Research Article
A Defense Mechanism for Identifying DDoS Attack in SDN Based Cloud
@INPROCEEDINGS{10.4108/eai.23-11-2023.2343176, author={Lavanya A}, title={A Defense Mechanism for Identifying DDoS Attack in SDN Based Cloud}, proceedings={Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India}, publisher={EAI}, proceedings_a={IACIDS}, year={2024}, month={3}, keywords={distributed denial-of-service hyper-heuristic butterfly optimization algorithm software-defined networks support vector machines source-based ip filtering}, doi={10.4108/eai.23-11-2023.2343176} }- Lavanya A
Year: 2024
A Defense Mechanism for Identifying DDoS Attack in SDN Based Cloud
IACIDS
EAI
DOI: 10.4108/eai.23-11-2023.2343176
Abstract
The advancements in Software defined Networking (SDN) techniques, which have significantly increased the number of cyber security threats, cloud applications have experienced an astounding transformation. The performance of most organizations' systems is severely decreased by the damaging cyberattack known as distributed denial of service (DDoS). By effectively identifying the traffic data in the SDN-based cloud, prominent research has found that machine learning models are the most efficient options for detecting assaults. This work seeks to offer a machine learning classifier model that balances accuracy and complexity based on these interpretations. Through the use of a brand-new Hyper-heuristic Butterfly Optimisation Algorithm (HHBOA), an enhanced Support Vector Machine (SVM) classifier has been developed. Source-based IP filtering (SIPF), an effective filtering method, was used at first.