Abstract
Internet of thing is part of our everyday life nowadays. Where millions of devices contented to the internet to collect and share data. Although IoT devices are evolving quickly to the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators have been also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. With the rapid development of the Internet of Things in different areas, IoT is facing difficulty in securing overall availability of the network due to its heterogeneous nature. There are many types of vulnerability in IoT that can be mitigated with further research, however, in this paper, we have concentrated on distributed denial of Service attack (DDoS) on IoT. In this paper, we propose a machine learning architecture to detect DDoS attacks in IoT networks. The architecture collects IoT network traffic and analyzes the traffic through passing to machine learning model for attack detection. We propose the use of real-time data collection tool to dynamically monitor the network.
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References
Mahmoud, R., Yousuf, T., Aloul, F., Zualkernan, I.: Internet of things (IoT) security: current status, challenges and prospective measures. In: 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 336–341, December 2015
Zhao, K., Ge, L.: A survey on the internet of things security. In: 2013 Ninth International Conference on Computational Intelligence and Security, pp. 663–667, December 2013
Gunduz, S., Arslan, B., Demirci, M.: A review of machine learning solutions to denial-of-services attacks in wireless sensor networks. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 150–155, December 2015
Abomhara, M., Køien, G.M.: Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks. J. Cyber Secur. 4(1), 65–88 (2015)
Conti, M., Dehghantanha, A., Franke, K., Watson, S.: Internet of things security and forensics: challenges and opportunities (2018)
Lee, I., Lee, K.: The internet of things (IoT): applications, investments, and challenges for enterprises. Bus. Horiz. 58(4), 431–440 (2015)
Rivera, J., van der Meulen, R.: Gartner says the internet of things will transform the data center (2014). Accessed 5 Aug 2014
Osterlind, F., Dunkels, A., Eriksson, J., Finne, N., Voigt, T.: Cross-level sensor network simulation with cooja. In: Proceedings of 2006 31st IEEE Conference on Local Computer Networks, pp. 641–648. IEEE (2006)
Kaplantzis, S., Shilton, A., Mani, N., Sekercioglu, Y.A.: Detecting selective forwarding attacks in wireless sensor networks using support vector machines. In: 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP 2007, pp. 335–340. IEEE (2007)
Sedjelmaci, H., Feham, M.: Novel hybrid intrusion detection system for clustered wireless sensor network. arXiv preprint arXiv:1108.2656 (2011)
Kurosawa, S., Nakayama, H., Kato, N., Jamalipour, A., Nemoto, Y.: Detecting blackhole attack on AODV-based mobile ad hoc networks by dynamic learning method. IJ Netw. Secur. 5(3), 338–346 (2007)
Wallgren, L., Raza, S., Voigt, T.: Routing attacks and countermeasures in the RPL-based internet of things. Int. J. Distrib. Sens. Netw. 9(8), 794326 (2013)
Kasinathan, P., Pastrone, C., Spirito, M.A., Vinkovits, M.: Denial-of-service detection in 6LoWPAN based Internet of Things. In: 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 600–607. IEEE (2013)
Le, A., Loo, J., Chai, K.K., Aiash, M.: A specification-based ids for detecting attacks on RPL-based network topology. Information 7(2), 25 (2016)
Cervantes, C., Poplade, D., Nogueira, M., Santos, A.: Detection of sinkhole attacks for supporting secure routing on 6LoWPAN for Internet of Things. In: IM, pp. 606–611 (2015)
Raza, S., Wallgren, L., Voigt, T.: SVELTE: real-time intrusion detection in the Internet of Things. Ad Hoc Netw. 11(8), 2661–2674 (2013)
Sonar, K., Upadhyay, H.: An approach to secure internet of things against DDoS. In: Proceedings of International Conference on ICT for Sustainable Development, pp. 367–376. Springer (2016)
Oikonomou, G.: Sensniff: live traffic capture and sniffer for iEEE 802.15.4 networks (2017). https://github.com/g-oikonomou/sensniff
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Al-Hadhrami, Y., Hussain, F.K. (2020). A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT. In: Barolli, L., Hussain, F., Ikeda, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_37
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DOI: https://doi.org/10.1007/978-3-030-22354-0_37
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