PREDICTION APPROACH AGAINST DDOS ATTACK BASED ON MACHINE LEARNING MULTICLASSFIER
Main Article Content
Abstract
DDoS attacks, also known as distributed denial of service (DDoS) attacks, have emerged as one of the most serious and fastest-growing threats on the Internet. Denial-of-service (DDoS) attacks are an example of cyber-attacks that target a specific system or network in an attempt to render it inaccessible or unusable for a period of time. As a result, improving the detection of diverse types of DDoS cyber threats with better algorithms and higher accuracy while keeping the computational cost under control has become the most significant component of detecting DDoS cyber threats. In order to properly defend the targeted network or system, it is critical to first determine the sort of DDoS assault that has been launched against it. A number of ensemble classification techniques are presented in this paper, which combine the performance of various algorithms. They are then compared to existing Machine Learning Algorithms in terms of their effectiveness in detecting different types of DDoS attacks using accuracy, F1 scores, and ROC curves. The results show high accuracy and good performance.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
Badve, O. P.,et al. (2017). Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a cloud
computing environment. Neural Computing and Applications, 28(12), 3655-3682.
Gupta, Brij B., et al. "A Comprehensive Survey on DDoS Attacks and Recent Defense Mechanisms."
Handbook of Research on Intrusion Detection Systems. IGI Global, 2020. 186-218.
Https://radar.cloudflare.com/notebooks/ddos-2022-q1
Mishra, Anupama,et al (2020) "Security Threats and Recent Countermeasures in Cloud Computing." Modern
Principles, Practices, and Algorithms for Cloud Security. IGI Global. 145-161.
Mishra, Anupama, and Neena Gupta. "Analysis of Cloud Computing Vulnerability against DDoS." 2019
International Conference on Innovative Sustainable Computational Technologies (CISCT). IEEE, 2019. 1-6.
Mishra, A.,et al. (2021). Defense mechanisms against DDoS attack based on entropy in SDN-cloud using POX
controller. Telecommunication systems, 77(1), 47-62.
Gaurav, A., et al. (2021). Identity-Based Authentication Mechanism for Secure Information Sharing in the
Maritime Transport System. IEEE Transactions on Intelligent Transportation Systems.