AI in the Detection and Prevention of Distributed Denial of Service (DDoS) Attacks

EasyChair Preprint 15386

Abstract

Distributed Denial of Service (DDoS) attacks are malicious attacks that aim to disrupt the normal flow of traffic to the targeted server or network by manipulating the server’s infrastructure with overflowing internet traffic. This study aims to investigate several artificial intelligence (AI) models and utilise them in the DDoS detection system. The paper examines how AI is being used to detect DDoS attacks in real-time to find the most accurate methods to improve network security. The machine learning models identified and discussed in this research include random forest, decision tree (DT), convolutional neural network (CNN), NGBoosT classifier, and stochastic gradient descent (SGD). The research findings demonstrate the effectiveness of these models in detecting DDoS attacks. The study highlights the potential for future enhancement of these technologies to enhance the security and privacy of data servers and networks in real-time. Using the qualitative research method and comparing several AI models, research results reveal that the random forest model offers the best detection accuracy (99.9974%). This finding holds significant implications for the enhancement of future DDoS detection systems.

Keyphrases: Accuracy, Artificial Intelligence, Distributed Denial of Service (DDoS), detection, machine learning

@booklet{EasyChair:15386,
  author    = {Sina Ahmadi},
  title     = {AI in the Detection and Prevention of Distributed Denial of Service (DDoS) Attacks},
  howpublished = {EasyChair Preprint 15386},
  year      = {EasyChair, 2024}}