Large-scale dynamic intranet environments are characterized by constantly changing configurations, evolving user behaviors, and diverse assets that increase vulnerability pathways. These factors undermine the effectiveness of Bayesian attack graphs and reveal the limitations of traditional security methods that rely on static assumptions. To address these challenges, this paper proposes a novel Bayesian attack graph method designed for large-scale, active intranet security assessments. It captures real-time intranet changes by extracting system audit logs and generates attack graphs with MulVAL, ultimately resulting in a time-spanning understanding of potential security risks. Furthermore, it identifies direct-risk paths by eliminating weak dependencies between actions and estimates the likelihood of action execution based on expectations, thereby substantially reducing the computational complexity of Bayesian security analysis. To validate the proposed method, this paper conducts dynamic threat modeling and quantitative security analysis on an enterprise intranet using logs from over 1,000 hosts. The results demonstrate that the proposed method not only provides internal network security risk values at any given time but also identifies specific and observable potential attack paths. Furthermore, this study provides a reference framework for prioritizing vulnerability remediation based on changes in internal network security conditions

Blockchain phishing scams cause billions in annual losses, yet extreme data imbalance severely hampers existing detection algorithms. Current dynamic graph generation methods fragment structures and generate erroneous connections, failing to capture local dynamic patterns vital for node classification. This raises critical questions: Can models minimize isolated subgraph generation? How can they learn and replicate structured, recurring interaction patterns? To answer these questions, we introduce GraphFlowGen, an end-to-end deep generative framework. To minimize isolated subgraph generation, GraphFlowGen employs a novel preprocessing module that jointly extracts structural and temporal contexts from transaction data, preventing fragmentation and information loss. To learn and replicate structured interaction patterns, it incorporates a Transformer encoder with Graph Attention Networks (GAT) to capture node connection dynamics and temporal evolution. To ensure high fidelity while reducing erroneous links, a reinforcement learning (RL) mechanism iteratively refines generated graph structures. Empirical validation on three real-world datasets demonstrates the effectiveness of our algorithm in local dynamic graph generation and its utility for downstream phishing detection tasks.

The utilization of Named Data Network (NDN) in Vehicle Autonomous Network (VANET) has emerged as a prominent research area, aiming to enhance data transmission and distribution. However, effectively addressing the mobility issues in NDN-based VANET poses significant challenges. Existing Q-learning-based geographic routing methods suffer from slow convergence and heavy reliance on dynamic Q-value tables. Moreover, local information-based next-hop selection does not always prioritize optimal global routing forwarding. To overcome these limitations, this study presents a novel routing strategy for in-vehicle named data networks, employing the Deep Prioritized Sarsa algorithm. The proposed approach incorporates fuzzy logic techniques and depth-first search algorithms to obtain and utilize comprehensive global road information. Additionally, it maintains an adaptive fixed-size Q-value table with a customized reward function for vehicle node selection. Simulation results showcase substantial improvements in terms of packet hit rate and end-to-end delay. This research contributes to the advancement of efficient and effective routing strategies for NDN-based VANET, addressing the challenges associated with mobility and data transmission in vehicular networks.

With the rapid expansion of the Internet of Medical Things (IoMT) and cloud computing, ensuring secure communication in e-health systems has become increasingly critical. However, many existing authentication solutions suffer from excessive overhead and security vulnerabilities. To address these challenges, we present a lightweight, high-speed authentication protocol that relies on secure hash functions and XOR operations, facilitating efficient mutual authentication among users, trusted servers, and medical servers while establishing session keys for data exchange. We then rigorously assess our protocol's security against a comprehensive threat model, employing both informal methods and formal analyses, including Real-Or-Random (ROR) model, BAN logic, and automated verification via ProVerif. The results demonstrate that our protocol remains resilient against known attacks and satisfies e-health security standards. Furthermore, a detailed performance comparison reveals that our approach significantly reduces some costs compared to existing schemes, while reinforcing security and privacy protections.

