Abstract
Internet of Things (IoT) is becoming an integral part of our homes today. Internet-connected devices, such as smart speakers, smart bulbs, and security cameras are improving our convenience and security. With the growth in smart environments, there is an increasing concern over the security and privacy issues related to IoT devices. The issue of the IoT security has received considerable attention due to (1) the intrinsic technological constraints of IoT devices (computing and storage limitations) and (2) its prevalence in people’s life’s, in close proximity. IoT devices can be easily compromised (much easier than PCs and/or smart phones) and can be utilized for generating botnet attacks. In this paper, we propose an Artificial Intelligence (AI) based solution for malicious traffic detection. We explore the accuracy of Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) learning algorithm in detecting botnet traffic from IoT devices infected by two major IoT botnets, namely, Mirai and Bashlite (also known as Gafgyt). After tuning and optimization, the MLP-ANN algorithm achieved an accuracy rate of 100% in the testing phase of IoT botnet traffic classification.
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Antonakakis, M., April, T., Bailey, M., Bernhard, M., Bursztein, E., Cochran, J., Durumeric, Z., Halderman, J.A., Invernizzi, L., Kallitsis, M., et al.: Understanding the Mirai botnet. In: USENIX Security Symposium, pp. 1092–1110 (2017)
Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron)–a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998)
Jain, A.K., Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 29(3), 31–44 (1996)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Scikit Learn: Neural network models documentation (supervised learning). https://scikit-learn.org/stable/modules/neural_networks_supervised.html
Marzano, A., Alexander, D., Fonseca, O., Fazzion, E., Hoepers, C., Steding-Jessen, K., Chaves, M.H., Cunha, Í., Guedes, D., Meira, W.: The evolution of Bashlite and Mirai IoT botnets. In: 2018 IEEE Symposium on Computers and Communications (ISCC), pp. 00813–00818. IEEE (2018)
Meidan, Y., et al.: N-BaIoT–network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 17(3), 12–22 (2018)
Midi, D., Rullo, A., Mudgerikar, A., Bertino, E.: Kalis—a system for knowledge-driven adaptable intrusion detection for the Internet of Things. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 656–666. IEEE (2017)
Ng, A.: What data scientists should know about deep learning. https://goo.gl/nJqHsP (2015)
Nielsen, M.A.: Neural Networks and Deep Learning, vol. 25. Determination Press, USA (2015)
Oliveira, T.P., Barbar, J.S., Soares, A.S.: Multilayer perceptron and stacked autoencoder for internet traffic prediction. In: IFIP International Conference on Network and Parallel Computing, pp. 61–71. Springer (2014)
Symantec Security Response: Mirai: what you need to know about the botnet behind recent major DDoS attacks. https://www.symantec.com/connect/blogs/mirai-what-you-need-know-about-botnet-behind-recent-major-ddos-attacks (2016)
Sedjelmaci, H., Senouci, S.M., Al-Bahri, M.: A lightweight anomaly detection technique for low-resource IoT devices: a game-theoretic methodology. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2016)
Summerville, D.H., Zach, K.M., Chen, Y.: Ultra-lightweight deep packet anomaly detection for internet of things devices. In: 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC), pp. 1–8. IEEE (2015)
Tuor, A., Kaplan, S., Hutchinson, B., Nichols, N., Robinson, S.: Deep learning for unsupervised insider threat detection in structured cybersecurity data streams. arXiv preprint arXiv:1710.00811 (2017)
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Javed, Y., Rajabi, N. (2020). Multi-Layer Perceptron Artificial Neural Network Based IoT Botnet Traffic Classification. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1069. Springer, Cham. https://doi.org/10.1007/978-3-030-32520-6_69
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DOI: https://doi.org/10.1007/978-3-030-32520-6_69
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