Abstract In recent years, cloud-edge-end collaborative federated learning frameworks have been widely used in many scenarios and achieved good results. However, with the complexity of application requirements, the problems of device heterogeneity and data heterogeneity become more prominent. Traditional frameworks often face challenges such as uneven allocation of computational resources and inefficient training when dealing with these problems. To address this problem, this paper proposes a novel federated learning framework for multi-domain collaborative analysis of networked encrypted flows. First, we split the model training tasks, intelligently assign part of the model training tasks to terminal devices based on their performance, while the remaining model training tasks that require more computational resources are handed over to edge servers. Second, we introduce a resource scheduling scheme among edge servers to reasonably allocate model training tasks and fully utilize resources. Finally, high quality global models are obtained through a weight-enabled global model aggregation scheme. Experiments show that our proposed scheme can effectively address the impact of device heterogeneity and data heterogeneity in encrypted traffic identification in cross-domain networks, and improve the training efficiency and model performance of the overall system while ensuring data privacy and security.

Ransomware has emerged as one of the most prevalent and destructive cyber attacks confronting global organizations. By locking critical devices or encrypting essential data and then demanding payment for restoration, ransomware attacks disrupt operations, result in significant financial losses, and damage organizational reputations. In particular, zero-day ransomware attacks, which attempt to exploit previously unknown vulnerabilities, pose a severe threat to existing cyber security solutions. Due to the lack of training data, detection of zero-day ransomware attacks remains a significant challenge. This paper proposes a novel zero-day ransomware detection framework that integrates a refined Conditional Variational Autoencoder (CVAE) with a 1D Convolutional Neural Network (1D-CNN). The encoder of the CVAE model comprises a posterior network and a parallel prior network. Using variational coding, the posterior network maps behavioral features of software samples from known families into a latent space, represented by a fixed multivariate Gaussian distribution with a diagonal covariance matrix. Simultaneously, the prior network eliminates dependency on class labels while maintaining distributional consistency with the posterior network via Kullback–Leibler (KL) divergence minimization. This dual-network structure enables unified latent space mapping for both labeled and unlabeled samples, effectively narrowing distributional discrepancies between software samples from known and unknown families. The harmonized latent representations subsequently enhance the discriminative capability of the 1D-CNN classifier in detecting zero-day ransomware. The comprehensive experimental results have verified that the proposed method can effectively detect zero-day ransomware attacks.

Federated Learning (FL), as a distributed training framework, has demonstrated significant potential in preserving data privacy. However, client heterogeneity in data volume, computational resources, and communication conditions causes two critical issues: inefficient global synchronization due to stragglers, and excessive communication overhead from repeated model transmissions. To address these challenges, this paper proposes an Adaptive Federated Distillation (AFD) framework, which enhances training efficiency by dynamic local training optimization and balanced communication load. AFD dynamically adjusts the number of local training epochs based on client-specific data volume, communication conditions, and historical training time. Simultaneously, it employs federated distillation to transmit lightweight logits instead of full model parameters, thereby reducing communication costs and enabling model heterogeneity. Experimental results on the MNIST and CIFAR-10 datasets demonstrate that, compared to FedAvg, AFD reduces communication costs by 3-4 orders of magnitude when CNN or ResNet models are used. Additionally, AFD reduces client energy variance by 96.5% and training time disparity by 88.7% while maintaining model accuracy. Crucially, its model-agnostic design supports heterogeneous architectures, ensuring fairness across diverse edge devices.

Abstract With the development of electric vehicles, charging stations have garnered significant attention and progress. As a result, various charging platforms have emerged. However, due to the lack of shared charging information, issues such as low utilization of charging stations during peak hours and insufficient station availability have negatively impacted user experience. Inspired by the concept of an aggregation platform, we propose an aggregated charging platform for electric vehicles (EVs). However, challenges related to trust, such as single-point failures and the difficulty of achieving unified identity management, hinder the implementation of the aggregation model. To address these issues, we propose a blockchain-based EV aggregation charging platform model and a cross-platform identity authentication scheme. Leveraging the decentralized and highly secure characteristics of blockchain, we construct a decentralized aggregation platform and enhance the credibility and reliability of cross-platform authentication through smart contracts. Furthermore, we design a batch authentication scheme to efficiently handle a large number of authentication requests. Simulation results demonstrate that, compared to other schemes, our proposed approach improves authentication efficiency by 50–66.6%.