The rapid expansion of mobile communication networks has led to a surge in cellular traffic, highlighting the need for advanced prediction models to improve network performance. Federated learning (FL) offers a promising solution by enabling distributed model training across multiple nodes, aligning well with the decentralized nature of modern networks. However, applying FL to spatiotemporal cellular traffic prediction is challenging due to the substantial communication overhead in distributed learning. To address this, we propose LFedSG, a lightweight FL framework incorporating subgraph partitioning for spatiotemporal traffic prediction. LFedSG supports collaborative training while preserving inter-client dependencies critical for accurate prediction. Communication efficiency is achieved by focusing on essential model parameters, while subgraph partitioning and spatiotemporal graph convolutional networks (STGCN) enhance spatial and temporal correlation modeling. An adaptive transmission weight pruning strategy further reduces communication and computation costs. Extensive experiments on the Telecom Italia and Pems07 datasets demonstrate that LFedSG achieves higher predictive accuracy than traditional methods, with significant reductions in communication overhead and training time, validating its effectiveness and scalability for large-scale mobile network environments.

Network scanning is crucial for both network management and cybersecurity. However, due to the vast address space of IPv6, brute-force scanning is infeasible. Seed-based target generation algorithms have recently attracted considerable research attention. However, existing target generation algorithms lack a deeper exploration of patterns, leading to poor capture of dense regions and consequently low hitrate. To address this issue, we propose 6Global, a dynamic IPv6 active address scanning method assisted by global perspective. 6Global first performs rapid clustering of seed addresses based on their descriptive attributes. Then, for each cluster, patterns are generated in a bottom-up manner based on entropy, using subranges to represent patterns and resulting in denser patterns. Finally, dynamic scanning is conducted using these patterns. During scanning, the reward of each pattern is dynamically adjusted based on its active density and global statistics, which enhances the capability in capturing dense regions. Experimental results on six seed datasets show that 6Global overall outperforms seven baseline methods and demonstrates significant advantages across multiple datasets.

The decentralized and immutable nature of blockchainprovides a resilient foundation for covert communication in adversarial and untrusted environments, specifically in scenarios requiring unobservable multi-recipient messaging. Most existing schemes, however, are limited to one-to-one transmission and lack mechanisms to handle untrusted receivers, which constrains their scalability and security. To address these challenges, we propose ORCA (Orthogonal Covert Architecture), a group covert communication framework based on strictly orthogonal, integer-valued codewords. ORCA selects codewords from a Hadamard matrix and applies secret column permutations to ensure decoding isolation and resistance against inference attacks. Each receiver recovers only its assigned message through projection, without coordination or leakage. This encoding structure supports scalable embedding, seamless receiver revocation, and clean integration with standard transaction fields. In contrast to prior work, we analyze the impact of imperfect orthogonality and provide theoretical bounds on decoding interference. Extensive experiments on real-world Bitcoin blockchain data and comparative evaluation against representative covert communication schemes confirm ORCA’s robustness, high embedding capacity, and statistical indistinguishability from normal blockchain activity. These results establish ORCA as a scalable and secure solution for multi-recipient covert communication in adversarial environments.

In recent years, with the explosive growth in the number of Internet of Things (IoT) devices, the demand for content transmission over the network has increased rapidly, making Named Data Networks (NDN) a rising research focus. The in-network caching mechanism of NDN plays a significant role in enhancing network efficiency, thus making the development of efficient caching strategies particularly crucial. This paper proposes a dynamic clustering caching strategy for IoT-NDN based on user-preferred contents and the SDN-NDN architecture, aimed at maximizing cache hit rates and minimizing transmission latency. We initially designed a dynamic clustering mechanism that takes into account the preferences of individual nodes and their neighboring nodes, allowing for dynamic adjustment of node clusters. Subsequently, we constructed a Graph Attention Network-Deep Reinforcement Learning (GAT-DRL) agent, which uses node preference information to make caching decisions for each node. Simulation results indicate that compared to existing typical caching strategies, the proposed strategy significantly improves in terms of cache hit rates, transmission latency, and path stretch ratios.

