Large-scale network unbounded DoS intrusion detection based on gravity search algorithm
In order to ensure large-scale network security and reduce the harm of unbounded DoS intrusion,a large-scale network unbounded DoS intrusion detection method based on gravity search algorithm is proposed.DWT(Discrete Wavelet Transform)is used to extract network traffic features and complete dimensionality reduction of large-scale network traffic data.The features are used as input data,and SVM(Support Vector Machine)is used for unbounded DoS intrusion detection.The minimum root-mean-square error of the output results during SVM training is taken as the goal.The heavy gravity search algorithm is used to obtain the optimal penalty factor,insensitivity coefficient and kernel parameters of SVM,and the results are returned to SVM to update the parameters.The unbounded DoS intrusion detection is completed by using SVM with the best parameters.The experimental results show that the optimized SVM detection error rate decreases rapidly,and only 60 iterations are carried out,and the optimized SVM completes the convergence of the error rate.After 15:00,the average throughput of nodes increased rapidly from 200～300 bit/s to about 2 000 bit/s.
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