The advancement of cloud computing and Internet of Things (IoT) has driven progress in image-based searchable encryption technology, which meets the escalating security demands of outsourced multimedia data in IoT scenarios. However, existing encrypted image retrieval schemes still face critical challenges, such as lack of cross-domain support, low retrieval efficiency and accuracy, and absence of reliable verifiability. To address these issues, this paper proposes a verifiable and privacy-preserving cross-domain image retrieval scheme (VPCDIR) in IoT. In our scheme, the re-encryption and key transformation technologies are implemented to achieve the availability of cross-domain image retrieval, and the learning with errors (LWE)-based enhanced secure k-nearest neighbor (kNN) algorithm is used to preserve the privacy of image features. Furthermore, the hybrid index mechanism that combines clustering and locality-sensitive hashing (LSH) is designed to enhance retrieval efficiency and accuracy, and the Merkle hash tree (MHT) with the short signature realizes reliable authenticity verification of the retrieval result. Finally, formal security analysis confirms the security of our scheme. Extensive experiments on the real dataset demonstrate the efficiency and practicability of VPCDIR for cross-domain image retrieval in IoT.

In the Industrial Internet of Things (IIoT), the identification resolution system enhances communication and overall efficiency between isolated work islands, ensuring the trustworthiness and effectiveness of secure resource sharing across domains through cross-domain authentication. However, traditional identity authentication methods fail to empower users with control over their identity information and face challenges such as difficulty in tracking anonymous users, high computational overhead, and insufficient cross-domain trust. To address these issues, this paper proposes a cross-domain identity authentication scheme based on self-sovereign identity. The scheme leverages aggregate signature technology to enhance authentication efficiency, integrates blockchain technology and smart contracts to achieve cross-domain trust, and designs a mechanism for threshold identity tracking and revocation, as well as an attribute credential update mechanism to enable secure and efficient cross-domain authentication in the identification resolution system. The paper provides formal security definitions and proofs and evaluates the computational and storage efficiency of the scheme through theoretical analysis and experimental simulations. The results demonstrate that the proposed scheme offers significant advantages in resource-constrained scenarios within the IIoT.

To solve the problem of climate warming, countries around the world have paid special attention to the construction of carbon governance. Carbon emission accounting is an important policy tool to control the vented CO2. But at present, there are third-party agencies in carbon emission accounting that cannot ensure the fairness and impartiality of accounting, and there may be risks such as illegal use and leakage of sensitive information in the process of carbon emission data transmission. Therefore, We design the blockchain-based carbon emission security accounting scheme (BCESAS) and propose cross-chain verification contract to ensure the efficiency of cross-chain information accounting. In addition, bilinear pairing is used to ensure data integrity, and we encrypt private data using an improved and more secure homomorphic encryption algorithm to ensure that privacy is not leaked during the transfer of carbon emission data, which is more efficent than other homomorphic encryption algorithms. We also use reputation mechanism to regulate the behavior of carbon emission auditors. The theoretical and experimental analysis demonstrates that BCESAS can verify the integrity, correctness and privacy of cross-chain data calculation result effectively, realizing secure and reliable expansion of blockchain.

With the rapid advancement of data sharing technology, an increasing amount of data is being stored on cloud servers. To enable fine-grained access control over the data stored on cloud servers, the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) technology has been widely adopted. Recognizing that shared data and files often possess a hierarchical structure, hierarchical CP-ABE technology has been proposed recently. However, most existing schemes are restricted to single-domain data access, which limits their flexibility and universal applicability in practical applications. To address this limitation, an access control scheme based on hierarchical CP-ABE, named CDS-CP-ABE, is proposed to facilitate secure and efficient cross-domain data sharing. The scheme is capable of not only realizing fine-grained hierarchical access control within a single domain but also enabling cross-domain data sharing. Security analysis confirms that our scheme effectively resists chosen-plaintext attack. Furthermore, empirical results indicate that the time consumption associated with our scheme is lower compared to other existing schemes. • Achieve fine-grained hierarchical access control for data users. • Support cross-domain data access for data users based on their access levels. • Resist chosen-plaintext attack effectively, and reduce computational overhead compared to existing scheme.

Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization, decentralization, democratization, and digitalization. In this evolving context, energy blockchain, aiming to enhance efficiency, transparency, and security, emerges as an integrated technological solution designed to address the diverse challenges in this field. Data security is essential for the reliable and efficient functioning of energy blockchain. The pressing need to address challenges related to secure data storage, effective data management, and efficient data utilization is increasingly vital. This paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years, adopting an all-encompassing perspective that spans data storage, management, and utilization. Our work systematically evaluates and contrasts the strengths and weaknesses of various research methodologies. Additionally, this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture, specifically designed to meet the complex security requirements of multi-blockchain business environments. Concludingly, this paper identifies key directions for future research, particularly in advancing the integration of storage, management, and utilization of energy blockchain data security.

Recently, vehicles have experienced a rise in networking and informatization, leading to increased security concerns. As the most widely used automotive bus network, the Controller Area Network (CAN) bus is vulnerable to attacks, as security was not considered in its original design. This paper proposes SIDuBzip2, a traffic anomaly detection method for the CAN bus based on the bzip2 compression algorithm. The proposed method utilizes the pseudo-periodic characteristics of CAN bus traffic, constructing time series of CAN IDs and calculating the similarity between adjacent time series to identify abnormal traffic. The method consists of three parts: the conversion of CAN ID values to characters, the calculation of similarity based on bzip2 compression, and the optimal solution of model parameters. The experimental results demonstrate that the proposed SIDuBzip2 method effectively detects various attacks, including Denial of Service , replay, basic injection, mixed injection, and suppression attacks. In addition, existing CAN bus traffic anomaly detection methods are compared with the proposed method in terms of performance and delay, demonstrating the feasibility of the proposed method.

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.

In modern industrial enterprises, the application of identity resolution systems contributes to improving efficiency and simplifying production management. With the development of the Industrial Internet of Things (IIoT), integrating identity resolution and Prognostics and Health Management (PHM) has become a new trend. However, ensuring the confidentiality and integrity of enterprise identity data has become challenging due to flaws in identifier encoding design and the semi-trusted nature of identity resolution platforms. To address these issues, we propose a fine-grained access control scheme for the identity resolution system. Our scheme utilizes a novel identifier encoding method and attribute-based encryption algorithm, enabling flexible data classification and permission management for industry enterprises. Moreover, to combat potential malicious behaviors by users, such as unauthorized access or identity abuse, we leverage Blockchain technology to trace malicious users while safeguarding user privacy. The security of our scheme is formally proven under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. Comparative experiments demonstrate the advantages of our proposal in terms of time costs and storage overhead over alternative schemes.

With emerging technology, the blockchain-based Vehicle Ad-hoc Network (VANET) can alleviate traffic congestion and optimize resource management to improve the transportation system's efficiency significantly. However, since the information transmitted in VANET is distributed in an open environment, security, and privacy are now critical issues. Recently, many authentication protocols based on Tamper-Proof Devices (TPD) have been proposed to solve the above-mentioned hindrances, and most of them rely on the ideal TPD with extreme security assumptions. Moreover, when the aggregate signature verification fails, these schemes can only completely discard the aggregate signature, which is impractical for VANET. In order to solve the above problems, we propose a more realistic TPD-based identity verification scheme with a privacy protection function for the blockchain-based VANET. Specifically, it uses an offline self-update method to periodically update the data in the TPD to resist side-channel attacks. Our performance analysis shows that the proposed scheme is superior to the existing schemes regarding security and average delay, so it is more suitable for the actual blockchain-based VANET environment.

With the digital transformation of the energy industry, energy blockchain is playing an important role in application areas, such as energy data sharing and distributed power trading. In this process, the use of energy data is a top priority. Federated learning (FL) can enable the analysis and computation of energy data while protecting their privacy. However, traditional FL relies on a central server and parties involved are not fully trusted. In energy blockchain environment, FL also faces data poisoning attacks launched by energy departments, besides, the supervisory committee carrying out checking models can launch deception attacks. Therefore, we propose a game theory-based incentive mechanism for collaborative security of FL in energy blockchain environment, which can discourage nodes from taking malicious behaviors in iterative training of FL. First, we propose an FL model in energy blockchain environment, which can protect privacy and achieve collaborative security. Considering that game theory can be used to analyze the strategies of participants, we build a game model with energy departments and supervisory committee as players and design our incentive mechanism based on game theory, which is implemented by smart contracts. Even if the accuracy of model checking algorithm is low, malicious behaviors in FL can be reduced by using our incentive mechanism. In particular, we prove that our mechanism can lead game model to a Nash equilibrium (NE) that achieve collaborative security. Security analysis and experimental evaluation show that our incentive mechanism is feasible in energy blockchain with robustness, reliability, and low complexity.

