DDoS Attacks Detection in the IoT Using Deep Gaussian-Bernoulli Restricted Boltzmann Machine

Abstract

Distributed denial of service (DDoS) attack is generally known as one of the most significant threats to the internet of things (IoT). Current detection technologies of DDoS attacks are not adequate for IoT systems because of the peculiar features of IoT such as resource constraint nodes, specific network architecture, and specific network protocols. Providing adequate DDoS attacks detection systems to IoT, however, becomes a necessity since IoT is ubiquitous. This study hence developed a deep learning-based model for detecting DDoS in IoT, while considering its peculiarities. The proposed deep learning-based model was formulated using a deep Gaussian-Bernoulli restricted Boltzmann machine (DBM) because of its capability to learn high-level features from input following the unsupervised approach and its ability to manage real-time data that is common in the IoT network. Furthermore, the SoftMax regression was used for classification. The accuracy of the proposed model on the network socket layer-knowledge discovery in databases was obtained as 93.52%. The outcome of the study shows that the proposed DBM can efficiently detect DDoS attacks in IoT.