DDoS Attacks in Cloud Computing: Detection and Prevention

TL;DR

This paper provides a comprehensive overview of DDoS attack types, detection methods, and prevention techniques in cloud computing, highlighting their effectiveness and limitations to improve cybersecurity defenses.

Contribution

It offers an extensive analysis of DDoS attack characteristics, evaluates existing detection and prevention methods, and compares their suitability for various attack types and environments.

Findings

Machine learning-based detection approaches show promising accuracy.

Prevention techniques like firewalls and rate limiting vary in effectiveness.

Combination of detection and prevention strategies enhances security posture.

Abstract

DDoS attacks are one of the most prevalent and harmful cybersecurity threats faced by organizations and individuals today. In recent years, the complexity and frequency of DDoS attacks have increased significantly, making it challenging to detect and mitigate them effectively. The study analyzes various types of DDoS attacks, including volumetric, protocol, and application layer attacks, and discusses the characteristics, impact, and potential targets of each type. It also examines the existing techniques used for DDoS attack detection, such as packet filtering, intrusion detection systems, and machine learning-based approaches, and their strengths and limitations. Moreover, the study explores the prevention techniques employed to mitigate DDoS attacks, such as firewalls, rate limiting , CPP and ELD mechanism. It evaluates the effectiveness of each approach and its suitability for different types of attacks and environments. In conclusion, this study provides a comprehensive overview of the different types of DDoS attacks, their detection, and prevention techniques. It aims to provide insights and guidelines for organizations and individuals to enhance their cybersecurity posture and protect against DDoS attacks.