Purpose The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations (NSS). Because the conventional methods for the prediction of NSS, such as support vector machine, particle swarm optimization, etc., lack accuracy, robustness and efficiency, in this study, the authors propose a new method for the prediction of NSS based on recurrent neural network (RNN) with gated recurrent unit. Design/methodology/approach This method extracts internal and external information features from the original time-series network data for the first time. Then, the extracted features are applied to the deep RNN model for training and validation. After iteration and optimization, the accuracy of predictions of NSS will be obtained by the well-trained model, and the model is robust for the unstable network data. Findings Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models. Although the deep RNN models need more time consumption for training, they guarantee the accuracy and robustness of prediction in return for validation. Originality/value In the prediction of NSS time-series data, the proposed internal and external information features are well described the original data, and the employment of deep RNN model will outperform the state-of-the-arts models.

In open and complex underwater environments, targets to be monitored are highly dynamic and exhibit great uncertainty. To optimize monitoring target coverage, the development of a method for adjusting sensor positions based on environments and targets is of crucial importance. In this paper, we propose a distributed hybrid fish swarm optimization algorithm (DHFSOA) based on the influence of water flow and the operation of an artificial fish swarm system to improve the coverage efficacy of the event set and to avoid blind movements of sensor nodes. First, by simulating the behavior of foraging fish, sensor nodes autonomously tend to cover events, with congestion control being used to match node distribution density to event distribution density. Second, the construction of an information pool is used to achieve information-sharing between nodes within the network connection range, to increase the nodes’ field of vision, and to enhance their global search abilities. Finally, we conduct extensive simulation experiments to evaluate network performance in different deployment environments. The results show that the proposed DHFSOA performs well in terms of coverage efficacy, energy efficiency, and convergence rate of the event set.

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The self-deployment of nodes with non-uniform coverage in underwater acoustic sensor networks (UASNs) is challenging because it is difficult to access the three-dimensional underwater environment. The problem is further complicated if network connectivity needs to be considered. In order to solve the optimization problem of sensor network node deployment, we propose a maximum coverage and connectivity self-deployment algorithm that is based on ant colony optimization (MCC-ACO). We carry out the greedy strategy, improve the path selection probability and pheromone update system, and propose a self-deployment algorithm based on the foundation of standard ant colony optimization algorithms, so as to achieve energy-saving optimization coverage of target events. The main characteristic of the MCC-ACO algorithm is that it fully considers the effects of the changes in the event quantities and the random distribution of the nodes on the deployment effect of the nodes, ensures that every deployed node can be connected to the sink, and achieves the matching of node distribution density and event distribution. Therefore, the MCC-ACO algorithm has great practical value. A large number of comparative simulation experiments show that the algorithm can effectively realize the self-deployment problem of underwater sensor nodes. In addition, the paper also gives the impact of changes in the number of events in the network on the deployment effect.

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