With the fast-paced progress of the Medical Internet of Things (MIoT), cross-institutional data sharing has become a crucial tool for chronic disease prevention and control. Attribute-based proxy re-encryption (ABPRE) is a novel cryptographic technique that facilitates secure medical data sharing by ensuring data confidentiality and supporting fine-grained access control. However, although the existing ABPRE solution supports fine-grained access control, it still lacks an effective accountability mechanism to address a series of key issues, such as system key abuse and user revocation from shared sets. Therefore, this paper proposes a lattice-based traceable and direct revocable ABPRE with a fair verification scheme (T-DR-VABPRE) for Data Sharing in MIoT. This scheme incorporates a traceability and user revocation mechanism within the ABPRE framework, locating and depriving access rights to malicious users within a shared data set, preventing innocent users from being affected by attribute revocation. Furthermore, a trusted third party is incorporated to establish a bidirectional fair verification mechanism, verifying the correctness of proxy re-encryption results. The scheme, based on lattice problems, is inherently resistant to quantum attacks. Experimental results and performance analysis demonstrate the proposed T-DR-VABPRE scheme to be efficient and practical.

Space–air–ground–sea integrated network (SAGSIN) is widely envisioned as a key architecture for 6G, offering seamless global communication coverage. However, conventional congestion control schemes that do not consider data freshness are not suitable for SAGSIN, which consists of cooperative multilayer networks with time-varying topologies operating in open communication environments. In this paper, to support efficient, real-time communication in SAGSIN, we propose the age of information (AoI)-driven dynamic multipath congestion control (AMCC) scheme, which uses data freshness as the primary constraint for congestion control. By integrating the centralized control features of Software Defined Networking (SDN) with the content-centric communication paradigm of Named Data Networking (NDN), a hierarchical congestion control framework is designed. At the global level, the SDN controller employs a Nash bargaining game to dynamically compute the optimal forwarding weight for each interface according to the interface quality and the AoI constraints. At the local level, intermediate forwarding nodes establish a bidirectional congestion detection mechanism that monitors the interest queue length and AoI of data packets. The results are fed back to the consumers to enable real-time traffic adjustment, forming a closed-loop control system integrating awareness, feedback, and adaptation for fully adaptive traffic control. Experimental results demonstrate that the proposed AMCC scheme reduces the AoI from 46.2% to 71.3% and improves throughput from 18.5% to 25.8% when compared with representative schemes PCON, HoBHIS, MIRCC, and JAQMCC. The proposed AMCC scheme can provide high-timeliness data transmission of SAGSIN for scenarios such as air-based disaster warning, vehicle–edge–cloud collaboration, and real-time ocean monitoring.

With the development of Internet of Vehicles technology, more and more private data from vehicle users are being collected in cloud storage. However, cloud storage adopts a centralized storage model, and once attacked, all the data may be leaked. In addition, to protect data confidentiality, the data owner adopts a ciphertext policy attribute-based encryption scheme to enable one-to-many data sharing and fine-grained access control. The traditional ciphertext-policy attribute-based encryption scheme relies on a single authorization server for managing user attributes and key distribution, making it vulnerable to key misuse attacks and prone to single-point-offailure problem. To resolve these issues, this paper presents a secure and verifiable data sharing scheme leveraging cloud-edge collaboration in the Internet of Vehicles. Data are stored at edge nodes, while metadata are stored at cloud storage to meet users’ low-latency requirements and reduce transmission pressure. In addition, an attribute management and key generation model with multiple authorization servers is proposed to resist key misuse attacks by the attribute authority and to address the single-point-of-failure problem of a single authorization server. Security analysis and experiments demonstrate that the proposed scheme effectively resists various attacks while maintaining low time overhead.

Radio Frequency Fingerprinting (RFF) exploits the unique characteristics of device hardware and has become a key technology in IoT device authentication and network security. Identification of unknown devices is a key challenge for radio frequency fingerprinting (RFF) in open-set scenarios, the similarity of device hardware characteristics further exacerbates the difficulty of the task. This paper proposes an open-set RFF recognition framework called SV-NPR (Siamese VGG16 with Negative Prototype Rejection). The framework combines the advantages of the VGG16 network in local feature extraction with the contrastive learning mechanism of the siamese network, and can efficiently capture the distribution of local detail features in RF signals. In addition, the introduction of a dynamic rejection mechanism based on negative prototypes improves the robustness and generalization ability of the model for unknown categories. Experimental results show that SV-NPR significantly outperforms the state-of-the-art on the Oracle dataset and exhibits leading recognition capabilities in open-set scenarios.

