Metaheuristic algorithms are widely used in engineering problems due to their high efficiency and simplicity. However, engineering challenges often involve multiple control variables, which present significant obstacles for metaheuristic algorithms. The Crested Porcupine Optimizer (CPO) is a metaheuristic algorithm designed to address engineering problems, but it faces issues such as falling into a local optimum. To address these limitations, this article proposes three new strategies: composite Cauchy mutation strategy, adaptive dynamic adjustment strategy, and population mutation strategy. The three proposed strategies are then introduced into CPO to enhance its optimization capabilities. On three well-known test suites, the improved CPO (CAPCPO) outperforms 11 metaheuristic algorithms. Finally, comparative experiments on seven real-world engineering optimization problems demonstrate the advantages and potential of CAPCPO in solving complex problems. The multifaceted experimental results indicate that CAPCPO consistently achieves superior solutions in most cases.

To address the class imbalance issue in network intrusion detection, which degrades performance of intrusion detection models, this paper proposes a novel generative model called VAE-WACGAN to generate minority class samples and balance the dataset. This model extends the Variational Autoencoder Generative Adversarial Network (VAEGAN) by integrating key features from the Auxiliary Classifier Generative Adversarial Network (ACGAN) and the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). These enhancements significantly improve both the quality of generated samples and the stability of the training process. By utilizing the VAE-WACGAN model to oversample anomalous data, more realistic synthetic anomalies that closely mirror the actual network traffic distribution can be generated. This approach effectively balances the network traffic dataset and enhances the overall performance of the intrusion detection model. Experimental validation was conducted using two widely utilized intrusion detection datasets, UNSW-NB15 and CIC-IDS2017. The results demonstrate that the VAE-WACGAN method effectively enhances the performance metrics of the intrusion detection model. Furthermore, the VAE-WACGAN-based intrusion detection approach surpasses several other advanced methods, underscoring its effectiveness in tackling network security challenges.

Many studies indicated that ionospheric total electron content (TEC) prediction is vital for terrestrial and space-based radio-communication systems. In previous TEC prediction schemes based on RNN, they learn TEC representations from previous time steps, and each time-step made an equal contribution to a prediction. To overcome these drawbacks, we propose two improvements in our study: (1) To predict TEC with both past and future time-step, Bidirectional Gate Recurrent Unit (BiGRU) was presented to improve the capabilities. (2) To highlight critical time-step information, attention mechanism was used to provide weights to each time-step. The proposed attentional BiGRU TEC predicting method was evaluated on the publicly available data set from the Centre for Orbit Determination in Europe. We chose three geographical locations in low latitude, middle latitude, and high latitude to verify the performance of our proposed model. Comparative experiments were conducted using Deep Neural Network (DNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Term memory (BiLSTM), and Gated Recurrent Unit (GRU). Experimental results show that the proposed Attentional BiGRU model is superior to the other models in the selected nine regions. In addition, the paper discussed the effects of latitudes and solar activities on the performance of Attentional BiGRU model. Experimental results show that the higher the latitude, the higher the prediction accuracy of our proposed model. Experimental results also show that in the middle latitude, the prediction accuracy of the model is less affected by solar activity, and in other areas, the model is greatly affected by solar activity.

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model.

In recent years, social networks have developed rapidly and have become the main platform for the release and dissemination of fake news. The research on fake news detection has attracted extensive attention in the field of computer science. Fake news detection technology has made many breakthroughs recently, but many challenges remain. Although there are some review papers on fake news detection, a more detailed picture for carrying out a comprehensive review is presented in this paper. The concepts related to fake news detection, including fundamental theory, feature type, detection technique and detection approach, are introduced. Specifically, through extensive investigation and complex organization, a classification method for fake news detection is proposed. The datasets of fake news detection in different fields are also compared and analyzed. In addition, the tables and pictures summarized here help researchers easily grasp the full picture of fake news detection.

The prediction of the total electron content (TEC) in the ionosphere is of great significance for satellite communication, navigation and positioning. This paper presents a multiple-attention mechanism-based LSTM (multiple-attention Long Short-Term Memory, MA-LSTM) TEC prediction model. The main achievements of this paper are as follows: (1) adding an L1 constraint to the LSTM-based TEC prediction model—an L1 constraint prevents excessive attention to the input sequence during modelling and prevents overfitting; (2) adding multiple-attention mechanism modules to the TEC prediction model. By adding three parallel attention modules, respectively, we calculated the attention value of the output vector from the LSTM layer, and calculated its attention distribution through the softmax function. Then, the vector output by each LSTM layer was weighted and summed with the corresponding attention distribution so as to highlight and focus on important features. To verify our model’s performance, eight regions located in China were selected in the European Orbit Determination Center (CODE) TEC grid dataset. In these selected areas, comparative experiments were carried out with LSTM, GRU and Att-BiGRU. The results show that our proposed MA-LSTM model is obviously superior to the comparison models. This paper also discusses the prediction effect of the model in different months. The results show that the prediction effect of the model is best in July, August and September, with the R-square reaching above 0.99. In March, April and May, the R-square is slightly low, but even at the worst time, the fitting degree between the predicted value and the real value still reaches 0.965. We also discussed the influence of a magnetic quiet period and a magnetic storm period on the prediction performance. The results show that in the magnetic quiet period, our model fit very well. In the magnetic storm period, the R-square is lower than that of the magnetic quiet period, but it can also reach 0.989. The research in this paper provides a reliable method for the short-term prediction of ionospheric TEC.