The rapid development and spread of digital technology, information and communications technology, have led to an increasing number of individuals and organizations relying on the Internet for their daily work and life. However, a variety of emerging threats pose significant obstacles to traditional defense strategies. Network Security Situation Assessment (NSSA) is an effective measure to protect network systems from malicious attacks and provides an effective solution to protect network security. However, the existing NSSA schemes suffer from low accuracy and poor efficiency when dealing with network traffic data, which is characterized by large scale, nonlinearity, irregularity, high dimensionality, and temporal correlation. To solve these problems, we propose a novel integration of Time-Attention and residual connections, and design a NSSA scheme for network in this paper. Specifically, we first design a Time-Attention mechanism, which can achieve sufficient feature extraction with linear complexity. Subsequently, we integrate the Time-Attention and residual connection to improve the Gated Recurrent Unit (GRU) and design a novel integration of Time-Attention and residual connections neural network model called Time Attention Gated Network (TAG-Net). TAG-Net uses Time-Attention and residual connection as a reset gate and an update gate to reduce conflicts between reset and update gates. Meanwhile, we propose a TAG-Net-based NSSA scheme for network, which can improve the assessment accuracy and efficiency. Finally, we implement our proposed scheme and provide a performance evaluation. The experimental results show an accuracy of 81.87% for the NSL-KDD dataset, 98.28% for the UNSW-NB15 dataset, and 99.99% for the Bot-IoT dataset compared to the state-of-the-art models.

In scenarios with extremely harsh channel conditions and severely limited communication resources, the reliability and effectiveness of semantic communication require urgent enhancement to satisfy the increasing demands of 6G technology. To address this issue, we propose an importance prioritized framework that integrates both message importance and feature importance to identify critical semantics for reliable and efficient semantic transmission. Considering service personalization and task intelligence, we analyze the message importance by factoring the receiver’s preferences and the communication tasks requirements. Specifically, a large AI model is introduced to quantify message importance, while an importance-based metric for semantic accuracy is established to evaluate the overall reliability of semantic communication. To safeguard significant messages in harsh channel conditions, an unequal error protection strategy based on message importance is employed. Furthermore, we propose a novel approach for feature importance analysis based on loss variation to accurately identify critical features. A feature importance prediction network is designed for algorithm deployment. Additionally, a semantic compression strategy based on feature importance is utilized to prioritize the transmission of essential features in limited communication resources scenarios. Extensive experimental results demonstrate substantial performance advantages of our framework and methods, especially in low signal-to-noise ratio and communication resource shortages, providing a reliable and efficient solution for semantic communication in adverse communication environments.

The Industrial Control Network (ICN), which is characterized by real-time responsiveness and reliability, plays a key role in increasing production speed, ensuring efficient processing, and managing industrial processes. Despite tremendous advantages, ICN inevitably struggles with some challenges, such as malicious user intrusion and hacker attacks. To detect malicious intrusions in ICN, Intrusion Detection Systems (IDS) have been deployed. However, network traffic in ICN often exhibits significant temporal periodicity, and computational resources are limited on edge nodes and infrastructure gateway devices. These characteristics pose significant challenges to the design and performance of IDS. To properly solve these problems, we design a new intrusion detection method for ICN. Specifically, we first design a novel neural network model called Associative Recurrent Network (ARN), which can properly handle the relationship between previous hidden state and current input. Then, we construct a novel intrusion detection method based on the ARN, which avoids gating conflicts in traditional Recurrent Neural Network (RNN), effectively captures the temporal characteristics of ICN traffic, and maintains slightly higher computational overhead than GRU, thus demonstrating good adaptability to industrial control networks. Subsequently, through theoretical analysis of computational complexity, we demonstrate that the proposed method achieves high computational efficiency, comparable to mainstream RNN methods and superior to Transformer methods. Finally, we implement a prototype system to evaluate detection accuracy. Experimental results show that our method achieves state-of-the-art performance on the industrial control systems datasets (ICS-ADD and SWaT) and the conventional network dataset (UNSW-NB15), with average accuracies of 98.93%, 95.57%, and 98.27%, respectively.

Thanks to the advent and rapid development of fog computing, the smart grid has made great progress. In fog-based smart grid, the fog node can maintain and handle the data for the smart meter which is resource-constraint, thus improving the performance. However, the security of the data (especially for malicious data reservation) is the primary concern of the smart meter. To resist the malicious outsourced data reservation in fog-based smart grid, we propose a provable deletion method in this article. In our method, we first improve the Merkle hash tree and design a novel tree named Merkle position index hash tree (MPIHT). For maintaining the same number of data blocks, MPIHT can reduce the height of the tree since it is able to store plenty of data blocks in every leaf. At the same time, the number of data blocks in every leaf would be changeably, thus supporting data deletion operation. Subsequently, we use MPIHT to propose a secure and provable deletion method for outsourced data. Our method can guarantee data integrity and achieve provable data deletion, requiring neither a trusted third party nor any complex calculations or protocols. Moreover, we demonstrate the security analysis to formally prove that our method can meet the expected requirements. Finally, we also implement our method to assess the performance. The assessment results disclose the efficiency advantages of our method over some existing methods.