Computing offloading optimization for energy saving is becoming increasingly important in low-Earth orbit (LEO) satellite-terrestrial integrated networks (STINs) since battery techniques has not kept up with the demand of ground terminal devices. In this paper, we design a delay-based deep reinforcement learning (DRL) framework specifically for computation offloading decisions, which can effectively reduce the energy consumption. Additionally, we develop a multi-level feedback queue for computing allocation (RAMLFQ), which can effectively enhance the CPU's efficiency in task scheduling. We initially formulate the computation offloading problem with the system delay as Delay Markov Decision Processes (DMDPs), and then transform them into the equivalent standard Markov Decision Processes (MDPs). To solve the optimization problem effectively, we employ a double deep Q-network (DDQN) method, enhancing it with an augmented state space to better handle the unique challenges posed by system delays. Simulation results demonstrate that the proposed learning-based computing offloading algorithm achieves high levels of performance efficiency and attains a lower total cost compared to other existing offloading methods.

As the demand for global internet connectivity continues to grow, the satellite-terrestrial integrated networks (STINs) have become more and more crucial for expanding the service coverage and enhancing the network performance. However, the task offloading problem in STINs faces many significant challenges such as high processing latency and energy consumption. The current intelligent offloading strategies often rely on the real-time interactions with the environments which not only consume valuable satellite resources but also cause irreversible damage to the satellite equipment due to some operational errors. To address these issues, in this paper, we propose an offline deep reinforcement learning (offline DRL) approach to learn and optimize the task offloading decisions by leveraging the stored historical decision data and employing the soft actor-critic (SAC) algorithm specifically. Experimental results show that the proposed strategy outperforms most of the existing methods in terms of latency and energy consumption and effectively reduces the direct interactions with STINs.

In data‐driven big data security analysis, knowledge graph‐based multisource heterogeneous threat data organization, association mining, and inference analysis attach increasinginterest in the field of cybersecurity. Although the construction of knowledge graph based on deep learning has achieved great success, the construction of a largescale, high‐quality, and domain‐specific knowledge graph needs a manual annotation of large corpora, which means it is very difficult. To tackle this problem, we present a straightforward active learning strategy for cybersecurity entity recognition utilizing deep learning technology. BERT pre‐trained model and residual dilation convolutional neural networks (RDCNN) are introduced to learn entity context features, and the conditional random field (CRF) layer is employed as a tag decoder. Then, taking advantages of the output results and distribution of cybersecurity entities, we propose an active learning strategy named TPCL that considers the uncertainty, confidence, and diversity. We evaluated TPCL on the general domain datasets and cybersecurity datasets, respectively. The experimental results show that TPCL performs better than the traditional strategies in terms of accuracy and F1. Moreover, compared with the general field, it has better performance in the cybersecurity field and is more suitable for the Chinese entity recognition task in this field.

The resource-constrained edge devices are struggling with the rising computational demands of modern user applications with many dependent tasks. In this paper, we investigate the dependent task offloading strategy by constructing graph theory for the dependent tasks so that the explicit priority relations based on deep reinforcement learning (DRL) are drawn. In particular, we propose a novel task-priority-transformer dependent task offloading (TPTDTO) method with the refined priority features of dependent tasks and the application context information. We first construct a priority representation for the dependent tasks which enables the efficient task offloading and energy consumption reduction. To improve the intelligent offloading decision efficiency, a proximal policy optimization (PPO) method is utilized to interact with the multi-access edge computing (MEC) system. Specifically, we leverage the enhanced representation capability of the transformer model to encode the dynamic MEC application topology and utilize a deep neural network to capture the task priority features. Moreover, the transformer model is adopted to address the scalability issue. Simulation results demonstrate that the proposed algorithm can achieve competitive performance compared with that of the existing baselines for dependent task offloading.

The widespread adoption of computation-and communication-intensive applications, such as object detection, VR/AR, and telemedicine, has significantly alleviated transmission pressure on backbone networks and improved user experience. However, efficiently managing and computing these tasks on user sides remains a significant challenge, particularly under resource-constrained conditions. To address this problem, we propose a new service caching decision method based on Deep Dueling Double Q-Network (D3QN) by employing a learnable policy to handle the unknown task requests and determine the optimal caching strategies. Additionally, the limited storage capacity of edge servers (ES) is mitigated by forwarding the resource-intensive or infrequently requested tasks to the cloud data centers (CDC). The channel selection problem is modeled as a multi-user game and a distributed method is developed to achieve the Nash Equilibrium (NE). Simulation results demonstrate that the proposed method outperforms the existing benchmarks, showcasing its effectiveness in managing complex, dynamic environments.