A Survey of Distributed Denial of Service (DDoS) Attack Mitigation Techniques
Rajender Pell Reddy · International Journal of Computer Trends and Technology · 2024
One of the biggest and continuous challenges to the availability of online services currently is Distributed Denial of Service (DDoS) attacks. These attacks seek to deny users and/or network resources access to a specific server, service or network through its inundation with a large number and threatening traffic. Besides making the target system unusable, this leads to tremendous operational and financial losses for organizations. Botnets, amplification attacks, various evasion techniques, etc., are all piling on the pressure as attackers’ sophistication increases, meaning traditional security measures are ineffective. Many techniques have been evolved to prevent or mitigate these attacks, such as the simple ones, like rate limiting and IP blacklisting, to the complex techniques, like anomaly-based detection and Machine Learning (ML) models. In this survey, we offer a comprehensive review of DDoS attack mitigation techniques, categorizing them into three key areas: prevention, detection, and actions after the emergence of occurrences. We look into contemporary approaches like real-time anomaly detection systems based on artificial intelligence and distributed defense framework, which seek to counter enormous system-level multi-vector DDoS attacks. Our examination also discusses the effectiveness and working issues related to technique and concentrates on high-level adaptive and scalable techniques for combating threats. Furthermore, we also provide a comparative analysis of these techniques in a tabular and graphical form with the help of figures so that an overall picture of the prevailing situation can be presented accurately. The paper concludes with directions for future research about the areas mentioned above, such as the application of decentralized security utilizing blockchain and the advancement of the integration of machine learning in order to enhance attack prediction and prevention.