Research on intrusion detection methods for ship communication networks based on cloud computing
To improve the real-time performance of intrusion detection in the face of large-scale network attacks or sudden attacks,a cloud computing based intrusion detection method for ship communication networks is studied.Using the MapReduce programming model of cloud computing,design a genetic quantum particle swarm optimization algorithm for MapReduce parallelization,and extract network intrusion features from ship communication network data;Using the MapReduce parallelized entropy clustering algorithm,determine the basis function center of the radial basis function neural network;After determining the center of the basis function,input network intrusion feature samples into the Map function of the MapReduce programming model,train the neural network,optimize the weights of the neural network,output training completion instructions through the Reduce function,and complete the neural network training;In the trained MapReduce parallelized radial basis function neural network,input feature samples and output intrusion detection results for ship communication networks.Experimental results have shown that this method can effectively extract intrusion features from ship communication networks;This method can accurately detect ship communication network intrusion under different types of network attacks.
cloud computingship communication networkintrusion detectionmapreduce parallelismparticle swarmradial basis function
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