As a key technology for the development of 5G networks, network slicing is developing rapidly. Although network slicing can realize the flexible division of 5G network resources and quickly customize virtual networks that meet the differentiated needs of customers, it is still difficult to determine the optimal service quality parameters in application scenarios. To solve the problem, this article designs a multichain 5G network slicing service quality computing model to calculate the service quality parameters of the network slicing. The calculated service quality parameters can be used as an adjustment basis in the negotiation of the SLA between the customer and the network operator. However, the traditional method of calculating information across chains will cause frequent information interactions and affect efficiency. Therefore, in this scheme, we deploy a smart contract on each blockchain to calculate the information, which can reduce the frequency of information transmission and improve efficiency. In addition, in order to make the calculation between smart contracts more fluent and the requirements more relevant, this article proposes to coordinate the development of smart contracts through multiple blockchains. Besides, to ensure the cross-chain security calculation, the signature by Cosi protocol and multisigncryption algorithms are used in the transmission of nonprivate information and private information in the cross-chain process, respectively. Security analysis and experimental results prove that the multichain 5G network slicing service quality computing model is feasible and efficient in practice.

With the volume of data increasing in the Internet of Things, a new business mode, where data owners share their own data to others for rewards, has emerged. Therefore, how to motivate data owners to participate in the data trading process is the main challenge. So far, lots of works focus on the motivation mechanism designing and ensure a fair distribution of profits among data owners. However, some security and privacy issues are still not well solved and the data owners are still unwilling to participate in the process. Especially, when a data provider claims rewards with its real identity for the shared data, the linkage between its real identity and the shared data will expose the participator's private information included in the shared data, such as location information. To protect user's privacy in the scenario, a privacy and efficiency-oriented data sharing mechanism for IoTs is proposed in this paper. We first propose a blockchain-based data sharing framework in which the behavior of all participants will be supervised. Then, in order to hide the real identities of data providers during the data sharing process, an anonymous certificate-based data sharing policy is proposed. At last, two novel non-interactive zero-knowledge proofs are designed to hide the identities of qualified data providers while claiming rewards to the system. Through security analysis and performance evaluation, the feasibility and effectiveness of the data sharing scheme are illustrated.

With the development of the Industrial Internet of Things, industrial control systems have become more open and intelligent. However, large numbers of unknown protocols exist in IIoT, threatening the security of IIoT devices and systems. Protocol reverse engineering extracts the grammar and semantics of the protocol by monitoring and analyzing the traffic trace or the execution process of instructions, without the need for protocol description. As the executable programs are mainly integrated into the IIoT devices and the communication traffic is relatively sparse, the traditional protocol analyzing method is not suitable for the IIoT environment. This paper proposes an improved sparse protocol parsing method of IIoT protocol based on the BPSO-vote-HMM hybrid model. The binary particle swarm optimization algorithm is introduced to expand the captured IIoT protocol message sequence, solving the problems of sparse samples in IIoT and the low efficiency of the GA-based data expansion model. Besides, we improve on the parameter training part to improve the efficiency and get better model parameters by dividing the training set into several sub-sets, conducting the parameter update parallel, and inputting the results into a voter to generate the final parameter of HMM, which is used in protocol field prediction. Finally, by combining the BPSO-based data expansion model and the protocol field parsing model based on vote-HMM, a hybrid analytical model is constructed to improve the analytical accuracy in a gradual evolutionary manner. Through a series of comparative experiments, the improved protocol field parsing model has better performance on IIoT protocol.

The advancement of Internet of Medical Things (IoMT) leading to the proliferation of electronic healthcare scenarios, with an expanding trend of hospitals and healthcare organizations employing Electronic Medical Records (EMRs) with uploading to the cloud for sharing. However, security challenges exist during the generation and uploading of medical records, such as record tampering, key attacks, etc., which will become the maximum bottleneck restricting the development of e-Health scenarios in the near future. Some scholars consider digital signature techniques, such as Attribute-based Signature, to address the security challenges but ignoring its overwhelming efficiency. In this paper, we propose a lightweight forward secure aggregate signature for e-Health scenarios. We devise a lightweight secure aggregate signature to provide unforgeability and forward security for medical records, which is the first aggregate signature scheme that enables forward security in e-Health scenarios. Furthermore, our scheme offers superior lightweight properties, requiring only 5.78ms for aggregation and 4.85ms for verification of 1000 signatures, which is significantly lower than existing schemes, especially suitable for medical systems with enormous data volume. Security analysis and experimental evaluation indicate that our scheme assures correctness, unforgeability and forward security, while fulfilling lightweight demands that outperform existing schemes.

