The cloud boundary network environment is characterized by a passive defense strategy, discrete defense actions, and delayed defense feedback in the face of network attacks, ignoring the influence of the external environment on defense decisions, thus resulting in poor defense effectiveness. Therefore, this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent, designs the network structure of the intelligent agent attack and defense game, and depicts the attack and defense game process of cloud boundary network; constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment, and portrays the interaction process between intelligent agent and environment; establishes the reward mechanism based on the attack and defense gain, and encourage intelligent agents to learn more effective defense strategies. the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics, interaction lag, and control dispersion in the defense decision process of cloud boundary networks, and improve the autonomy and continuity of defense decisions.

n the scenario of large-scale data ownership transactions, existing data integrity auditing schemes are faced with security risks from malicious third-party auditors and are inefficient in both calculation and communication, which greatly affects their practicability. This paper proposes a data integrity audit scheme based on blockchain where data ownership can be traded in batches. A data tag structure which supports data ownership batch transaction is adopted in our scheme. The update process of data tag does not involve the unique information of each data, so that any user can complete ownership transactions of multiple data in a single transaction through a single transaction auxiliary information. At the same time, smart contract is introduced into our scheme to perform data integrity audit belongs to third-party auditors, therefore our scheme can free from potential security risks of malicious third-party auditors. Safety analysis shows that our scheme is proved to be safe under the stochastic prediction model and k-CEIDH hypothesis. Compared with similar schemes, the experiment shows that communication overhead and computing time of data ownership transaction in our scheme is lower. Meanwhile, the communication overhead and computing time of our scheme is similar to that of similar schemes in data integrity audit.

Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all participants. However, single-signature applications in transactions can lead to user identification issues due to the reuse of public keys. To address this issue, group signatures can be used, where the same group public key is used to verify signatures from group members to provide anonymity to users. However, in dynamic groups where membership may change, an attack can occur where a user who has left the group can disguise themselves as a group member by leaking a partial key. This problem cannot be traced back to the partial key leaker. In this paper, we propose assigning different partial keys to group members to trace partial key leakers and partially alleviate the damage caused by partial key leaks. Exist schemes have shown that arbitrary tracing issues occurred when a single administrator had exclusive key generation and tracing authority. This paper proposes a group signature scheme that solves the synchronization problem by involving a threshold number of TMs while preventing arbitrary tracing by distributing authority among multiple TMs.

Wireless sensor networks have been deployed in areas such as healthcare, military, transportation and home automation to collect data and forward it to remote users for further processing. Since open wireless communication channels are utilized for data transmissions, the exchanged messages are vulnerable to various threats such as eavesdropping and message falsifications. Therefore, many security solutions have been introduced to address these challenges. However, the resource-constrained nature of the sensor nodes makes it inefficient to deploy the conventional security schemes which require long keys for improved security. Therefore, lightweight authentication protocols have been presented. Unfortunately, majority of these schemes are still insecure while others incur relatively higher energy, computation, communication and storage complexities. In this paper, a protocol that deploys only lightweight one-way hashing and exclusive OR operations is presented. Its formal security analysis using Real-or Random (ROR) model demonstrates its capability to uphold the security of the derived session keys. In addition, its semantic security evaluation shows that it offers user privacy, anonymity, untraceability, authentication, session key agreement and key secrecy. Moreover, it is shown to resist attacks such as side-channeling, physical capture, eavesdropping, offline guessing, spoofing, password loss, session key disclosure, forgery and impersonations. In terms of performance, it has relatively lower communication overheads and improves the computation costs and supported security characteristics by 31.56% and 33.33% respectively.

Since the data samples on client devices are usually non-independent and non-identically distributed (non-IID), this will challenge the convergence of federated learning (FL) and reduce communication efficiency. This paper proposes FedQMIX, a node selection algorithm based on multi-agent reinforcement learning(MARL), to address these challenges. Firstly, we observe a connection between model weights and data distribution, and a clustering algorithm can group clients with similar data distribution into the same cluster. Secondly, we propose a QMIX-based mechanism that learns to select devices from clustering results in each communication round to maximize the reward, penalizing the use of more communication rounds and thereby improving the communication efficiency of FL. Finally, experiments show that FedQMIX can reduce the number of communication rounds by 11% and 30% on the MNIST and CIFAR-10 datasets, respectively, compared to the baseline algorithm (Favor).

To solve the current situation of low vehicle-to-pile ratio, charging pile (CP) operators incorporate private CPs into the shared charging system. However, the introduction of private CP has brought about the problem of poor service quality. Reputation is a common service evaluation scheme, in which the third-party reputation scheme has the issue of single point of failure; although the blockchain-based reputation scheme solves the single point of failure issue, it also brings the challenges of storage and query efficiency. It is a feasible solution to classify and store information on multiple chains, and at this time, reputation needs to be calculated in a cross-chain mode. Crosschain reputation calculation faces the problems of correctness verification, integrity verification and efficiency. Therefore, this paper proposes a verifiable and efficient cross-chain calculation model for CP reputation. Specially, in this model, we propose a verifiable cross-chain contract calculation scheme that adopts polynomial commitment to solve the problems of polynomial damage and tampering that may be encountered in the crosschain process of outsourced polynomials, so as to ensure the integrity and correctness of polynomial calculations. In addition, the miner selection and incentive mechanism algorithm in this scheme ensures the correctness of extracted information when the outsourced polynomial is calculated on the blockchain. The security analysis and experimental results demonstrate that this scheme is feasible in practice.

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, we study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors (LBDs). This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that real-time data performance is ensured It has been proven that the RDCL problem is NP-hard. To address this, we initially focus on studying two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Subsequently, we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL problem. Finally, the proposed algorithm is verified by extensive simulations.

