Optimized machine learning model for detecting DDoS attacks

Optimized machine learning model for detecting DDoS attacks in Cloud Computing


  •   A new model is being developed that will have the ability to adapt its mutation strategy, crossover rate, and crossover operator. The system has the ability to automatically determine the optimal number of hidden layer neurons.
  •   A DDoS attack detection system for cloud computing has been constructed using the developed model.
  •   The proposed system has undergone performance evaluation through various experiments using state-of-the-art datasets. The evaluation included comparisons with other machine learning models and systems that are widely employed.

  • Description

Cloud computing is a cost-effective solution for users and organizations as it offers a range of online resources that can help reduce infrastructure costs. The resources are available as services. The payment policy is based on pay-as-you-use, indicating that users/organizations are only charged for the amount of time they use the service. Utilizing a connection with these services is crucial because without them, users and organizations may face significant financial or negative consequences. A potential security threat to cloud computing is the Distributed Denial of Service (DDoS) attack, which can impact the availability of cloud services. It is important to defend against these attacks.