Open AccessFeatures-Aware DDoS Detection in Heterogeneous Smart Environments based on Fog and Cloud Computing
Author(s) -
Wanderson L. Costa,
Ariel L. C. Portela,
Rafael L. Gomes
Publication year - 2021
Publication title -
international journal of communication networks and information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 20
eISSN - 2076-0930
pISSN - 2073-607X
DOI - 10.54039/ijcnis.v13i3.5080
Subject(s) - denial of service attack , computer science , cloud computing , scalability , adaptability , context (archaeology) , fog computing , trinoo , internet of things , distributed computing , application layer ddos attack , computer network , computer security , the internet , real time computing , world wide web , database , ecology , paleontology , biology , operating system
Nowadays, urban environments are deploying smart environments (SEs) to evolve infrastructures, resources, and services. SEs are composed of a huge amount of heterogeneous devices, i.e., the SEs have both personal devices (smartphones, notebooks, tablets, etc) and Internet of Things (IoT) devices (sensors, actuators, and others). One of the existing problems of the SEs is the detection of Distributed Denial of Service (DDoS) attacks, due to the vulnerabilities of IoT devices. In this way, it is necessary to deploy solutions that can detect DDoS in SEs, dealing with issues like scalability, adaptability, and heterogeneity (distinct protocols, hardware capacity, and running applications). Within this context, this article presents an Intelligent System for DDoS detection in SEs, applying Machine Learning (ML), Fog, and Cloud computing approaches. Additionally, the article presents a study about the most important traffic features for detecting DDoS in SEs, as well as a traffic segmentation approach to improve the accuracy of the system. The experiments performed, using real network traffic, suggest that the proposed system reaches 99% of accuracy, while reduces the volume of data exchanged and the detection time.