Hiroki Takakura, Ji-Jiang Yang, Chung-Horng Lung, Sheikh Iqbal Ahamed, Kamrul Hasan, Thomas Conte, Motonori Nakamura, Zhiyong Zhang, Toyokazu Akiyama, William Claycomb, Stelvio Cimato
PublisherIEEE Computer Society
Pages664-669
Number of pages6
ISBN (Electronic)9781538626665
DOIs
Publication statusPublished - Jun 8 2018
Event42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan
Duration: Jul 23 2018Jul 27 2018

Publication series

NameProceedings - International Computer Software and Applications Conference
Volume2
ISSN (Print)0730-3157

Other

Other42nd IEEE Computer Software and Applications Conference, COMPSAC 2018
CountryJapan
CityTokyo
Period7/23/187/27/18

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications

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  • Cite this

    Su, J., Danilo Vasconcellos, V., Prasad, S., Daniele, S., Feng, Y., & Sakurai, K. (2018). Lightweight Classification of IoT Malware Based on Image Recognition. In C. Demartini, S. Reisman, L. Liu, E. Tovar, H. Takakura, J-J. Yang, C-H. Lung, S. I. Ahamed, K. Hasan, T. Conte, M. Nakamura, Z. Zhang, T. Akiyama, W. Claycomb, & S. Cimato (Eds.), Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018 (pp. 664-669). [8377943] (Proceedings - International Computer Software and Applications Conference; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2018.10315