With the development of electric vehicles, the shortage of charging piles (CPs) has gradually been exposed. In response to this situation, CP operators have taken private CPs into the shared charging system. Due to the lack of maintenance personnel for private CPs that join shared charging, users often face the problems of damaged CPs and poor service attitudes of CP owners. Reputation solutions based on third-party platforms face a problem of single-point failures and reputation solutions based on blockchain face problems of storage and query efficiency. To improve storage and query efficiency, this article proposes a multichain charging model that stores different types of information on different blockchains. However, it faces the problem of unreliable information called across chains, when calculating reputation across chains. Therefore, this article proposes a cross-chain trusted smart contract ($C_{2}T$smart contract) to ensure the authenticity, real-time, and interchain write mutual exclusion of cross-chain information, making reputation calculation in the multichain charging model more convenient and more accurate. Especially, we propose a data mutual trust mechanism based on Merkle proof to ensure the authenticity of cross-chain information and prevent forged information from participating in calculating reputation. Furthermore, we present a data structure composed of multiple counting Bloom filters (MCBFs) to verify the real time of information and filter out non-real-time information, thereby ensuring the real time of the calculated reputation. In addition, we put forward an algorithm to guarantee the interchain write mutual exclusion by hash mutexes, making the reputation calculation process more accurate and complete. The security analysis and experimental results demonstrate that$C_{2}T$smart contract is feasible in practice.

Pervasive smartphones boost the prosperity of location-based service (LBS) and the increasing data prompt LBS providers to outsource their LBS datasets to the cloud side. The privacy issues of LBS in the outsourced cloud scenario have attracted considerable interest recently. However, current schemes cannot provide sufficient privacy preservation against practical challenges and are little concerned about the data retrieval efficiency of the cloud side. Therefore, we present an efficient Privacy-Preserving LBS Query Scheme (i.e., PPQS ). In our scheme, two cloud entities are employed to store the sensitive information of the outsourced data and provide the query service, which enhances the ability of privacy preservation for sensitive information. Besides, by using the techniques of homomorphic encryption and searchable symmetric encryption, the proposed scheme supports both the type query and the range query, which can significantly improve the data retrieval efficiency of the cloud side and reduce the computation burden on the cloud side and the user side. Through detailed analysis on security and computation cost, we show the enhanced ability of privacy preservation and the lower computation cost compared to previous schemes. Based on a real dataset, extensive simulations are performed to validate the effectiveness and performance of our scheme.

Nowadays, the quality of cloud services offered by different service providers varies greatly. Reputation mechanism, as a better service evaluation method, can help standardize and regulate the cloud service market. However, existing reputation systems either rely on a trusted third party with security and privacy issues, or lack reliable evaluation. To address the above issues, we propose a blockchain based privacy-preserving dynamic reputation mechanism for cloud service and a reputation management smart contract (RM) is designed to implement trusted reputation computation. The reputation integrates conformance trust in the subjective view and recommendation trust in the objective view together to provide a comprehensive evaluation. Besides, a miner selection algorithm is designed to prevent miners from launching a collusion attack. Moreover, the Paillier homomorphic encryption algorithm (PHE) is introduced to encrypt the sensitive data of customers, ensuring the security of data stored on the blockchain. Experiment results reveal that the proposed model is feasible and the performance of encryption is acceptable.

With the development of the Energy Internet and the support of the subsidy policies of various countries, Electric Vehicles(EVs) have ushered in a golden development period. However, the development of EVs needs to solve the problems of insufficient charging piles(CPs) and difficulty in finding CPs. In order to solve the problem of difficult charging of EVs, the concept of shared charging came into being, in which idle CPs or private CPs are shared to meet the charging needs of more people and improve the utilization rate of CPs. However, the shared charging scheme implemented by third-party platforms faces the issue of trust lacking. This paper proposes a blockchain architecture for shared charging, which can use the blockchain to build a trust environment involving private pile owners, charging pile(CP) operators, Electric Vehicle(EV) users, etc.. The blockchain architecture also contains the block structure where pointer was added for quick search, contract content that can automatically execute multi-party contracts to achieve secure computing and reputation-based incentive mechanism to provide high-quality charging services in detail. This architecture establishes the multi-party trust environment for shared charging from three aspects: secure storage, secure computing, and secure incentives.

