The Cloud Resource Load Security Detection Based on Wavelet Packet Transform and Grey Wolf Algorithm

Abstract

In order to improve the service quality of online business, solve the problem of high false alarm rate and low accuracy rate caused by the traditional cloud computing security detection methods. This paper proposed a method of combining wavelet packet decomposition and grey wolf algorithm to optimize the least squares support vector machine for cloud resource load. The load sequence is decomposed by the wavelet packet transform, and the least squares support vector machine is used to predict the reconfigurable load subsequence, which is improved by the gray Wolf Chaos positive cosine search. Early warning is achieved by detecting abnormal reduction and growth at various frequencies. Experiments show that this method can greatly reduce the false positive rate and has a good prediction accuracy.