Vehicular Named Data Networking (VNDN) is a network architecture that supports various content-oriented applications in high-dynamic topology environments. Its inherent in-network caching helps with content delivery and communication efficiency in vehicular networks. Due to the variability in vehicle mobility, the change of relay nodes makes data packet transmission difficult. Additionally, frequent link interruptions and packet retransmissions lead to increased network load and reduced quality of service for users. To address these issues, this paper proposes a novel vehicle clustering-based cooperative caching strategy. The strategy first utilizes Recurrent Neural Networks (RNN) to predict vehicle connectivity and clusters vehicles into different groups based on these predictions. Concurrently, we construct a popularity prediction model based on the multi-head attention mechanism to forecast content trends. Furthermore, we introduce the concept of importance levels, allowing nodes of different levels to cache content of corresponding popularity levels. Performance evaluation results show that the proposed scheme outperforms existing strategies in terms of cache hit rate, latency, and link load.

Ethereum, as one of the most active cryptocurrency trading platforms, has garnered significant academic interest due to its transparent and accessible transaction data. In recent years, phishing scams have emerged as a serious criminal activity on Ethereum. Although most studies model Ethereum account transactions as networks and analyze them using traditional machine learning or network representation learning techniques, these approaches often rely solely on the latest static transaction records or use manually designed features while neglecting transaction histories, thus failing to fully capture the dynamic interactions and potential trading patterns between accounts. This article introduces an innovative multiperspective cascaded dynamic graph neural network model named DMPCG, which extracts phishing transaction data from authoritative databases like blockchain explorers to construct transaction network graphs. The model elevates the analysis from the microscopic features of nodes to the macroscopic dynamics of the entire network, integrating the attributes of static snapshot graphs with the evolution of dynamic trading networks, significantly enhancing the accuracy of phishing detection. Experimental results demonstrate that the DMPCG method achieves an impressive precision of 92.6% and an F1-score of 90.9%, outperforming existing baseline models and traditional subgraph sampling techniques.

Vehicular Ad-hoc Networks (VANETs) face significant security and privacy challenges in modern intelligent transportation systems. This paper analyzes vulnerabilities in Al-Shareeda et al.'s vehicle authentication protocol (doi: 10.1109/TDSC.2025.3553868) and proposes an enhanced ECC-based scheme using short-lived pseudonymous certificates. We identify two critical weaknesses in Al-Shareeda et al.'s protocol—a desynchronization attack causing potential denial-of-service and an identity linking attack compromising vehicle privacy. Our protocol establishes mutual authentication between vehicles and roadside units, ensuring message integrity, anonymity, and perfect forward secrecy. Unlike existing approaches, it eliminates the need for online third-party authenticators. Formal security proofs demonstrate that the scheme's security is reducible to the hardness of the ECDLP and CDH problems. Performance analysis shows our approach achieves an optimal security-efficiency balance with competitive communication overhead (4608 bits) and computation costs (5.02 ms) compared to state-of-the-art alternatives, while uniquely satisfying all twelve evaluated security properties.

In this article, our primary focus is on the current lack of privacy-preserving queries tailored to real-number fields rather than integers. We take multisource private data counting on real numbers (R-PDC) within IoT architecture as a breakthrough point to enable diverse query services without revealing sensitive data. The advantage of this is that the parameters required for queries remain stable and minimal in the scenario of wide numerical domain and dynamic changed data set. First, we present a general R-PDC method based on curve approximation, in which an approximate query function (AQF) is established to approximate the constructed ideal counting curve for element queries on target set. We also demonstrate curve construction process of AQF and provide the feasibility theorem of AQF for counting a target element within an allowable error. By integrating the R-PDC method with fixed-point fully homomorphic encryption, an efficient R-PDC scheme is presented to perform multiparty collaborative queries in IoT. In security aspect, the R-PDC scheme on$(m,\epsilon)$-AQF is proved to be statistically secure against chosen element attack (CEA) for cumulative error$\epsilon $and data set size$m$. Moreover, the scheme achieves$O(nm\gamma)$computation and$O(n^{2}m\gamma)$communication complexities for$n$servers and$\gamma $-length fraction. Finally, as an extension of AQF over single attribute, multidimensional R-PDC method is applied into privacy-preserving Naive Bayes classifier and Apriori algorithm over multiple attributes. Our work provides substantial support and insights for the advancement of privacy computation.

