Detection and Mitigation of DDoS Attacks: A Review of Robust and Scalable Solutions

Sheshang Degadwala and Verma Jyoti Sukhdev Sushila

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 5, 12-23

Abstract: Distributed Denial-of-Service (DDoS) attacks have emerged as a critical threat to network security, causing significant disruptions by overwhelming systems with malicious traffic. The motivation behind this review is the growing sophistication and frequency of DDoS attacks, which demand more robust and scalable detection and mitigation techniques. While numerous methods have been proposed, limitations such as high false positive rates, resource constraints, and the evolving nature of attacks continue to challenge existing solutions. This review aims to analyze and evaluate various robust detection mechanisms, including machine learning, anomaly detection, and hybrid models, with a focus on scalability and adaptability in real-world applications. The objective is to identify key strengths and weaknesses in current approaches, highlighting future research directions for building more resilient DDoS defense systems capable of operating efficiently under high-traffic conditions.

Keywords: DDoS Attacks; Network Security; Robust Detection; Mitigation Techniques; Scalability; Anomaly Detection; Machine Learning (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410582
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i5:id:289

DOI: 10.32628/CSEIT2410582

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