With the rapid development of Internet technology, the number of global Internet users is rapidly increasing, and the scale of the Internet is also expanding. The huge Internet system has accelerated the spread of bad information, including bad images. Bad images reflect the vulgar culture of the Internet. They will not only pollute the Internet environment and impact the core culture of society but also endanger the physical and mental health of young people. In addition, some criminals use bad images to induce users to download software containing computer viruses, which also greatly endanger the security of cyberspace. Cyberspace governance faces enormous challenges. Most existing methods for classifying bad images face problems such as low classification accuracy and long inference times, and these limitations are not conducive to effectively curbing the spread of bad images and reducing their harm. To address this issue, this paper proposes a classification method (RepVGG-SimAM) based on RepVGG and a simple parameter-free attention mechanism (SimAM). This method uses RepVGG as the backbone network and embeds the SimAM attention mechanism in the network so that the neural network can obtain more effective information and suppress useless information. We used pornographic images publicly disclosed by data scientist Alexander Kim and violent images collected from the internet to construct the dataset for our experiment. The experimental results prove that the classification accuracy of the method proposed in this paper can reach 94.5% for bad images, that the false positive rate of bad images is only 4.3%, and that the inference speed is doubled compared with the ResNet101 network. Our proposed method can effectively identify bad images and provide efficient and powerful support for cyberspace governance.

The imbalance between normal and attack samples in the industrial control systems (ICSs) network environment leads to the low recognition rate of the intrusion detection model for a few abnormal samples when classifying. Since traditional machine learning methods can no longer meet the needs of increasingly complex networks, many researchers use deep learning to replace traditional machine learning methods. However, when a large amount of unbalanced data is used for training, the detection performance of deep learning decreases significantly. This paper proposes an intrusion detection method for industrial control systems based on a 1D CWGAN. The 1D CWGAN is a network attack sample generation method that combines 1D CNN and WGAN. Firstly, the problem of low ICS intrusion detection accuracy caused by a few types of attack samples is analyzed. This method balances the number of various attack samples in the data set from the aspect of data enhancement to improve detection accuracy. According to the temporal characteristics of network traffic, the algorithm uses 1D convolution and 1D transposed convolution to construct the modeling framework of network traffic data of two competing networks and uses gradient penalty instead of weight cutting in the Wasserstein Generative Adversarial Network (WGAN) to generate virtual samples similar to real samples. After a large number of data sets are used for verification, the experimental results show that the method improves the classification performance of the CNN and BiSRU. For the CNN, after data balancing, the accuracy rate is increased by 0.75%, and the accuracy, recall rate and F1 are improved. Compared with the BiSRU without data processing, the accuracy of the s1D CWGAN-BiSRU is increased by 1.34%, and the accuracy, recall and F1 are increased by 7.2%, 3.46% and 5.29%.

As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.

Network-security situation prediction is a crucial aspect in the field of network security. It is primarily achieved through monitoring network behavior and identifying potential threats to prevent and respond to network attacks. In order to enhance the accuracy of situation prediction, this paper proposes a method that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU), while also incorporating an attention mechanism. The model can simultaneously handle the spatial and temporal features of network behavior and optimize the weight allocation of features through the attention mechanism. Firstly, the CNN’s powerful feature extraction ability is utilized to extract the spatial features of the network behavior. Secondly, time-series features of network behavior are processed through the GRU layer. Finally, to enhance the model’s performance further, we introduce attention mechanisms, which can dynamically adjust the importance of different features based on the current context information; this enables the model to focus more on critical information for accurate predictions. The experimental results show that the network-security situation prediction method, which combines a CNN and a GRU and introduces an attention mechanism, performs well in terms of the fitting effect and can effectively enhance the accuracy of situation prediction.

Wireless traffic prediction can help operators accurately predict the usage of wireless networks, and it plays an important role in the load balancing and energy saving of base stations. Currently, most traffic prediction methods are centralized learning strategies, which need to transmit a large amount of traffic data and have timeliness and data privacy issues. Federated learning, as a distributed learning framework with no client data sharing and multi-client collaborative training, can solve such problems. We propose a federated learning wireless traffic prediction framework based on mutual information clustering (FedMIC). First, a sliding window scheme is used to construct the raw data into adjacent and periodic dual-traffic sequences and capture their traffic characteristics separately to enhance the client model learning capability. Second, clients with similar traffic data distributions are clustered together using a mutual information-based spectral clustering algorithm to facilitate the capture of the personalized features of each clustered model. Then, models are aggregated using a hierarchical aggregation architecture of intra-cluster model aggregation and inter-cluster model aggregation to address the statistical heterogeneity challenge of federated learning and to improve the prediction accuracy of models. Finally, an attention mechanism-based model aggregation algorithm is used to improve the generalization ability of the global model. Experimental results show that our proposed method minimizes the prediction error and has superior traffic prediction performance compared to traditional distributed machine learning methods and other federated learning methods.

Nowadays, the industrial Internet is developing rapidly, but at the same time it faces serious information security risks. At present, industrial Internet data generally have the problems of complex attack sample types, large numbers, and high feature dimensions. When training a model, the complexity and quantity of attack samples will result in a long detection time for the intrusion detection algorithm, which will fall short of the system’s real-time performance. Due to the high feature dimension of the data, shallow feature extraction will be unable to extract the data’s more significant features, which will render the model’s overall detection capacity insufficient. Aiming at the above problems, an industrial Internet intrusion detection method based on Res-CNN-SRU is proposed. This method not only considers the temporality of network traffic data but can also effectively capture the local features in the data. The dataset used in the experiment is the gas pipeline industry dataset proposed by Mississippi State University in 2014. Experiments show that the algorithm can effectively improve the recognition rate of the system and reduce the false-alarm rate. At the same time, the training time required for this method is also greatly shortened, and it can perform efficient intrusion detection on the industrial Internet.