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Simulation Analysis of DDoS Attack in IoT Environment

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1122)


Now a day, Internet of Things (IoT) has touched almost every corner of human and unimaginably affect our life by its applications. Resources and environment are being more susceptible to security threats like Virus, DoS/DDoS, Ransomware, Spyware, IP Spoofing, etc. To consider security services and IoT devices capabilities, low power and processing constraints, response rate, this paper has proposed a Decision Tree-Based IDS for IoT environment to prevent intra and inter network from DoS/DDoS attacks. In this paper, the analysis is done in two ways- (a) Power consumption and (b) Attack Detection. The experiments are conducted in the Cooja simulator pre-installed in Contiki operating system within the virtual machine. From attack detection mode it is concluded that C5 Decision Tree-Based IDS model shows high accuracy with low false alarm rate (FAR). Whereas, from power consumption mode it is observed that the simulated network suffers from high-power consumption and around three times more CPU power and two-time Listening Power consumption during attack as compare to their normal behavior.


  • Internet of Thing
  • Intrusion Detection System
  • Monitor node
  • Decision tree
  • CPU power
  • LPM power
  • Listen power

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  • DOI: 10.1007/978-3-030-39875-0_8
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  1. Zanella, A., Bui, N., Castellani, A., Vangelista, L., Zorzi, M.: Internet of things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2014)

    CrossRef  Google Scholar 

  2. Bellavista, P., Cardone, G., Corradi, A., Foschini, L.: Convergence of MANET and WSN in IoT urban scenarios. IEEE Sens. J. 13(10), 3558–3567 (2013)

    CrossRef  Google Scholar 

  3. The Mirai botnet explained: How teen scammers and CCTV cameras almost brought down the internet.

  4. Loulianou, P., Vasilakis, V., Moscholios, I., Logothetis, M.: A signature-based intrusion detection system for the internet of things. In: Information and Communication Technology Form (2018)

    Google Scholar 

  5. Shreenivas, D., Raza, S., Voigt, T.: Intrusion detection in the RPL-connected 6LoWPAN networks. In: Proceedings of the 3rd ACM International Workshop on IoT Privacy, Trust, and Security, pp. 31–38. ACM, April 2017

    Google Scholar 

  6. Moustafa, N., Slay, J.: The evaluation of Network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Inf. Secur. J. A Glob. Perspect. 25(1–3), 18–31 (2016)

    CrossRef  Google Scholar 

  7. Koroniotis, N., Moustafa, N., Sitnikova, E., Slay, J.: Towards developing network forensic mechanism for Botnet Activities in the IoT based on machine learning techniques. In: International Conference on Mobile Networks and Management, pp. 30–44. Springer, Cham (2017)

    Google Scholar 

  8. Papamartzivanos, D., Mármol, F.G., Kambourakis, G.: Dendron: genetic trees driven rule induction for network intrusion detection systems. Future Gener. Comput. Syst. 79, 558–574 (2018)

    CrossRef  Google Scholar 

  9. Eastman, D., Kumar, S.A.: A simulation study to detect attacks on internet of things. In: 2017 IEEE 15th International Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence & Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 645–650. IEEE, November 2017

    Google Scholar 

  10. Cooja Simulator – Contiki.

  11. Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: Military Communications and Information Systems Conference, pp. 1–6 (2015)

    Google Scholar 

  12. KDD 99 data set.

  13. Diaz, A., Sanchez, P.: Simulation of attacks for security in wireless sensor network. Sensors 16(11) (2016)

    CrossRef  Google Scholar 

  14. Raza, S., Wallgren, L., Voigt, T.: SVELTE: real-time intrusion detection in the Internet of Things. Ad Hoc Netw. 11(8), 2661–2674 (2013)

    CrossRef  Google Scholar 

  15. Mehare, T.M., Bhosale, S.: Design and development of intrusion detection system for internet of things. Int. J. Innovative Res. Comput. Commun. Eng. 5(7), 13469–13475 (2017)

    Google Scholar 

  16. Kumar, V., Das, K.A., Sinha, D.: Statistical analysis of the UNSW-NB15 dataset for intrusion detection. In: 1st International Conference on Computational Intelligence in Pattern Recognition. Springer (2019, in press)

    Google Scholar 

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Correspondence to Vikash Kumar .

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Kumar, V., Kumar, V., Sinha, D., Das, A.K. (2020). Simulation Analysis of DDoS Attack in IoT Environment. In: Nain, N., Vipparthi, S. (eds) 4th International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2019. ICIoTCT 2019. Advances in Intelligent Systems and Computing, vol 1122. Springer, Cham.

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