In some application scenarios, strong spatio-temporal correlations exist between remote sensing data streams. For instance, during earthquake relief efforts, users in nearby locations may simultaneously request remote sensing data from an area of interest within the same time period. This leads to large data volumes transmitted in a limited spatio-temporal range, causing network traffic imbalance and reduced transmission efficiency. To address this issue, this paper proposes the Aggregated Transmission Strategy for Remote Sensing Data based on Spatio-Temporal Association (AFRST). According to the spatio-temporal attributes of remote sensing data and the location of users, AFRST utilizes the mapping relationship between naming and demand in Named Data Networking(NDN) and generates a demand association set when the demand between users in the same area reaches the association threshold, and the data in the overlapping area in the set is transmitted only once. We also uniformly assign transmission paths to the association set to improve the data transmission efficiency. Furthermore, AFRST takes into account the network state and user demand, constructing a Transmission-Load Balancing Control Model (TLBCM) based on the network utility maximization framework. This model maximizes the data transmission rate and balances the network load under constraints such as link capacity and other factors in each time slot, optimizing network service performance. The performance of AFRST is evaluated using ndnSIM, and the experimental results demonstrate the effectiveness of AFRST in terms of transmission latency, throughput, and number of completions. Compared to DCT and DPCCP, the average completion time per demand is increased by 29.9% and 18.1%, the overall transmission rate is increased by 43.5% and 22.8%, the overall average increase in the number of completions is about 42.4% and 22.9%, and the throughput is about 16.1% and 15.7% higher.

To address the issues of poor user privacy protection and insecure communication in the Internet of Medical Things (IoMT) environment, we propose a blockchain-based lightweight mutual authentication scheme for the IoMT. First, our scheme is a two-factor authentication scheme that uses certificates and feature information for identity authentication. Second, we use elliptic curve cryptography and the Chinese remainder theorem to design a lightweight identity registration and authentication algorithm. This algorithm can aggregate multiple identity information of users for verification, while achieving efficient user identity authentication and ensuring the security of user identity information. Finally, we combined nonfungible tokens (NFTs) with user device information, and through a composable NFT solution, we ensured the uniqueness and immutability of user identity information on the blockchain, while facilitating user identity management. The formal security analysis based on AVISPA has proven the security of our scheme. Performance analysis shows that the proposed scheme has low communication and storage overhead. We simulated the proposed scheme on the Ethereum platform using the Solidity language and conducted latency and throughput analysis of our smart contracts using the stress testing tool, Hyperledger Caliper. The results illustrate the practicality of our scheme.

In drone-assisted communication scenarios, especially in post-disaster rescue, there is a risk of privacy leakage when heterogeneous drone groups collaborate. Therefore, ensuring the anonymity of drones while guaranteeing the reliability of the message source is a significant challenge. Although existing schemes achieve anonymity, they are unable to dynamically adjust the authentication difficulty according to changes in the physical environment. In this paper, we propose an application of multi-factor puf authentication circuit based on lattice group signature for in drone-assisted communication. The scheme innovatively designs a PUF-based multi-factor authentication circuit and proposes a dynamic authentication strategy, achieving authentication and physical security under different security requirements. The scheme proposes a lattice-based heterogeneous drone group signature scheme through Merkle hash tree and lattice cryptography, which achieves efficient anonymous authentication and group member update and ensures post-quantum security. The security of the scheme is proved by formal security analysis based on AVISPA and random oracle model (ROM). Moreover, experimental results show that PUF-CLGS outperforms the benchmark schemes in terms of both computational and communication costs.