To face urgent concern of privacy leakage on mobile crowdsourcing, some Lattice-Based Cryptographic (LBC) schemes have been applied to the cloud-fog-edge data sharing platform for privacy protection. As an important factor of LBC’s security, current key generation usually involves Preimage Gaussian Sampling for Lattice Trapdoor (PGS-LT) to sample short preimage vector from dual lattice. However, there lacks researches on the implementation of PGS-LT according to entities’ computation and storage capacities. To address this issue, we present a fast double-perturbation scheme that is applied to the cloud-fog-edge data sharing platform. Firstly, we design a fast spherical G-lattice sampling algorithm including two samplers: G-perturbation sampler and G-lattice sampler. Among them, the fast non-spherical G-lattice sampling algorithm is extended to arbitrary bases, and deployed on the G-lattice sampler. Meanwhile, the G-perturbation sampler is designed to sample G-perturbation for converting the non-spherical distribution of output G-lattice vector to the spherical one. Secondly, we optimize the assignment of computational tasks in PGS-LT by considering entities’ abilities in the cloud-fog-edge platform. Moreover, we analyze three types of delegated preimage sampling in terms of Gaussian quality and complexity. The analysis and experimental results show that fast spherical G-lattice sampling provides high Gaussian quality of output vector. Meanwhile, in the aspect of complexity, the G-perturbation sampler has lower time & space complexity than the existing works. The G-lattice sampler remains good performance as it only involves extra integer multiplications in linear time complexity.

Spatial crowdsourcing (SC) has become a fundamental and emerging technology in Metaverse, facilitating the creation of immersive experiences through location-based services. In these systems, a central SC server leverages SC workers who physically travel to task locations to gather spatiotemporal environment data. However, conventional SC systems face two significant challenges: (1) the SC server, functioning as a centralized authority, can sometimes be unreliable, either due to intentional or unintentional misconduct, (2) to ensure efficient task assignment and validation, the location privacy of tasks and workers is openly accessible. In this study, we formally define location privacy preserved proof generation and verification problem (LP-PGVP) within an SC task matching scenario, with the aim to the above two challenges. Our proposed solution is a blockchain-based SC system (BlockSC), which provides a decentralized platform for task requesters and workers in the Metaverse context through calling smart contracts. We also introduce a ciphertext-based task matching scheme where task location access is granted only to eligible workers executing a task, benefiting from the design of geographic coordinate transformation and bilinear mapping methodology. To further demonstrate the task matching scheme’s operation and impact, we present an easy-to-understand case study. Our evaluation findings confirm that the proposed system effectively maintains location privacy for both SC workers and task requesters, without a considerable sacrifice in task matching efficiency.

With the rapid scale expansion of the Internet of Things (IoT), the probability of system failure increases. Frequent system failures degrade the quality of service (QoS) of IoT. Existing optimization strategies utilize reinforcement learning (RL) to enhance the robustness of IoT topology. However, due to the increasing scale of the IoT environment, the unbalanced exploration and exploitation of RL agents make it prone to premature convergence at the local optimum. Large-scale action spaces and state spaces lead to a sparse reward problem, which reduces the convergence efficiency of the algorithm. This paper proposes an evolutionary reinforcement learning scheme for IoT robustness to solve the above problems. We design a multi- agent evolution mechanism to provide multiple experiences for RL, which strengthens exploration capability. We present new evolution operators to promote convergence, which combine dis- tillation crossover and Gaussian mutation. Extensive experiments show that our scheme has a strong exploration capability, and the optimization rate of IoT topology robustness reaches 81.15%, which outperforms other robustness optimization algorithms.

As the Industrial Internet of Things (IIoT) continues to grow in scale, edge devices will generate massive amounts of data every single day. However, most of the IIoT data exists in the form of data silos, which makes it difficult to share data across domains securely. Therefore, a secured data-sharing scheme for IIoT based on blockchain and federated learning (FL) is proposed in this article. Leveraging blockchain in FL systems to enhance the tamper-proof and decentralized capabilities of IIoT devices. Model parameter validation and incentives are also added to the consensus algorithm to encourage more IIoT data owners to contribute local privacy data and arithmetic power. To address potential security issues, such as parameter leakage and inference attacks in data sharing, this article designs an adaptive differential privacy mechanism and a node contribution consensus mechanism. Without affecting the global model’s accuracy, some of the noise is also reduced. The reputation mechanism is used to resist poisoning attacks by malicious nodes. It is demonstrated on different data sets that our scheme has high global model accuracy and can effectively resist 30% model poisoning attacks.

