| Title | Predicting Distributed Denial of Service Attacks in Machine Learning Field |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Umarani, S., Aruna, R., Kavitha, V. |
| Conference Name | 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) |
| Keywords | classification, composability, independent component analysis, machine learning, Metrics, modified Cuckoo Search, Modified Source Support Vector Machine, pattern classification, Protocols, pubcrawl, resilience, Resiliency, Search methods, supply vector machines, Support vector machines, Training |
| Abstract | A persistent and serious danger to the Internet is a denial of service attack on a large scale (DDoS) attack using machine learning. Because they originate at the low layers, new Infections that use genuine hypertext transfer protocol requests to overload target resources are more untraceable than application layer-based cyberattacks. Using network flow traces to construct an access matrix, this research presents a method for detecting distributed denial of service attack machine learning assaults. Independent component analysis decreases the number of attributes utilized in detection because it is multidimensional. Independent component analysis can be used to translate features into high dimensions and then locate feature subsets. Furthermore, during the training and testing phase of the updated source support vector machine for classification, their performance it is possible to keep track of the detection rate and false alarms. Modified source support vector machine is popular for pattern classification because it produces good results when compared to other approaches, and it outperforms other methods in testing even when given less information about the dataset. To increase classification rate, modified source support Vector machine is used, which is optimized using BAT and the modified Cuckoo Search method. When compared to standard classifiers, the acquired findings indicate better performance. |
| DOI | 10.1109/ICACITE53722.2022.9823865 |
| Citation Key | umarani_predicting_2022 |
Predicting Distributed Denial of Service Attacks in Machine Learning Field