Satellite remote sensing cooperation is essential for ensuring efficient data transmission in real-time applications. Network traffic prediction plays a crucial role in optimizing data transmission strategies, managing congestion, and reducing network latency. However, current research work on network traffic prediction frequently fails to fully exploit the complex spatial-temporal dependencies inherent in satellite network traffic. To address this limitation and improve the accuracy of long-term network traffic prediction, we propose an Efficient Spatiotemporal Prediction Transformer (ESPformer) for dynamic data transmission in cooperative satellite remote sensing. The proposed scheme not only considers propagation delays but also captures the temporal and spatial relationships among the network traffic. In particular, we design a spatial-temporal multi-head attention mechanism within an encoder-decoder transformer to capture the dynamic spatial dependencies and predict the network topology and its parameters, including traffic flow and bandwidth. By leveraging historical traffic data and the network traffic conditions, the model estimates expected queuing delays. Finally, based on the volume of traffic predicted and the changes in network conditions, we dynamically adjust the transmission strategies to maintain an efficient relaying mechanism. Therefore, our model enables an adaptive transmission strategy and offers an optimal delay reduction in real-time satellite data transmission. Extensive experiments conducted on four well-known traffic datasets demonstrate that the ESPformer significantly outperforms state-of-the-art baselines across all key performance metrics.

Logs are valuable data for detecting anomalous network behavior. Accurate feature extraction from logs is essential for anomaly detection. However, statistical-based feature extraction methods consider the statistical features of logs over a period of time, while ignoring the semantic information in each log. Most semantic-based feature extraction methods only embed the token semantics into high-dimensional vectors for semantic representation, which may lead to the lack of positional information between tokens, so that the uniqueness of the log template semantics cannot be maintained. At the same time, it also causes time-consuming problems due to high-dimensional data. To address the above challenges, we propose a novel log anomaly detection method called LogESP, which enhances the semantic representation of logs with positional information. Lo-gESP aims to effectively capture the semantic differences between normal and abnormal logs, thereby providing a new perspective for accurate detection of abnormal network behavior. First, we select representative words to summarize the semantics of log templates, then use structured tuples to enhance the semantic representation of the templates, and finally, construct LSTM model to extract the temporal features of the log sequences. Experimental results on two public log datasets (BGL and HDFS) show better performance compared to other state-of-the-art methods.

Alert fatigue has caused serious consequences for enterprise security. When analysts are inundated with a vast number of alerts, high-risk alerts may be overlooked or responded to with delay, thereby exposing the organization to potential cyber threats or data breaches. Although there are many alert classification research focusing on reducing alerts, it’s still impossible to investigate all alerts due to the resource shortage. Therefore, it is necessary to prioritize alerts based on their potential severity, allowing analysts to address higher-risk alerts first. This paper proposes a novel alert risk rating DeepARR (Deep Learning-based Alert Risk Rating), that utilizes deep learning technology to rate alert risk levels in bulk instead of investigating each alert individually. It first employs a dynamic time window segmentation approach to merge alerts, reducing the overall number. Subsequently, a directed graph-based method is proposed to handle data imbalance. Both temporal-spatial features and event features are extracted. Finally, deep learning techniques are used to classify alert levels. The proposed method is evaluated on the public CPTC-2018 alert dataset. Compared with existing methods, DeepARR achieves an average precision of 95.73%, a recall of 94.83%, and an F1 Score of 94.84% in risk rating, demonstrating its higher effectiveness.

With the development of vehicular networks and cloud-edge collaborative technologies, a large amount of vehicle data is collected at edge nodes (Edge Node, EN) for analysis and decision-making. However, edge data faces challenges in terms of integrity and security. Data owners (Data Owner, DO) should delegate auditors to periodically verify the integrity of the data. However, existing verification methods have not yet addressed issues related to verifiers forging evidence and fair payment. This paper proposes a smart contract-based edge data integrity verification scheme. An audit tree based on lattice hashing is designed, allowing the smart contract to initiate multiple verification challenges while only storing a complete label, thus reducing storage overhead. The homomorphic additivity of lattice hashing supports arbitrary data segmentation as challenges, effectively preventing EN from forging evidence. This scheme also designs two smart contract arbitration algorithms to ensure fair payment. Experimental comparisons show that this scheme effectively resolves the trust issues related to EN and ensures fair payment among EN, CSP, and DO.

