Application of Neural Networks with Feature Selection for Detection of DDoS Attacks in IoT Environments
DOI:
https://doi.org/10.5753/reic.2022.2295Abstract
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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