As the intelligentization of Industrial Internet of Things (IIoT) broke the relatively closed and credible industrial environment, IIoT faces increasingly serious security problems. The commonly used vulnerability discovery method is protocol reverse engineering. However, it is difficult to analyze IIoT protocols with existing protocol reverse engineering approaches, as they influence the normal operation or have spare sample data. In this paper, a sparse protocol parsing method for IIoT protocols is proposed. The parsing method expands the samples of the captured IIoT protocol message sequences using a genetic algorithm (GA), which designs its fitness function based on the protocol response data to select high-quality samples. By combining the GA with the hidden Markov model (HMM) with lower algorithm complexity, a hybrid parsing model is constructed to improve accuracy in a gradual evolution way. Through comparison experiments on various IIoT protocols, our HMM hybrid model has better performance than RNN hybrid models under sparse samples.

Renewable energy powered road side units(RSUs) in Internet of Vehicles(IoVs) are a desirable green alternative choice compared to the traditional electric grid, because it not only extends the service range of IoVs but also saves the cost on energy. However, the energy on RSUs is limited and usually influenced by communication policies. Therefore, the communication scheduling policy on RSUs plays an important role on the persistence of network, which is still an open topic to be solved. In this paper, we focus on the communication scheduling problem on RSUs, and propose an LSTM-based communication scheduling algorithm for RSUs in IoVs. The scheduling algorithm is composed of three parts - deep learning clustering, LSTM-based traffic prediction, and a vehicle access scheduling algorithm. At last, we conduct an extensive simulation, and the simulation results indicate that our algorithm can achieve a better performance than the no scheduling mechanism.

The 5th generation (5G) wireless network with Unmanned aerial vehicle (UAV) is considered to be one of the most effective solutions for improving the communication coverage. However, UAV is easily affected by the wind, accompanied by a certain time delay during the air communication. Thus the inaccurate beamforming will be performed by the base station (BS), resulting in the unnecessary capacity loss. To address this issue, we propose a novel Recurrent Neural Networks (RNN)-based arrival angle predictor to predict the specific communication location of UAV under the 5G Internet of Things (IoT) networks in this paper. Specifically, a grid-based coordinate system is applied during the data preprocessing to make the training process easier and more effective. Moreover, the RNN model with the highest accuracy can be saved during the training process to ensure the real-time prediction. Simulation results reveal that the RNN-based predictor we proposed is of high prediction accuracy, which is 98% in average. Therefore, a more precise beamforming can be performed by BS to reduce the unnecessary capacity loss, resulting in a more effective and reliable communication system.

The new radio technology for the fifth‐generation wireless system has been extensively studied all over the world. Specifically, the air interface protocols for 5G radio access network will be standardized by the 3GPP in the coming years. In the next‐generation 5G new radio (NR) networks, millimeter wave (mmWave) communications will definitely play a critical role, as new NR air interface (AI) is up to 100 GHz just like mmWave. The rapid growth of mmWave systems poses a variety of challenges in physical layer (PHY) security. This paper investigates those challenges in the context of several 5G new radio communication technologies, including multiple‐input multiple‐output (MIMO) and nonorthogonal multiple access (NOMA). In particular, we introduce a ray‐tracing (RT) based 5G NR network channel model and reveal that the secrecy capacity in mmWave band widely depends on the richness of radio frequency (RF) environment through numerical experiments.

Video surveillance has been widely adopted to ensure home security in recent years. Most video encoding standards such as H.264 and MPEG-4 compress the temporal redundancy in a video stream using difference coding, which only encodes the residual image between a frame and its reference frame. Difference coding can efficiently compress a video stream, but it causes side-channel information leakage even though the video stream is encrypted, as reported in this paper. Particularly, we observe that the traffic patterns of an encrypted video stream are different when a user conducts different basic activities of daily living, which must be kept private from third parties as obliged by HIPAA regulations. We also observe that by exploiting this side-channel information leakage, attackers can readily infer a user's basic activities of daily living based on only the traffic size data of an encrypted video stream. We validate such an attack using two off-the-shelf cameras, and the results indicate that the user's basic activities of daily living can be recognized with a high accuracy.