E3S Web Conf.
Volume 297, 2021The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
|Number of page(s)||9|
|Published online||22 September 2021|
DDoS Detection on Internet of Things using Unsupervised Algorithms
Department of Information Systems, School of Informatics, Wolaita Sodo University, Wolaita Sodo, Ethiopia
Correspondence should be addressed to Hailye Tekleselassie: email@example.com
Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.
Key words: Distributed denial of Service / wireless networks / Machine Learning Algorithms / Transmission Control Protocol / CNN / network security
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.