With the popularity of cloud storage, data integrity verification has become a challenging issue. Traditional centralized auditing techniques rely on third-party auditors (TPAs), who not only suffer from single points of failure and security vulnerabilities but are also not fully trustworthy. Blockchain technology offers a new approach to solving this problem. This paper proposes a data integrity verification framework based on blockchain. The scheme employs Merkle tree and Interval Tree structures by constructing an independent Merkle tree for each data fragment. The root hash values of these individual Merkle trees are subsequently aggregated to form an Interval Tree. Leveraging the tamper-proof features of the blockchain, the root hash summaries are uploaded to the blockchain for preservation. Additionally, we introduce a novel verification method based on Shamir's Secret Sharing (SSS), which effectively safeguards data privacy. Furthermore, the use of Merkle Tree and Interval Tree aims to optimize the retrieval process, enhancing the efficiency of data integrity verification. Finally, theoretical analysis and experimental results demonstrate that the scheme improves processing speed and reduces resource consumption.
Herein, a social trust model is presented for investigating social relationships and social networks in the real world. Our proposal addresses the design of conceptual concepts to easily implement and develop complex applications that require direct interaction among objects. The secure social relationship paradigm guarantees network navigability, even if there are more objects on the traditional Internet of Things (IoT). This paradigm shift is very interesting, but very limited researchers have worked in this direction. Therefore, the core objective of our article is to study and classify these objects and emphasize the importance and the characteristics of trust in social objects and their relationships. Herein, five types of secure social relationships were identified and explained with real scenarios. Finding new social relationships and understanding different features and characteristics helps the researcher understand their structure and, thus, build trust between smart objects. Based on these social relationships we propose secure social relationship modeling. The achieved experimental result shows that our proposed model outperforms as compared to the state-of-the-art model. The efficiency is measured based on processing time and precision. We present a critical alternative vision based on the most recent literature. We discuss social relations along with strengths and weaknesses. Our critical analysis guides the reader to understand this paradigm in-depth and its impact on future technologies, i.e., artificial social intelligence. We also present the major ongoing research activities and highlight the most important technical and critical challenges associated with these objects, such as inter-object relations, support and definition of new communication primitives, and security and privacy concerns.
With the rapid development of modern networks, more and more people are gradually learning how to reduce the information gap. In recent years, students under high academic pressure often apply to multiple universities at the same time when applying for further education. They decide whether to accept after receiving multiple offers or even decide whether to breach the contract after accepting. Hong Kong, a city with a large concentration of international universities, has been widely favored among applicants in recent years. However, this also leads to fierce competition and complex games. Most applicants need to consider which schools they should apply to and how to weigh the trade-offs between striving for a better school and mitigating the risk of going without a book. This article will analyze the best decisions for applicants from the perspective of both the applicants and the universities, and explain why defaults have increased in recent years. It is concluded that for most applicants, retaining lower-quality offers and then giving up after successfully striving for high-quality offers has become the optimal decision. This also explains why defaults have increased in recent years.
As a result of the Internet of Things, high-speed data transmission and ultra-low latency are achieved in various applications. Data tampering in IoT networks can be caused by malicious or accidental interference, however. By combining blockchain technology with software-defined networking (SDN), we can mitigate these problems. IoT devices and SDN controllers will be able to communicate peer-to-peer by using public and private blockchains. For devices with limited resources, it is an ideal solution since it eliminates Proof-of-Work (PoW) and incorporates distributed trust. In this paper, we present a new Proof-of-Authority (PoA) consensus algorithm that is designed to improve the consistency and reliability of edge devices within the IoT ecosystem, as well as to ensure a high level of trust across IoT ecosystems. During experiments, the integration of SDN and blockchain outperformed existing models in terms of throughput, delay, response time, and CPU utilization, including Blockchain Fundamentals (BCF), AS Cooperative Interdomain Reputation (ASCIR), and Blockchain-based SDN-enabled Secure Routing (BSDNSR).
Machine learning-based methods for detecting malicious Android applications are widely researched, but their security is a concern. Adversarial attacks can easily evade the detection of these methods. This paper designs a feature-dispersion-based Android malware detection method that can effectively defend against adversarial attacks. The method takes the function call graph as features and reduces the feature dimensions according to the function types. The feature matrix is then separated to form 349 features of 1*349 dimensions, while 349 lightweight convolutional neural networks are used for malicious application detection. Experimental results on public datasets show that the method in this paper is effective in detecting adversarial attacks, with a high detection rate of 94.83%.
Non-Fungible Tokens (NFTs), recognized for their uniqueness and irreplaceability, serve as an effective mechanism for copyright protection of digital works. Utilizing NFTs to build trading platforms facilitates copyright authentication, monitoring, and circulation of artworks. To alleviate the immense storage pressure on blockchain networks, NFT artworks are often stored in the InterPlanetary File System (IPFS). However, as IPFS is a decentralized file system lacking encryption mechanisms for data, the absence of privacy protection could render copyright protection measures ineffective if malicious users access and utilize the data outside the trading platform. This paper proposes a tri-layered protection mechanism for NFT artwork data, encompassing access control, image watermarking, and data encryption. The proposed scheme ensures artwork protection against theft without compromising the circulation and transaction of works. Experimental results demonstrate that the scheme meets the privacy and security needs of the artworks efficiently and at a low cost.
