Detection and Mitigation of DDOS based Attacks using Machine Learning Algorithm
| Title | Detection and Mitigation of DDOS based Attacks using Machine Learning Algorithm |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Satyanarayana, D, Alasmi, Aisha Said |
| Conference Name | 2022 International Conference on Cyber Resilience (ICCR) |
| Date Published | oct |
| Keywords | Analytical models, Botnet, composability, DDoS attack detection, DDoS attack mitigation, DDoS Attacks, decision making, denial-of-service attack, Human Behavior, machine learning, machine learning algorithms, Metrics, Network security, Proposals, pubcrawl, resilience, Resiliency, Servers |
| Abstract | In recent decades, a Distributed Denial of Service (DDoS) attack is one of the most expensive attacks for business organizations. The DDoS is a form of cyber-attack that disrupts the operation of computer resources and networks. As technology advances, the styles and tools used in these attacks become more diverse. These attacks are increased in frequency, volume, and intensity, and they can quickly disrupt the victim, resulting in a significant financial loss. In this paper, it is described the significance of DDOS attacks and propose a new method for detecting and mitigating the DDOS attacks by analyzing the traffics coming to the server from the BOTNET in attacking system. The process of analyzing the requests coming from the BOTNET uses the Machine learning algorithm in the decision making. The simulation is carried out and the results analyze the DDOS attack. |
| DOI | 10.1109/ICCR56254.2022.9995773 |
| Citation Key | satyanarayana_detection_2022 |