Open AccessDDoS Attacks Detection in the IoT Using Deep Gaussian-Bernoulli Restricted Boltzmann Machine
Author(s) -
Gafarou O. Coli,
Segun Aina,
Samuel D. Okegbile,
Adebayo Lawal,
Adeniran Oluwaranti
Publication year - 2022
Publication title -
modern applied science
Language(s) - English
Resource type - Journals
eISSN - 1913-1852
pISSN - 1913-1844
DOI - 10.5539/mas.v16n2p12
Subject(s) - denial of service attack , computer science , softmax function , internet of things , deep learning , bernoulli's principle , artificial intelligence , restricted boltzmann machine , application layer ddos attack , network packet , trinoo , machine learning , computer network , computer security , the internet , world wide web , engineering , aerospace engineering
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 eciently detect DDoS attacks in IoT.