In this article, we address the problem of data privacy in multisource data mining. To do it, we present a new multiparty fully homomorphic encryption (MP-FHE) scheme, in which all participants are completely fair to perform the same computation. At first, the proposed MP-FHE scheme is divided into five stages (i.e., calculation, configuration, recombination, resharing, and reconstruction stage) to achieve the unified computation form of addition and multiplication. Meanwhile, random bivariate polynomials and commutative encryption are used to achieve the degree reduction of polynomials and the continuity of computation. Moreover, we prove that the scheme meets result consistency and program termination under the fail-stop adversary model. Especially, three kinds of error detection criteria are presented to find errors in three different stages (i.e., recombination, resharing, and reconstruction stage), which provides the monitor basis for the fail-stop adversary model. In addition, the MP-FHE scheme is applied into privacy preserving k-means clustering algorithm. Finally, we evaluate the computation and communication performance of our scheme from both theoretical and experimental aspects, and the evaluation results show that the scheme is efficient enough for multisource data mining.

Optimal Transport (OT) has become a powerful tool to compare probability distributions. However, it suffers from a severe computational burden for high dimensional and continuous distributions. To this end, we develop two novel methods for the Kantorovich and Monge formulations, which are the fundamental problems in OT. First, we learn the optimal joint distribution in the Kantorovich formulation and propose an algorithm, namely Cop-OT, which transforms the primal objective of the Kantorovich problem into a tractable objective with respect to the copula parameter. Second, based on the copula formulation of the joint distribution, we learn the optimal map in the Monge problem and propose an algorithm, namely Map-OT, which describes the optimal map using a parameterized function estimated by approximating the barycentric projection of the optimal joint distribution and then obtains a tractable objective with respect to parameters of interest. Both of them can be solved by stochastic optimization with a stable optimizing process. Empirical results demonstrate that Cop-OT and Map-OT can gain more accurate approximations of the Kantorovich and Monge problems compared with the baseline methods.

Grey wolf optimization (GWO) is a widely used meta-heuristic method. It has limited searching potential when solving the majority of function optimization problems. This paper proposes a new variant of GWO, named SCGWO, which combines GWO with an improved spread strategy and a chaotic local search (CLS) mechanism to overcome these performance limitations. In detail, a spread strategy is introduced into the basic GWO to change the search agent's ability to avoid the local optima, the global exploration capability, and the individual movement's randomness. Then, a CLS mechanism is adopted to accelerate the convergence rate of the evolving agents. This method's effectiveness is illustrated by comparing the proposed SCGWO method with various algorithms, including seven GWO variants and eight well-known state-of-the-art algorithms on a comprehensive set of benchmark functions with the type of unimodal, multimodal, and composition functions. The experimental results confirmed that the established SCGWO algorithm has apparent advantages in processing unimodal, multimodal, and composition functions. Additionally, the proposed algorithm was utilized for finding the approximate optimal feature subset when applied to the feature selection problems on a set of 32 real-world data sets from the UCI machine learning repository. The results show that the binary variant also reveals a very competitive performance in dealing with feature selection. Our findings and analysis suggest that the proposed method can be a very suitable tool for realizing the optimal solutions to global optimization and wrapper-based feature selection tasks.