Abstract In recent years, with the development of low‐earth orbit broadband satellites, the combination of multi‐path transmission and software‐defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi‐path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi‐path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real‐time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi‐objective optimization‐based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high‐speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN‐MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN‐MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS.

Today’s Internet of Vehicles (IoV) faces many security risks in the data transmission process, and image data is more vulnerable to security threats in the transmission process due to its special characteristics such as large amounts of information and high visibility. Therefore, to guarantee the dependability of data transmission in the IoV environment, this paper designs a blind signature scheme for IoV based on two-dimensional sine cosine cross-chaotic mapping (2D-SCML) image encryption and lattice cipher (BSS-IoV). The innovation of this scheme is that it aims at blind signature of image information, blinds it before sending the information, and combines the lattice public key encryption algorithm to better ensure the safe and reliable transmission of information and reduce the risk of information disclosure.To further ensure the security of the scheme, an image encryption algorithm based on 2D-SCML and pixel splitting (2PS-IEA) is proposed, which is used to blind the information and thus reduce the risk of information leakage on the one hand, on the other hand, it is used in the signature process to ensure the security of the signed information. The 2D-SCML is derived from the cross-model structure proposed in this paper. Through simulation results and experimental analysis, the values of NPCR and UACI, respectively, 99.6094% and 33.4635%, are close to ideal values. And 50% of the cut image can also recover the rough information, which indicates that the signature scheme has the security against differential attacks, cut attacks and noise attacks. Moreover, the security analysis shows that the scheme has the anti-tamper, anti-repudiation and traceability.

Logs play an important role in anomaly detection, fault diagnosis, and trace checking of software and network systems. Log parsing, which converts each raw log line to a constant template and a variable parameter list, is a prerequisite for system security analysis. Traditional parsing methods utilizing specific rules can only parse logs of specific formats, and most parsing methods based on deep learning require labels. However, the existing parsing methods are not applicable to logs of inconsistent formats and insufficient labels. To address these issues, we propose a robust Log parsing method based on Self-supervised Learning (LogSL), which can extract templates from logs of different formats. The essential idea of LogSL is modeling log parsing as a multi-token prediction task, which makes the multi-token prediction model learn the distribution of tokens belonging to the template in raw log lines by self-supervision mode. Furthermore, to accurately predict the tokens of the template without labeled data, we construct a Multi-token Prediction Model (MPM) combining the pre-trained XLNet module, the n-layer stacked Long Short-Term Memory Net module, and the Self-attention module. We validate LogSL on 12 benchmark log datasets, resulting in the average parsing accuracy of our parser being 3.9% higher than that of the best baseline method. Experimental results show that LogSL has superiority in terms of robustness and accuracy. In addition, a case study of anomaly detection is conducted to demonstrate the support of the proposed MPM to system security tasks based on logs.

With the development of in-orbit processing technology of remote sensing satellites, the intelligent processing units carried on the satellites realize the real-time acquisition and intelligent processing of remote sensing images, thus satisfying users’ individual needs and enhancing the response speed of tasks. However, a large amount of observation data cannot be transmitted back to the ground in time due to the limitation of the transmission capability of remote sensing satellites to the ground and the visible time window between satellites and ground stations in the process of satellite-terrestrial transmission. To solve this problem, a community division-based content distribution strategy (CDCD) is proposed. Firstly, the time slot model is designed to capture the time-varying topological information of the satellite network. Also, a content naming method that fits the characteristics of remote sensing data is presented. Then, a caching scheme based on community division is proposed by analyzing the regional characteristics of user requests. Taking advantage of the community structure characteristics of satellite networks, a novel cache node selection algorithm is designed to meet the user’s demand for fast access to target files. Meanwhile, a cached content prioritization model is constructed to further optimize the utilization of caching resources. Finally, the community structure-based routing algorithm (CSR) is proposed to effectively reduce the redundant transmission during content access through the mutual collaboration of intra-community and inter-community routing schemes. Simulation experiments show that the CDCD strategy effectively exploits the limited caching resources in the satellite network compared with other strategies, which promotes the stable and efficient distribution of remote sensing data.

