| Issue |
E3S Web Conf.
Volume 184, 2020
2nd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED 2020)
|
|
|---|---|---|
| Article Number | 01052 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/e3sconf/202018401052 | |
| Published online | 19 August 2020 | |
A Survey of DDOS Attacks Using Machine Learning Techniques
1 Assistant Professor, CSE Department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India
2 Assistant Professor, CSE Department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India
3 Professor, CSE Department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India
The DDoS attacks are the most destructive attacks that interrupt the safe operation of essential services delivered by the internet community’s different organizations. DDOS stands for Distributed Denial Of Service attacks. These attacks are becoming more complex and expected to expand in number day after day, rendering detecting and combating these threats challenging. Hence, an advanced intrusion detection system (IDS) is required to identify and recognize an- anomalous internet traffic behaviour. Within this article the process is supported on the latest dataset containing the current form of DDoS attacks including (HTTP flood, SIDDoS). This study combines well-known grouping methods such as Naïve Bayes, Multilayer Perceptron (MLP), and SVM, Decision trees.
© The Authors, published by EDP Sciences, 2020
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.
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