| Title | Application of Random Forest Classifier for Prevention and Detection of Distributed Denial of Service Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Das, Soumyajit, Dayam, Zeeshaan, Chatterjee, Pinaki Sankar |
| Conference Name | 2022 OITS International Conference on Information Technology (OCIT) |
| Keywords | composability, compositionality, Computer crime, DDoS Attack, DDoS Attack Prevention, denial-of-service attack, DoS attack, machine learning, Metrics, multiple regression, open systems, Predictive models, pubcrawl, regression analysis, rendering (computer graphics), resilience, Resiliency, Transportation |
| Abstract | A classification issue in machine learning is the issue of spotting Distributed Denial of Service (DDos) attacks. A Denial of Service (DoS) assault is essentially a deliberate attack launched from a single source with the implied intent of rendering the target's application unavailable. Attackers typically aims to consume all available network bandwidth in order to accomplish this, which inhibits authorized users from accessing system resources and denies them access. DDoS assaults, in contrast to DoS attacks, include several sources being used by the attacker to launch an attack. At the network, transportation, presentation, and application layers of a 7-layer OSI architecture, DDoS attacks are most frequently observed. With the help of the most well-known standard dataset and multiple regression analysis, we have created a machine learning model in this work that can predict DDoS and bot assaults based on traffic. |
| DOI | 10.1109/OCIT56763.2022.00078 |
| Citation Key | das_application_2022 |
Application of Random Forest Classifier for Prevention and Detection of Distributed Denial of Service Attacks