Information-centric satellite networks play a crucial role in remote sensing applications, particularly in the transmission of remote sensing images. However, the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands. Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content. In this paper, we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks, specifically focusing on the transmission of remote sensing images. Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time, effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion. We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction. To address these challenges, we leverage federated reinforcement learning techniques. Additionally, we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images. Through software-based simulations using a low Earth orbit satellite constellation, we validate the effectiveness of our proposed strategy, achieving a significant 17% reduction in the average delivery delay. This paper offers valuable insights into efficient content delivery in satellite networks, specifically targeting the transmission of remote sensing images, and presents a promising approach to mitigate burst traffic challenges in information-centric environments.

With the increasing communications between the In-Vehicle Networks (IVNs) and external networks, security has become a stringent problem. In addition, the controller area network bus in IVN lacks security mechanisms by design, which is vulnerable to various attacks. Thus, it is important to detect IVN anomalies for complete vehicular security. However, current studies are constrained by either requiring labeled data or failing to accurately detect message-level anomalies without labeled data. In addition, the concept drift of existing methods has become a challenge over time. To address these problems, this paper proposes an IVN anomaly detection method based on Self-supervised Learning (IVNSL), which is capable of detecting message-level anomalies without labels. The essential idea of IVNSL is to make the message prediction model learn the distribution of normal messages in sequences using message sequences with noise. Furthermore, to accurately detect anomalies, a Message Prediction Model based on Hierarchical transformers (MPMHit) is proposed, which captures the spatial features of the message and the dependencies between messages. Meanwhile, to solve the concept drift over time, this paper proposes an online update mechanism for MPMHit based on vehicle-cloud collaboration. We conduct an extensive experimental evaluation on the car hacking dataset, resulting to an F1-score average and average false positive rates of IVNSL being 2.282% higher and 1.595% lower than the best baseline method. The average detection speed of each message is as fast as 0.1075 ms.

The low earth orbit (LEO) remote sensing satellite has a short communication time with the earth station (ES), and a large amount of remote sensing data cannot be transmitted back to the ES in time using the LEO-ES link during the communication period. Using relay satellites can indirectly increase the amount of data transmitted back from LEO. In this paper, we combine LEO- ES link and relay satellite offloading to study the problem of maximizing the amount of data transmitted back from LEO remote sensing satellites. Most of the existing methods do not consider the effect of topology change on policy. In this paper, we consider a three-layer satellite network architecture of geostationary earth orbit (GEO), LEO remote sensing satellite , and ES. We studied the problem of maximizing the amount of LEO transmitted back data under dynamic topology between layers, and proposed a transmission strategy based on a combination of LEO-ES link and GEO offload under dynamic topology. First, in order to reduce the number of link interruptions in each time slot, a Non-Uniform Time Slot Division Method (NUTSDM) based on visible relationships between layers is proposed based on discrete-time points, which helps to accurately determine the number and identity of LEOs competing under each time slot. Second, the relationship among GEOs, LEOs, and network administrators is modeled as a Stackelberg game model, and a Two-way Bargaining Game Scheme under Dynamic Topology (TWBGS-DT) is proposed to maximize the amount of data transmitted back from space. Compared with the existing methods, the experimental results confirm the effectiveness of the proposed scheme in terms of algorithm convergence speed, terms of pricing, GEO cache space allocation, and increase the data volume of LEO transmissions back by 11.5% and 8.2 times relative to the ISL-Aided strategy and GAA-FARR strategy, respectively.

Recently, it has been found that cloud storage still has security risks, and research on the security and privacy of user data and information is still in the early stage. This paper studies the security risks of cloud data, and designs an image encryption scheme based on neural networks. First, the existing neural network model is improved to obtain a new bidirectional activation (BA) neural network, to establish a many-to-one mapping relationship between the key and the chaotic initial value, to hide the original key of the cloud encryption system, and to improve the security and randomness of the key system. Then, a medical image encryption scheme based on dynamic index scrambling and the M-semitensor product diffusion is proposed. Dynamic index scrambling is more flexible than the traditional approach, and its security and efficiency are improved. The diffusion algorithm adopts the semi tensor product operation, and one of the product matrices is composed of a unitary matrix after Schur decomposition of a plaintext image to effectively resist a selective plaintext attack. Performance analysis shows that the encryption algorithm has high security.

