Detecting DDoS Attacks using Machine Learning: Survey


  • Sarah Zghair Arrak College of Computer science & Information Technology , University of Al –Qadisiyah , Al –Diwaniyah , Iraq
  • Rana Jumma Surayh Al- Janabi College of Computer science & Information Technology , University of Al –Qadisiyah , Al –Diwaniyah , Iraq



DDoS attack, distributed denial of service, detection, machine learning algorithms(ML), deep learning.


Phishing attacks have increased dramatically in recent years affecting many areas of society. Phishing attempts often use DDoS attacks to flood a server with too many requests, overwhelming it. DDoS attacks represent a major threat to cybersecurity and pose a significant risk to computer networks. Creating a solid defense system against these attacks is essential but complex due to the wide range of attack methods and complex networks and communication protocols. Ransom demands, revenge, rivalry, or other motives may trigger attacks. This survey discusses DDoS attacks, the advantages and disadvantages of detecting DDoS using machine and deep learning, and a framework for detection using machine learning and deep learning. And use their classifiers to detect DDoS attacks. Furthermore, we explore datasets used in related works. This research is necessary because DDoS attacks are diverse and pose a significant threat to computer networks.


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How to Cite

Zghair Arrak , S., & Jumma Surayh Al- Janabi , R. (2024). Detecting DDoS Attacks using Machine Learning: Survey. Journal of Al-Qadisiyah for Computer Science and Mathematics, 16(2), Comp. 118– 134 .



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