Application of Neural Networks with Feature Selection for Detection of DDoS Attacks in IoT Environments
Abstract
Distributed Denial of Service (DDoS) attacks have increasingly affected the effectiveness of Internet of Things (IoT)-based network infrastructures. In this way, it is necessary to apply solutions to detect DDoS attacks in IoT networks, dealing with issues like scalability, adaptability and heterogeinety. Within this context, this paper presents an Fog-Cloud System for detection of DDoS in IoT networks, based on Neural Networks (NNs) and Features Selection techniques, allowing the identification of the most suitable composition of features to train the model, as well as the necessary scalability. The experiments performed, using real network traffic, suggest that the proposed system reaches 99\% accuracy while reducing the volume of data exchanged and the detection time.
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References
Ahmed, E., Yaqoob, I., Gani, A., Imran, M., and Guizani, M. (2016). Internet-of-thingsbased
smart environments: state of the art, taxonomy, and open research challenges.
IEEE Wireless Communications, 23(5):10–16.
Al-Hadhrami, Y. and Hussain, F. K. (2021). Ddos attacks in iot networks: a comprehensive
systematic literature review. World Wide Web, pages 1–31.
Brun, O., Yin, Y., Augusto-Gonzalez, J., Ramos, M., and Gelenbe, E. (2018). Iot attack
detection with deep learning. In ISCIS Security Workshop.
Cviti´c, I., Perakovi´c, D., Periˇsa, M., and Botica, M. (2021). Novel approach for detection
of iot generated ddos traffic. Wireless Networks, 27(3):1573–1586.
Dao, N.-N., V. Phan, T., Sa’ad, U., Kim, J., Bauschert, T., Do, D.-T., and Cho, S. (2021).
Securing heterogeneous iot with intelligent ddos attack behavior learning. IEEE Systems
Journal, pages 1–10.
Doshi, R., Apthorpe, N., and Feamster, N. (2018). Machine learning ddos detection for
consumer internet of things devices. In 2018 IEEE Security and Privacy Workshops
(SPW), pages 29–35. IEEE.
Kaushik, S. (2016). Introduction to feature selection methods with an example (or how to
select the right variables?). Analytics Vidhya.
Kumar, P., Kumar, R., Gupta, G. P., and Tripathi, R. (2021). A distributed framework
for detecting ddos attacks in smart contract-based blockchain-iot systems by leveraging
fog computing. Transactions on Emerging Telecommunications Technologies,
(6):e4112.
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., and
Elovici, Y. (2018). N-baiot—network-based detection of iot botnet attacks using deep
autoencoders. IEEE Pervasive Computing, 17(3):12–22.
Pisani, F., de Oliveira, F. M. C., Gama, E. S., Immich, R., Bittencourt, L. F., and Borin,
E. (2020). Fog computing on constrained devices: Paving the way for the future iot.
Sharafaldin, I., Lashkari, A. H., Hakak, S., and Ghorbani, A. A. (2019). Developing
realistic distributed denial of service (ddos) attack dataset and taxonomy. In 2019 International
Carnahan Conference on Security Technology (ICCST), pages 1–8. IEEE.
Yamauchi, M., Ohsita, Y., Murata, M., Ueda, K., and Kato, Y. (2019). Anomaly detection
for smart home based on user behavior. In 2019 IEEE International Conference on
Consumer Electronics (ICCE), pages 1–6. IEEE.