Network attack identification effectively secures Internet of Things (IoT) application scenarios. However, dynamic scene changes, attack feature reliance, high data dimensions, and challenges with spatio-temporal feature fusion frequently pose limitations to attack traffic identification in IoT contexts. Definitive intelligent IoT attack identification enables intelligent algorithms to extract attack features for application scenarios with fixed topological environments but cannot construct the intricate changes of IoT application scenarios. Through the dynamic acquisition, feature awareness, and deep learning, intelligent digital twin-based attack detection can address these issues and enhance attack identification for IoT threats. Thus, this paper proposed an intelligent digital twin method based on spatio-temporal feature fusion for IoT attack behavior identification. Firstly, feature subsets are selected based on information gain to reduce the dimensionality of IoT data with high traffic; Secondly, a parallel spatio-temporal feature extraction model is designed unlike the existing tandem model, which uses a simplified Convolutional Neural Networks (CNN) model to learn the spatial features of the attack, a Bi-directional Long Short-Term Memory (BiLSTM) model to learn the temporal features of the attack, an attention mechanism to fuse the temporal and spatial features, and the (Deep Neural Networks) DNN to learn the combined features; Finally, the virtual instance space and topology of the attack scenario are simulated using digital twin (DT) to build a digital version of the complex system for IoT applications and tested in a simulation environment. Based on experimental results using the UNSW-NB15 and CICIDS2017 datasets, this paper shows that the proposed method can extract spatio-temporal features from network attack traffic and has a 5% improvement in test accuracy.

With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates, satellites have become an important part of data transmission in air-ground networks. However, due to the factors such as geographical location and people’s living habits, the differences in user’ demand for multimedia data will result in unbalanced network traffic, which may lead to network congestion and affect data transmission. In addition, in traditional satellite network transmission, the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner, which is not conducive to calculating optimal routes. The service quality requirements cannot be satisfied when multiple service requests are made. Based on the above, in this paper artificial intelligence technology is applied to the satellite network, and a software-defined network is used to obtain the global network information, perceive network traffic, develop comprehensive decisions online through reinforcement learning, and update the optimal routing strategy in real time. Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability. Compared with traditional routing, the throughput is 8% higher, and the proposed method has load balancing characteristics.

The WeChat Official Account (WCOA) is the most influential self-media platform in China, and the follower economy brought by the extension of self-media has profoundly affected its survival and development. Therefore, it is of vital importance to understand the followers' behavioural patterns and the law of quantity change of followers. To study this problem, we collected the operation data of two WCOAs. First, an evolutionary Followers-Susceptible-View-Forward-Removed (F-SVFR) model is proposed to describe the trends in the behavioural state of the followers of WCOA after accepting the pushed message. Second, we proposed an equation to simulate the number of newly increased followers and find that the number of newly increased followers of WCOA followed a similar pattern that grew rapidly in the early stages and kept a relatively low and steady rate of increase rate later on. Finally, the popularity formula is defined for the WCOA to serve as a predictor of online popularity, which demonstrated that popularity is positively correlated with the number of followers. Our study provides account operators with practical guidance, which has significance for understanding the popularity of increased official accounts and the law of quantity growth of followers.

Abstract A bit‐level image encryption algorithm based on Fully‐Connected‐Like network(FCLN) and random modification of edge pixels is proposed. In the paper, in order to enhance the security of the cryptographic system, random noise is first used to modify the least significant bits of the edge pixels of the image, and the modified image is used as the input image. Later,the chaotic sequence is used to perform cyclic shift transformation on the image. In the subsequent steps, the FCLN is generated based on a fully connected neural network, which can perform scrambling and diffusion operations on the input image. Finally, the bidirectional diffusion method is used to diffuse the image forward and backward. In addition, the image after the edge pixel modification is convolved with the chaotic sequence, and the initial value of the chaotic system is set by the result to establish the correlation between the plain image and the algorithm, which makes the algorithm resistant to known/chosen plaintext attack. Experimental results show that although the image is modified by random noise, the decrypted image is visually the same as the original image. At the same time, through the analysis of common attacks such as differential attacks, noise attacks, and data loss attacks, our algorithm shows high security.

