Abstract
Distributed Denial of Service (DDoS) attacks are one of the most versatile and powerful cyber-attack that has remarkably grown in sophistication, throughput as well as bandwidth. In addition to multiple efficient, effective, and accurate ML approaches been proposed over the years, DDoS detection remains a challenge due to ever-growing advancements in network technology. In this paper, a hybrid approach incorporating machine learning algorithms towards the detection of DDoS attacks is proposed. This approach includes a classifier that is formed by cascading two machine learning algorithms, Random Forest (RF) with a Multi-layer Perceptron (MLP) Neural Network. These algorithms were chosen due to their high real-time accuracies, efficiency, adaptability, and flexibility in changing the parameters which is vital due to the complex and changeable nature of network technology, that may cause inaccurate results if a set of features turn obsolete over time. Feature selection is done carefully to obtain the highest possible accuracy and efficiency through Information Gain (IG) algorithm which would aid towards detection of modern cyber-attacks including zero-day attacks.
Original language | English |
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Title of host publication | Proceedings of the IEEE Madras Section International Conference 2021, MASCON 2021 |
Place of Publication | Piscataway, NJ |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1-7 |
Number of pages | 7 |
ISBN (Electronic) | 9781665404051 |
ISBN (Print) | 978-1-6654-4736-2 |
DOIs | |
Publication status | Published - 27 Aug 2021 |
Event | 2021 IEEE Madras Section International Conference, MASCON 2021 - Chennai, India Duration: 27 Aug 2021 → 28 Aug 2021 |
Publication series
Name | Proceedings of the IEEE Madras Section International Conference 2021, MASCON 2021 |
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Conference
Conference | 2021 IEEE Madras Section International Conference, MASCON 2021 |
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Country/Territory | India |
City | Chennai |
Period | 27/08/21 → 28/08/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.