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Real-time DDoS flood attack monitoring and detection (RT-AMD) model for cloud computing.

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Summary

This study introduces a machine learning model for real-time detection of distributed denial of service (DDoS) attacks in cloud computing environments. The RT-AMD model achieved over 99% accuracy, enhancing cloud security.

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Area of Science:

  • Cloud Computing
  • Cybersecurity
  • Machine Learning

Background:

  • Cloud computing offers on-demand resources but faces significant threats like distributed denial of service (DDoS) attacks.
  • DDoS attacks pose a serious risk to cloud service availability and integrity.
  • Effective real-time monitoring and detection are crucial for cloud security.

Purpose of the Study:

  • To develop and evaluate a machine learning model for real-time detection of DDoS attacks in cloud environments.
  • To assess the performance of various machine learning classifiers in identifying DDoS flood attacks.
  • To propose a novel predictive model, RT-AMD, for enhanced cloud security.

Main Methods:

  • Utilized machine learning classifiers including Naïve Bayes, K-nearest neighbor, decision tree, and random forest.
  • Developed a predictive model named Real-Time DDoS flood Attack Monitoring and Detection (RT-AMD).
  • Evaluated the RT-AMD model on the DDoS-2020 dataset (70,020 records) and the NSL-KDD dataset, focusing on TCP, DNS, and ICMP protocols.

Main Results:

  • The proposed RT-AMD model demonstrated high accuracy in detecting DDoS attacks.
  • Random forest classifier achieved 99.38% accuracy in real-time cloud detection and 99.39% in local testing.
  • RT-AMD achieved 99.30% accuracy in real-time cloud detection on the NSL-KDD dataset.

Conclusions:

  • The RT-AMD model effectively enables real-time monitoring and detection of DDoS attacks in cloud environments.
  • Incremental learning contributed to the model's real-time attack detection capabilities.
  • The proposed approach shows significant improvements over related works, enhancing cloud security against DDoS threats.