A Novel Three Layer Filtering (3L-F) Framework for Prevention of DDoS Attack in Cloud Environment

Somasundaram, A.; Meenakshi, V.

doi:10.22247/ijcna/2021/209700

Cited by 2 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on authors namely, Somasundaram DrA, Devaraju S, Jawahar S, Prabu Kanna G, and Thenmozhi, in paper [11] they outlined that cloud computing is a prominent technology that provides a wide range of services to its users in the cloud. As cloud computing continues to grow significantly, researchers were motivated to explore new business models by facilitating the online exchange of critical computing resources.…”

Section: Related Workmentioning

confidence: 99%

“…These effects can be avoided by detecting DDoS attacks early, before they impact the system. In response to this challenge, this research paper [11] proposes a hybrid-based detection of integrating the signature-based detection and anomaly-based detection techniques. According to researchers SaiSindhuTheja Reddy, and Gopal Shyam K in paper [12], they showed that by integrating these approaches, the efficiency of detection rates can be increased, rapid identification of new attack signatures can be achieved, false positive rates can be reduced, and response capabilities can be improved [12].…”

Section: Related Workmentioning

confidence: 99%

“…According to researchers SaiSindhuTheja Reddy, and Gopal Shyam K in paper [12], they showed that by integrating these approaches, the efficiency of detection rates can be increased, rapid identification of new attack signatures can be achieved, false positive rates can be reduced, and response capabilities can be improved [12]. This paper [11] examines various contemporary hybrid-based detections to detect DDoS attacks in cloud-based services, whereas our paper implements hybrid model to detect DDoS (DNS flooding) attacks in MEC.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Implementing a Machine Learning based Hybrid Model to Counter Attacks in Mobile Edge Computing

Chaki,

Velempini

2023

iCARTi

View full text Add to dashboard Cite

This study focuses on the security of 5G mobile network major technology called Multi-Access Edge Computing (MEC) and its susceptibility to distributed denial of service (DDoS) attacks. The goal of the research is to address the effects of DDoS attacks and implement effective mitigation techniques. Several supervised Machine Learning (ML) techniques, which include Random Forest (RF), Decision Tree, Naïve Bayes, K-Nearest Neighbour, Logistics Regression, and Blending/Stack Model, are evaluated using multiple performance metrics such as accuracy, detection/recall, F1-Measure, Matthew’s correlation coefficient, Receiver Operating Characteristic, and Area Under Receiver Operating Characteristic. According to literature, ML algorithms achieve the best performance in mitigating DDoS attacks, therefore, they can be optimized to enhance their effectiveness. The research provides an overview of the existing mitigation schemes in the MEC and proposes a DDoS mitigation scheme. The findings show that hybrid models outperformed traditional ML models. Among the mitigation techniques evaluated, RF proved to be the most effective in mitigating DDoS attacks in MEC.

show abstract

Section: Related Workmentioning

confidence: 99%