Abstract In many cases, images contain sensitive information and patterns that require secure processing to avoid risk. It can be accessed by unauthorized users who can illegally exploit them to threaten the safety of people’s life and property. Protecting the privacies of the images has quickly become one of the biggest obstacles that prevent further exploration of image data. In this paper, we propose a novel privacy-preserving scheme to protect sensitive information within images. The proposed approach combines deoxyribonucleic acid (DNA) sequencing code, Arnold transformation (AT), and a chaotic dynamical system to construct an initial S-box. Various tests have been conducted to validate the randomness of this newly constructed S-box. These tests include National Institute of Standards and Technology (NIST) analysis, histogram analysis (HA), nonlinearity analysis (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), bit independence criterion strict avalanche criterion (BIC-SAC), bit independence criterion nonlinearity (BIC-NL), equiprobable input/output XOR distribution, and linear approximation probability (LP). The proposed scheme possesses higher security wit NL = 103.75, SAC ≈ 0.5 and LP = 0.1560. Other tests such as BIC-SAC and BIC-NL calculated values are 0.4960 and 112.35, respectively. The results show that the proposed scheme has a strong ability to resist many attacks. Furthermore, the achieved results are compared to existing state-of-the-art methods. The comparison results further demonstrate the effectiveness of the proposed algorithm.

Edge computing in vehicles is emerging as an essential candidate for the Internet of Vehicles (IoV) to improve traffic efficiency. The proliferation of IoV pushes the horizon of edge computing. The social features and connections among vehicles are significant for traffic efficiency solutions. However, it is quite challenging to perform collaborative edge computing (CEC) for social IoV systems because of network heterogeneity, vehicle mobility, user selfishness, privacy, and so on. This article focuses on the CEC, in the social IoV system to alleviate urban traffic congestion. Recent research reveals that intelligent traffic lights control through city-wide mobile edge computing (MEC) servers can reduce vehicles’ average waiting time at signal intersections. This article has focused on a CEC-based traffic management system (CEC-TMS) to reduce the average waiting time. It utilizes multiagent-based deep reinforcement learning (DRL) for the MEC servers that interact with IoV and traffic lights to generate dynamic green waves at congested intersections. Results demonstrate the effectiveness of the proposed system under the paradigm of multiagent DRL.

Attributable to the emergence of mobile edge computing (MEC), the hardware-constrained industrial devices have further computational and service capability in industrial Internet of Things (IIoT) systems. Nevertheless, unreliable network environments and unpredictable processing delays are intolerable factors for any service application. Therefore, this article studies the associated constraint problem of how to optimize the offloading decision and resource allocation in collaborative edge computing networks with multiple IIoT devices and MEC servers. In order to attain this purpose, the optimization problem is mathematically derived as a mixed-integer nonlinear programming problem which is a large-scale NP-hard problem. Then, an improved differential evolution algorithm (IDE) is proposed to obtain the optimal solutions in an accessible time complexity. Finally, the performance of the IDE-based resource allocation scheme has been compared with other baseline schemes. Simulation results demonstrate that the IDE-based optimization scheme could significantly reduce the system delay and energy consumption.

Existing malware classification methods cannot handle the open-ended growth of new or unknown malware well because it only focuses on pre-defined malware classes with sufficient training data. Due to the superiority of the visualization method, some researchers use it for solving few-shot malware classification. However, the malware images generated by existing visualization methods contain insufficient semantic information. At the same time, existing few-shot models tend to converge to sharp minima resulting in poor generalization performance. By synthesizing the observations, we think that accurate and effective few-shot malware classification methods are affected by generated malware images and classification models, which can be called data and model levels, respectively. To solve the above problems, we propose a novel method from the Data and Model levels, which is used to classify new or unknown malware well, called DMMal. More specifically, we propose a multi-channel malware image generation method based on multi-view so that malware images can contain more prosperous information at the data level. In addition, we investigated adaptive sharpness-aware minimization in a few-shot scenario from the perspective of model optimization at the model level to minimize the loss value and sharpness simultaneously. This enhances the generalization ability of the model and improves the ability of the model to classify new or unknown classes. Experiments on two few-shot malware classification datasets show that the method proposed can improve the performance of few-shot malware classification from the data and model levels.

With the development of Online to Offline (O2O) model and the rapid growth of service types and numbers, service matching algorithms have become the key in connecting users and services. The traditional service matching algorithms lack consideration for the limited resources of O2O services, leading to the Matthew effect more seriously. In this context, how to alleviate the Matthew effect through the optimization of matching algorithms has become an urgent problem in this field. Based on this, this paper proposes an adaptive optimization algorithm of O2O service matching to achieve the balance of supply and demand by optimizing supply, thus alleviating the Matthew effect. In addition, a computational experiment system is constructed to verify the effect of different matching algorithms on alleviating the Matthew effect. The result shows that our proposed algorithm can provide new means and ideas for alleviating the Matthew effect.

