A Collaborative Detection Method of Wireless Mobile Network Intrusion Based on Cloud Computing

Abstract

In order to improve the communication security of wireless mobile network, a collaborative intrusion detection method based on cloud computing is studied. The mobile terminal and the cloud computing platform are connected by the wireless mobile network. The cloud computing platform authentication server adopts a dual server and multifactor authentication scheme for mobile cloud computing to provide authentication services for mobile terminal users. The web server of the cloud computing platform uses the intrusion node detection protocol of the neighbor classification mechanism to provide a communication security protocol for users; Using the HMM algorithm, the intrusion detection module of the computing platform realizes the intrusion detection of wireless mobile network. Finally, using authentication service, security protocol, and intrusion detection module completes the cooperative detection of mobile network intrusion. The experimental results show that this method can realize the cooperative detection of wireless mobile network intrusion, and the detection accuracy is as high as 98%, which ensures the communication security of wireless mobile network.

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

“…This is because the current research on CC service technology is still at a relatively rudimentary stage in terms of technological advancements. Softmax single classifiers are frequently used in traditional deep learning-based ID methods for categorization in the output layer [15]. Due to their impact on data dimensions, the training model frequently exhibits a poor detection rate of attacks in the presence of large amounts of high-dimensional data, which has an impact on the overall detection efficiency.…”