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  Scopus ID: 21100926589

A Survey on DDoS Attack Detection by Using Machine Learning Approaches

Dutta Sai Eswari, Venkata Lakshmi Panga and P. Krishna Kishore

Abstract

Denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks are blatant attempts to prevent legitimate users from reaching a service or from using the service they already are using. DDoS assaults are possible due to the internet’s current design, and an attacker can get access to a large number of compromised machines by exploiting their vulnerabilities and setting up attack networks, also known as “Botnets”. Once an attacker has set up an attack network, also known as a botnet, they can launch a massive, coordinated attack against a target or targets. As new attacks are constantly being created, and the number of hosts that may be attacked online continues to grow, several various DDoS attack detection, prevention, and traceback methods have been developed to combat this growing threat. In this paper, we discuss a wide range of DDoS attacks and methods, as well as countermeasures. This review is useful because it addresses many different approaches to stopping DDoS attacks, such as detecting them, defending against them, minimizing their impacts, tracing their origins, answering unresolved problems, and overcoming research difficulties.

Published on: December 01, 2023
doi: 10.17756/nwj.2023-s4-069
Citation: Sai Eswari D, Panga VL, Kishore PK. 2023. A Survey on DDoS Attack Detection by Using Machine Learning Approaches. NanoWorld J 9(S4): S409-S416.

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