Logs are widely used in network security and management because they record runtime details in IT systems. It is difficult to gain insights from raw unstructured logs, so many researches first parse raw logs into structured templates. However, as the volume of logs grows rapidly, efficiency becomes a major concern in log parsing. In this paper, we propose a quick log parsing algorithm QuickLogS based on template similarity. QuickLogS utilizes regular expressions to replace the variables with wildcard and filters the reduplicate data to parse huge volume of unstructured logs into finite structured templates. To improve parsing efficiency, SimHash algorithm and Hamming distance are used to merge the similar templates of the same length. To the best of our knowledge, we are the first to apply the SimHash algorithm to log parsing. Besides, different with other work, we also merge the similar templates of different lengths based on the cosine similarity algorithm, which contributes to improve the parsing accuracy. QuickLogS is evaluated on six real public log datasets, and compared with four state-of-the-art log parsing algorithms. The experimental results show that QuicklogS outperforms the other parsers in terms of efficiency and accuracy.

Abstract To mitigate the shortcomings of existing medical image encryption algorithms, including a lack of anti‐tampering methods and security, this report presents an anti‐tampering encryption algorithm for medical images that is based on a self‐verification matrix. First, chaotic coordinates generated by chaos are used to traverse all pixels in a plain image to generate a two‐dimensional matrix (a self‐verification matrix) with positioning information. The accurate location of illegally altered image pixels can be detected using the self‐verification matrix. To improve the security of the self‐authentication matrix, DNA coding is also applied to the self‐authentication matrix, and the plain image is also diffused statically to destroy the pixel distribution. Next, the scrambled image and self‐verification matrix are mixed and cross‐scrambled. Finally, the fused image is diffused dynamically to improve the security of the encrypted image. Experimental simulation and performance analysis show that the algorithm achieves good encryption effectiveness, provides strong anti‐tampering capabilities, and can accurately locate at least 4 pixels.

Abstract In cryptosystems, the generation of random keys is crucial. The random number generator is required to have a sufficiently fast generation speed to ensure the size of the keyspace. At the same time, the randomness of the key is an important indicator to ensure the security of the encryption system. The chaotic random number generator has been widely used in cryptosystems due to the uncertainty, non-repeatability, and unpredictability of chaotic systems. However, chaotic systems, especially high-dimensional chaotic systems, have slow calculation speed and long iteration time. This caused a conflict between the number of random keys and the speed of generation. In this paper, we introduce the Least Squares Generative Adversarial Networks(LSGAN)into random number generation. Using LSGAN’s powerful learning ability, a novel learning random number generator is constructed. Six chaotic systems with different structures and different dimensions are used as training sets to realize the rapid and efficient generation of random numbers. Experimental results prove that the encryption key generated by this scheme can pass all randomness tests of the National Institute of Standards and Technology (NIST). Hence, our result shows that LSGAN has the potential to improve the quality of the random number generators. Finally, the results are successfully applied to the image encryption scheme based on selective scrambling and overlay diffusion, and good results are achieved.

By forming a temporary group, users in mobile social networks (MSNs) can disseminate data to others in proximity with short-range communication technologies. However, due to user mobility, airtime available for users in the same group to disseminate data is limited. In addition, for practical consideration, a star network topology among users in the group is expected. For the former, unfair airtime allocation among the users will undermine their willingness to participate in MSNs. For the latter, a group head is required to connect other users. These two problems have to be properly addressed to enable real implementation and adoption of MSNs. To this aim, we propose a joint head selection and airtime allocation scheme for data dissemination within the group using Nash bargaining theory. Specifically, we consider two cases in terms of user preference on the data to be disseminated: a homogeneous case and a heterogeneous case. For each case, a Nash bargaining solution (NBS) based optimization problem is proposed. The existence of optimal solutions to the optimization problems is proved, which guarantees Pareto optimality and proportional fairness. Next, an algorithm that allows distributed implementation is introduced. Finally, numerical results are presented to evaluate the performance, validate intuitions and derive insights of the proposed scheme.