Abstract
Internet of Things (IoT) devices are typically resource constrained micro-computers for domain-specific computations. Most of them use low-cost embedded system that lacked basic security monitoring and protection mechanisms. Consequently, IoT-specific malwares are made to target at these vulnerable devices for deep infection and utilization, such as Mirai and Brickerbot, which poses tremendous threats to the security of IoT. In this issue, we present a novel approach for detecting malware in IoT environments. The proposed method firstly extract one-channel gray-scale image sequence that converted from the disassembled malware binaries. Then we utilize a Two-Bits Convolutional Neural Network (TBN) for detecting IoT malware families, which can encode the network edge weights with two bits. Experimental results conducted on the collected dataset show that our approach can reduce the memory usage and improve computational efficiency significantly while achieving a considerable performance in terms of malware detection accuracy.
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References
Moser, A., Kruegel, C., Kirda, E.: Exploring multiple execution paths for malware analysis. In: Proceedings of the IEEE Symposium on Security and Privacy (2007)
Anderson, B., Storlie, C., Yates, M., Mcphall, A.: Automating reverse engineering with machine learning techniques (2014)
Ahmadi, M., Giacinto, G., Ulyanov, D., Semenov, S., Trofimov, M.: Novel feature extraction, selection and fusion for effective malware family classification. In: ACM Conference on Data and Application Security and Privacy (2016)
Su, J., Vargas, D.V., Prasad, S., Sgandurra, D., Feng, Y., Sakurai, K.: Lightweight classification of IOT malware based on image recognition (2018)
Zhang, J., Zheng, Q., Hui, Y., Lu, O., Hu, Y.: Malware variant detection using opcode image recognition with small training sets. In: International Conference on Computer Communication and Networks (2016)
Liu, L., Wang, B.: Malware classification using gray-scale images and ensemble learning. In: International Conference on Systems and Informatics (2017)
Han, K.S., Lim, J.H., Kang, B., Im, E.G.: Malware analysis using visualized images and entropy graphs. Int. J. Inf. Secur. 14(1), 1–14 (2015)
Nataraj, L., Yegneswaran, V., Porras, P., Jian, Z.: A comparative assessment of malware classification using binary texture analysis and dynamic analysis. In: ACM Workshop on Security and Artificial Intelligence (2011)
Nataraj, L., Karthikeyan, S., Jacob, G., Manjunath, B.S.: Malware images: visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security (2011)
Kirat, D., Nataraj, L., Vigna, G., Manjunath, B.S.: Sigmal: a static signal processing based malware triage. In: Computer Security Applications Conference (2013)
Raff, E., Barker, J., Sylvester, J., Brandon, R., Nicholas, C.: Malware detection by eating a whole EXE (2017)
Yue, S.: Imbalanced malware images classification: a CNN based approach (2017)
Acknowledgement
This work was partially supported by the National Key R&D Program of China (2018YFC1201102), National Natural Science Foundation of China (61802016, U1636120), China Postdoctoral Science Foundation (2018M641198), and the National Social Science Foundation of China (17ZDA331).
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Wen, H., Zhang, W., Hu, Y., Hu, Q., Zhu, H., Sun, L. (2019). Lightweight IoT Malware Visualization Analysis via Two-Bits Networks. In: Biagioni, E., Zheng, Y., Cheng, S. (eds) Wireless Algorithms, Systems, and Applications. WASA 2019. Lecture Notes in Computer Science(), vol 11604. Springer, Cham. https://doi.org/10.1007/978-3-030-23597-0_51
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DOI: https://doi.org/10.1007/978-3-030-23597-0_51
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