A DDoS Detection Approach Based on CNN in Cloud Computing

Abstract

A novel DDoS detection approach based on Cellular Neural Network (CNN) model in cloud computing is proposed in this paper. Cloud computing is a new generation of computation and information platform, which faces many security issues owing to the characteristics such as widely distributed and heterogeneous environment, voluminous, noisy and volatile data, difficulty in communication, changing attack patterns. CNN is an artificial neural network which features a multi-dimensional array of neurons and local interconnections among cells and CNN can be used to solve the cloud security difficulties according to the nature of non-linear and dynamic. RPLA and Tabu optimized algorithm is employed to learn the CNN classifier templates and bias for DDoS intrusion detection in cloud computing. Experiments on DDoS attacks detection show that whether RPLA-CNN or Tabu-CNN models are effective for DDoS Attacks detection. Results show that CNN model for DDoS attacks detection in cloud computing exhibits an excellent performance with the higher attack detection rate with lower false positive rate.