TY - GEN
T1 - Lightweight Classification of IoT Malware Based on Image Recognition
AU - Su, Jiawei
AU - Danilo Vasconcellos, Vargas
AU - Prasad, Sanjiva
AU - Daniele, Sgandurra
AU - Feng, Yaokai
AU - Sakurai, Kouichi
N1 - Funding Information:
This research was partially supported by Collaboration Hubs for International Program (CHIRP) of SICORP, Japan Science and Technology Agency (JST), and Project of security in the IoT space funding by Department of Science and Technology (DST), India.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - 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.
AB - 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.
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U2 - 10.1109/COMPSAC.2018.10315
DO - 10.1109/COMPSAC.2018.10315
M3 - Conference contribution
AN - SCOPUS:85055577661
T3 - Proceedings - International Computer Software and Applications Conference
SP - 664
EP - 669
BT - Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
A2 - Demartini, Claudio
A2 - Reisman, Sorel
A2 - Liu, Ling
A2 - Tovar, Edmundo
A2 - Takakura, Hiroki
A2 - Yang, Ji-Jiang
A2 - Lung, Chung-Horng
A2 - Ahamed, Sheikh Iqbal
A2 - Hasan, Kamrul
A2 - Conte, Thomas
A2 - Nakamura, Motonori
A2 - Zhang, Zhiyong
A2 - Akiyama, Toyokazu
A2 - Claycomb, William
A2 - Cimato, Stelvio
PB - IEEE Computer Society
T2 - 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018
Y2 - 23 July 2018 through 27 July 2018
ER -