The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also, as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop. Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.
As a security defense technique to protect networks from attacks, a network intrusion detection model plays a crucial role in the security of computer systems and networks. Aiming at the shortcomings of a complex feature extraction process and insufficient information extraction of the existing intrusion detection models, an intrusion detection model named the FCNN-SE, which uses the fusion convolutional neural network (FCNN) for feature extraction and stacked ensemble (SE) for classification, is proposed in this paper. The proposed model mainly includes two parts, feature extraction and feature classification. Multi-dimensional features of traffic data are first extracted using convolutional neural networks of different dimensions and then fused into a network traffic dataset. The heterogeneous base learners are combined and used as a classifier, and the obtained network traffic dataset is fed to the classifier for final classification. The comprehensive performance of the proposed model is verified through experiments, and experimental results are evaluated using a comprehensive performance evaluation method based on the radar chart method. The comparison results on the NSL-KDD dataset show that the proposed FCNN-SE has the highest overall performance among all compared models, and a more balanced performance than the other models.
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