Abstract: Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to network infrastructures, causing disruptions and financial losses for organizations worldwide. Traditional defense mechanisms often fall short in effectively detecting and mitigating these sophisticated attacks. In this study, a novel approach for the detection and protection analysis of DDoS attacks utilizing clustering data mining techniques. By leveraging the inherent patterns and anomalies present in network traffic data, the method aims to enhance the accuracy and efficiency of Distributed Denial of Service (DDoS) attack detection while minimizing false positives. Employ clustering algorithms to group network traffic data into distinct clusters, allowing for the identification of abnormal traffic patterns indicative of DDoS attacks. Furthermore, we analyze the effectiveness of various clustering algorithms, including k-means, DBSCAN, and hierarchical clustering, in accurately detecting and mitigating DDoS attacks. Through extensive experimentation on real-world network datasets, we demonstrate the efficacy of our approach in accurately detecting and mitigating DDoS attacks while minimizing the impact on legitimate network traffic. Our findings underscore the importance of leveraging clustering data mining techniques for enhancing the resilience of network infrastructures against DDoS attacks. [1]
Keywords: DDoS Attacks, Clustering Data Mining, Network Security, Intrusion Detection.
Title: DETECTION AND PROTECTION ANALYSIS OF DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACKS USING CLUSTERING DATA MINING
Author: Ojiruwe Benjamin Uzazo, Yazeed Al Moaiad
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 12, Issue 2, April 2024 - June 2024
Page No: 25-28
Research Publish Journals
Website: www.researchpublish.com
Published Date: 29-May-2024
