Open AccessDDoS Attack Detection on Internet o Things using Unsupervised Algorithms
Author(s) -
Hailye Tekleselase
Publication year - 2021
Publication title -
international journal of fuzzy logic systems
Language(s) - English
Resource type - Journals
ISSN - 1839-6283
DOI - 10.5121/ijfls.2021.11401
Subject(s) - denial of service attack , autoencoder , computer science , artificial intelligence , cluster analysis , software deployment , machine learning , network packet , internet of things , unsupervised learning , algorithm , the internet , deep learning , computer security , world wide web , operating system
The increase in the deployment of IoT networks has improved productivity of humans and organisations. However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS attack in IoT networks by classifying incoming network packets on the transport layer as either “Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep learning algorithms and two clustering algorithms were independently trained for mitigating DDoS attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during the experimentation phase. The accuracy score and normalized-mutual-information score are used to quantify the classification performance of the four algorithms. Our results show that the autoencoder performed overall best with the highest accuracy across all the datasets.