No abstract
With the rapid development of the Internet of things technology, the connection between things and people is realized in a real sense, and the intelligent perception, recognition, and management of goods and processes are also achieved. Cloud computing, as one of the core technologies of the Internet of Things, has been widely used in online services of various networks, but the generation of abnormal data will affect the service performance of cloud computing systems. Therefore, effective detection of abnormal data is of great significance to improve the efficiency of the system. Because of the large amount of data and nonlinear distribution in the cloud computing system, the accuracy of traditional methods is low. Based on this, this article proposes a deep learning algorithm based on recurrent neural network (RNN) to implement anomaly detection in the cloud computing system. Based on the basic principle of the RNN algorithm, this article analyses the properties and defects of the activation functions commonly used in RNN, and then improves the RNN algorithm, so as to realize the effective detection of abnormal data in cloud computing system. The simulation results show that the optimized RNN deep learning algorithm for anomaly detection in cloud computing system can effectively improve the detection success rate, effectively reduce the detection time and cost, show strong robustness, and effectively improve the online service efficiency of the Internet of things technology. 1 INTRODUCTION Cloud computing system is a new computing model developed by parallel processing, distributed computing, and grid computing, which is simple to operate, self-upgradable, and manageable, and has the ability of large-scale computing and mass storage. Therefore, it is widely used in online services of various networks and many key tasks. 1,2 However, because the infrastructure of cloud computing system is too complex and fragile, it is vulnerable to external attacks and threats, resulting in abnormal data, to a certain extent, affecting the service performance of cloud computing system, 3 so how to effectively monitor its abnormal data has become a hot research topic in this field. At present, the commonly used anomaly detection methods include sparse Bayesian regression algorithm, 4 wavelet transform algorithm, and statistical algorithm based on entropy and data packet. 5,6 However, due to the large amount of data in the cloud computing system and the nonlinear distribution, the detection success rate of traditional methods is low. In recent years, with the development of artificial intelligence, deep learning has been widely used in anomaly detection of cloud computing systems, and the emergence of recurrent neural network (RNN) provides a new method
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