The traditional covert communication that relies on a central node is vulnerable to detection and attack. Applying blockchain to covert communication can improve the channel's anti-interference and antitampering. Whisper is the communication protocol of Ethereum, which mainly relies on payload to store information and padding to expand. These two fields can store a large amount of information, creating conditions for the realization of covert communication. In this paper, we propose a covert communication method based on the whisper protocol to covertly transfer information in the blockchain. To implement this method, we use payload to store the carrier information, matching it with the secret message. The generated index is recorded in the padding field. To improve the concealment of communication, we simulate the default filling rules of the protocol to maintain the message size. A new topic–key pair interaction method is also proposed to improve the security of the model. Moreover, the anti-interference, antitampering and antidetection of the newly proposed model are verified through theoretical analysis and experiment. The experimental findings show that the amount of information in the proposed method is 4.7 times that of the traditional time-based covert communication. The time consumption of information transmission is reduced to 52.25% under the same settings and even less in actual use. The cost of the new topic–key pair interaction is reduced by nearly 50% compared with the original method.

Internet business can be divided into two categories: pure online business and Online to Offline (O2O) business. Currently, the recommendation technology for online business is maturing, such as news, movies, products, and so forth. However, traditional recommendation technology can easily cause the overcrowding at some O2O services because of the big data traps. In the end, the users’ experience with the O2O service recommendation is useless or very poor because they have to wait for a long time and can't enjoy the service immediately. Hence, how to improve the performance of O2O service recommendation has become a vital problem. To solve the problem, this paper proposes a research framework based on the continuous feedback learning mechanism between cyber layer and social layer. Then, the continuous feedback ideas are implemented in the design of the O2O service recommendation strategy step by step. Furthermore, the computational experiment system is constructed to perform performance analysis of these service strategies. The results show that our research framework is conductive to help O2O service recommendation to escape the big-data traps and to improve user experience.

Due to intelligent electronic management, the smart grid has recently played a significant role in modern energy infrastructure. However, along with widespread deployment of the smart grid, many potential security threats (e.g., impersonation attack, replay attack, man-in-the-middle attack) rise to the surface. To defend against these possible attacks, numerous cryptography-based authentication schemes have been proposed for the smart grid. Most of the schemes investigate the secret key distribution problem, but the requirement of decentralized registration authority is neglected. In addition, over-complicated cryptographic primitives also strengthen the burden of authentication system. In contrast with previous researches, our proposed BlockSLAP utilizes cutting-edge blockchain technology as well as smart contract to decentralize the registration authority and reduce the interaction process to 2 steps. Moreover, our protocol is proved secure under computational hard assumption and informal security analysis. Finally, experimental results show that smart grid authentication performance in our protocol has been improved compared to the other existing ECC-related schemes.

In this study, a novel reversible data hiding (RDH) in encrypted domain scheme for coding channel based on sliding‐block segmentation and adaptive steganography is proposed. The proposed scheme enriches the residual information with as little additional encryption information as possible to improve the testing error rate of a steganalyser by sliding‐block segmentation with bit stream encryption. The specific encryption process effectively weakens the correlation between the adjacent pixels and minimises the size of key stream bits. The encryption key can be further embedded in the channel code stream before transmitted in the channel. Experimental analysis shows that the image encrypted by the proposed RDH scheme can achieve a peak‐signal‐to‐noise ratio of >50 dB, as the payload is 0.5 bits per pixel (bpp). In terms of security performance, compared with the state‐of‐the‐art methods, their method has a higher testing error rate when the steganalyser is utilised. Even if the payload is 0.5 bpp, the testing error rate is >0.25.