Visual Dialog is a multi-modal task involving both computer vision and dialog systems. The goal is to answer multiple questions in conversation style, given an image as the context. Neural networks with attention modules are widely used for this task, because of their effectiveness in reasoning the relevance between the texts and images. In this work, we study how to further improve the quality of such reasoning, which is an open challenge. Our baseline is the Recursive Visual Attention (RVA) model, which refines the vision-text attention by iteratively visiting the dialog history. Building on top of that, we propose to improve the attention mechanism with contrastive learning. We train a Matching-Aware Attention Kernel (MAAK) by aligning the deep feature embeddings of an image and its caption, to provide better attention scores. Experiments show consistent improvements from MAAK. In addition, we study the effect of using Multimodal Compact Bilinear (MCB) pooling as a three-way feature fusion for the visual, textual and dialog history embeddings. We analyze the performance of both methods in the discussion section, and propose further ideas to resolve current limitations.

Multi-hop reasoning over language or graphs represents a significant challenge in contemporary research, particularly with the reliance on deep neural networks. These networks are integral to text reasoning processes, yet they present challenges in extracting and representing domain or commonsense knowledge, and they often lack robust logical reasoning capabilities. To address these issues, we introduce an innovative text reasoning framework. This framework is grounded in the use of a semantic relation graph and a graph neural network, designed to enhance the model’s ability to encapsulate knowledge and facilitate complex multi-hop reasoning.===Our framework operates by extracting knowledge from a broad range of texts. It constructs a semantic relationship graph based on the logical relationships inherent in the reasoning process. Beginning with the core question, the framework methodically deduces key knowledge, using it as a guide to iteratively establish a complete evidence chain, thereby determining the final answer. Leveraging the advanced reasoning capabilities of the graph neural network, this approach is adept at multi-hop logical reasoning. It demonstrates strong performance in tasks like machine reading comprehension and question answering, while also clearly delineating the path of logical reasoning.

Mobile networks are facing unprecedented challenges due to the traits of large scale, heterogeneity, and high mobility. Fortunately, the emergence of fog computing offers surprisingly perfect solutions considering the features of consumer proximity, wide-spread geographical distribution, and elastic resource sharing. In this paper, we propose a novel mobile networking framework based on fog computing which outperforms others in resilience. Our scheme is constituted of two parts: the personalized customization mobility management (MM) and the market-driven resource management (RM). The former provides a dynamically customized MM framework for any specific mobile node to optimize the handoff performance according to its traffic and mobility traits; the latter makes room for economic tussles to find out the competitive service providers offering a high level of service quality at sound prices. Synergistically, our proposed MM and RM schemes can holistically support a full-fledged resilient mobile network, which has been practically corroborated by numerical experiments.

Wireless networks have become integral to modern communication systems, enabling the seamless exchange of information across a myriad of applications. However, the inherent characteristics of wireless channels, such as fading, interference, and openness, pose significant challenges to achieving fault-tolerant consensus within these networks. Fault-tolerant consensus, a critical aspect of distributed systems, ensures that network nodes collectively agree on a consistent value even in the presence of faulty or compromised components. This survey paper provides a comprehensive overview of fault-tolerant consensus mechanisms specifically tailored for wireless networks. We explore the diverse range of consensus protocols and techniques that have been developed to address the unique challenges of wireless environments. The paper systematically categorizes these consensus mechanisms based on their underlying principles, communication models, and fault models. It investigates how these mechanisms handle various types of faults, including communication errors, node failures, and malicious attacks. It highlights key use cases, such as sensor networks, Internet of Things (IoT) applications, wireless blockchain, and vehicular networks, where fault-tolerant consensus plays a pivotal role in ensuring reliable and accurate data dissemination.

Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community, revealing security vulnerabilities and showcasing their potential in security-related tasks. This paper explores the intersection of LLMs with security and privacy. Specifically, we investigate how LLMs positively impact security and privacy, potential risks and threats associated with their use, and inherent vulnerabilities within LLMs. Through a comprehensive literature review, the paper categorizes the papers into “The Good” (beneficial LLM applications), “The Bad” (offensive applications), and “The Ugly” (vulnerabilities of LLMs and their defenses). We have some interesting findings. For example, LLMs have proven to enhance code security (code vulnerability detection) and data privacy (data confidentiality protection), outperforming traditional methods. However, they can also be harnessed for various attacks (particularly user-level attacks) due to their human-like reasoning abilities. We have identified areas that require further research efforts. For example, Research on model and parameter extraction attacks is limited and often theoretical, hindered by LLM parameter scale and confidentiality. Safe instruction tuning, a recent development, requires more exploration. We hope that our work can shed light on the LLMs’ potential to both bolster and jeopardize cybersecurity.

The emergence of the Internet of Things (IoT) has triggered a massive digital transformation across numerous sectors. This transformation requires efficient wireless communication and connectivity, which depend on the optimal utilization of the available spectrum resource. Given the limited availability of spectrum resources, spectrum sharing has emerged as a favored solution to empower IoT deployment and connectivity, so adequate planning of the spectrum resource utilization is thus essential to pave the way for the next generation of IoT applications, including 5G and beyond. This article presents a comprehensive study of prevalent wireless technologies employed in the field of the spectrum, with a primary focus on spectrum-sharing solutions, including shared spectrum. It highlights the associated security and privacy concerns when the IoT devices access the shared spectrum. This survey examines the benefits and drawbacks of various spectrum-sharing technologies and their solutions for various IoT applications. Lastly, it identifies future IoT obstacles and suggests potential research directions to address them.