The applications such as the remote communication and the control system are in critically integrated arrangement. The controlling of these network is specified by supervisory control and data acquisition (SCADA) syst...
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The applications such as the remote communication and the control system are in critically integrated arrangement. The controlling of these network is specified by supervisory control and data acquisition (SCADA) systems. This study discusses about the attack prediction and classification process by using an enhanced model of machinelearning technology. The attack types are classified by the optimal selection of features extracted from the sensor data. In this, the features are labelled and cluster between the matrixes are extracted. These cluster forms the initial processing of attack identification which prevents the mismatched result. This clustering of data is performed by mean-shift clustering algorithm. From that clustered data, the features that are irrelevant for classification process is identified and suppressed by using the genetically seeded flora optimisation algorithm. In this optimisation process, the flora seeds are selected genetically to select best features. Then, from that optimally selected clustered data, the relevancy vector is predicted and the types are classified. The classification process is performed by the boltzmann machine learning algorithm. The classified results of the proposed method for testing SCADA dataset are analysed and the performance metrics are evaluated and compared with the state-of-the-art methods.
Sugarscape model is a multi-agent environment that is used for modeling and organizing processes such as social, political and economic. After the previous studies which were concerned with the production of a learned...
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ISBN:
(纸本)9781467313988
Sugarscape model is a multi-agent environment that is used for modeling and organizing processes such as social, political and economic. After the previous studies which were concerned with the production of a learned multi-agent model based on boltzmann machine learning algorithm and also the evaluation of the learning of a learned system in sugarscape, the purpose of this study is to evaluate the learning done after adding the two parameters of communication and cooperation to the sugarscape learned model. Thus a cellular learned multi-agent model with use of boltzmann machine learning algorithm based on sugarscape model was considered. In this model, each agent has been allocated with a parameter that indicates the knowledge of the agent. Once all agents reach sugar peaks it means that all agents have become knowledgeable and the model has converged. After that the two parameters of communication and cooperation are added to the given model and for each one of the models the number of agents present in sugar peaks after the model had reached convergence per the specific number of agents has been measured. After analyzing the resulting diagram it was concluded that after the convergence of the model, the average number of knowledgeable agents in learned model with communication and cooperation is higher than the number of knowledgeable agents in learned model without use of communication and cooperation. Therefore communication and cooperation of the agents causes to increase the learning done in the environment.
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