The existence of Mobile Edge Computing (MEC) provides a novel and great opportunity to enhance user quality of service (QoS) by enabling local communication. The 5th generation (5G) communication is consisting of massive connectivity at the Radio Access Network (RAN), where the tremendous user traffic will be generated and sent to fronthaul and backhaul gateways, respectively. Since fronthaul and backhaul gateways are commonly installed by using optical networks, the bottleneck network will occur when the incoming traffic exceeds the capacity of the gateways. To meet the requirement of real-time communication in terms of ultralow latency (ULL), these aforementioned issues have to be solved. In this paper, we proposed an intelligent real-time traffic control based on MEC to handle user traffic at both gateways. The method sliced the user traffic into four communication classes, including conversation, streaming, interactive, and background communication. And MEC server has been integrated into the gateway for caching the sliced traffic. Subsequently, the MEC server can handle each user traffic slice based on its QoS requirements. The evaluation results showed that the proposed scheme enhances the QoS and can outperform on the conventional approach in terms of delays, jitters, and throughputs. Based on the simulated results, the proposed scheme is suitable for improving time-sensitive communication including IoT sensor’s data. The simulation results are validated through computer software simulation.

In this article, first a comprehensive study of the impact of term weighting schemes on the topic modeling performance (i.e., LDA and DMM) on Arabic long and short texts is presented. We investigate six term weighting methods including Word count method (standard topic models), TFIDF, PMI, BDC, CLPB, and CEW. Moreover, we propose a novel combination term weighting scheme, namely, CmTLB. We utilize the mTFIDF that takes into account the missing terms and the number of the documents in which the term appears when calculating the term weight. For further robust term weight, we combine mTFIDF with two weighting methods. We evaluate CmTLB against the studied weighting schemes by the quality of the learned topics (topic visualization and topic coherence), classification, and clustering tasks. We applied weighting schemes to Latent Dirichlet allocation (LDA) and Dirichlet multinomial mixture (DMM) on eight Arabic long and short document datasets, respectively. The experiment results outline that appropriate weighting schemes can effectively improve topic modeling performance on Arabic texts. More importantly, our proposed CmTLB significantly outperforms the other weighting schemes. Secondly, we investigate whether the Arabic stemming process can improve topic modeling performance. We study the three approaches of Arabic stemming including root-based, stem-based, and statistical approaches. We also train topic models with weighting schemes on documents after applying four stemmers related to different stemming approaches. The results outline that applying the stemming process not only reduces the dimensionality of term-document matrix leading to fast estimation process, but also show enhancement of topic modeling performance both on short and long Arabic documents. Moreover, Farasa stemmer achieves the highest performance in most cases, since it prevents the ambiguity that may happen because of the blind removal of the affixes such as in root-based or stem-based stemmers.

As a new form of manufacturing industry in the Internet era, social manufacturing has its inherent “social-cyber” complexity: the source of manufacturing service is social, and such sociality aggravates the diversity, uncertainty, and dynamics of service supply. This poses new challenges to the service matching between supply-side and demand-side. In order to meet this challenge, it is necessary to conduct a complexity analysis of social manufacturing. Traditional researches mainly rely on data statistics and macro analysis, in which there are difficulties in clearly identifying the links between various impact factors and macro evolution phenomena. In order to change such a situation, this paper proposes a modeling framework of social manufacturing from the aspect of social learning evolution (SLE), including individual evolution model, organizational learning model, and social learning model. Based on the SLE framework, the corresponding computational experiment system is built to analyze the complexity of social manufacturing. The performance of several evolution mechanisms in social manufacturing is simulated and compared as a case study to present the application of SLE framework. The results demonstrate that our method has a substantial promise.

In cognitive radio sensor networks (CRSNs), the sensor devices which are enabled to perform dynamic spectrum access have to frequently sense the licensed channel to find idle channels. The behavior of spectrum sensing will consume a lot of battery power of sensor devices and reduce the network lifetime. In this paper, we aim to answer the question of how many spectrum sensing nodes (SSNs) are required. In order to achieve this, SSN ratio effects on the accuracy of spectrum sensing from the perspective of network energy efficiency are analyzed first. Based on these analyses, the optimal SSN ratio is derived for maximizing the network lifetime by optimizing the cooperative detection probability (CDP). Simulation results show that the optimal SSN ratio can guarantee the spectrum sensing performance in terms of detection and false alarm probabilities and effectively extend the network lifetime.