Blockchain technology is popular, which can enhance the transparency of shipping logistics information in the shipping industry. With the time-varying state change of shipping service level driven by blockchain technology, this paper uses the continuous dynamic optimization control method to build the dynamic decision-making model of shipping blockchain technology investment of shipping company, and uses the HJB equation to solve the optimal blockchain technology investment level of shipping company, coupled with the optimal dynamic evolution path of shipping service level, shipping volume, and shipping company's expected discounted profit. This work reveals the impact of model parameters such as shipping price on blockchain investment and operation strategy. We find that appropriately increasing shipping prices can help increase shipping company's motivation to invest in the blockchain system and enhance transparency of shipping logistics information. We also find that shipping service level, shipping volume, and shipping company's expected discounted profit all change dynamically over time driven by blockchain technology. Finally, the validity of the model is verified by numerical simulation.
In recent years, encrypted malicious traffic has significantly threatened network security. Deep learning offers a viable solution for feature extraction, but its accuracy depends on data volume, and traffic data varies across organizations, leading to data silos. This paper proposes a blockchain-based federated learning framework, utilizing smart contracts for aggregation and sharing. A novel aggregation algorithm is introduced, selectively aggregating based on each round's local model score to enhance global model performance. Evaluations on public datasets show that this framework effectively shares encrypted malicious traffic data, achieving high detection accuracy and resolving single points of failure.
As the Internet continues to expand rapidly, the threat posed by malicious domain names to network security is on the rise. Cybercriminals exploit these domains to launch attacks such as malware infections, phishing scams, DDoS attacks, and botnets, posing a severe risk to both individuals and organizations. In light of this, research into effective methods for detecting malicious domains has become increasingly critical. However, traditional machine learning algorithms and blacklist-based approaches have proven ineffective against the sophisticated Domain Generation Algorithm (DGA) technology used by attackers. Recognizing the importance of DNS (Domain Name System) in detecting and mitigating malicious domains, research has focused on leveraging DNS information for detection. To overcome the limitation of robust feature engineering, This study presents a novel malicious domain detection method that fuses features using an attention mechanism. By integrating IP-domain name features and domain name character features through the attention mechanism, BiLSTM is employed to extract character features, while graph neural networks mine domain name-IP associations from DNS messages. Ultimately, the attention mechanism fuses these two types of features to enhance domain name detection, achieving an impressive accuracy rate of 92.38% on a subset of the CIC-Bell-DNS2021 dataset.
The proposed architecture eliminates the need for computationally intensive Proof-of-Work mechanisms by using both public and private blockchains to enable peer-to-peer communication between IoT devices and SDN controllers. These systems, however, rely on traditional client/server infrastructures that confront considerable security and privacy challenges, as well as strict compliance with healthcare privacy regulations. For modern health care systems, it is essential to manage electronic health records (EHRs) efficiently. With these challenges in mind, the proposed model introduces a blockchain-based approach for securing authorization and integrity maintenance, scalable and manageable enough to integrate seamlessly with existing healthcare systems. Blockchain and the healthcare sector are explored in this paper, highlighting the features and recent developments of this technology. In conclusion, blockchain plays an essential role in improving health records organization, security, and management, thus making healthcare more efficient and secure.
In recent years, the proliferation of cryptocurrency mining malware has posed significant threats to internet and computer users, leading to substantial losses. However, current research on mining malware detection tends to focus on single static features like opcodes or raw bytes, or some studies attempt to combine multiple static features, often overlooking the inherent correlations among them. To address these limitations, this paper presents a novel approach to detecting mining malware using Graphical Architecture-Aware Transformer (GAT) networks. We analyze three static features in malware samples: basic blocks, control flow graphs, and function call graphs, meticulously examining the connections between them, including jump relationships and process dependencies. Experimental results show that our detection model achieves more effective mining malware detection compared to the most recent related models.
In order to solve the problems of low embedding rate, weak robustness, and insufficient concealment in current blockchain covert communication, a blockchain covert communication model based on 3D model steganography is proposed. Firstly, combining blockchain and 3D models, a 3D model steganography based on the distance between 3D vertex coordinate vectors is proposed, which embeds hidden data into the 3D model by utilizing the distance between adjacent vertex coordinate vectors. Then, the generative text steganography based on the transaction amount is used to convert the identification information of the dense model into the blockchain transaction amount data according to the real amount distribution law. Subsequently, the Shuffle function and transaction address matrix are used to randomly generate the identity information of both parties in the transaction and package the transaction onto the chain. Finally, the receiving party obtains the encrypted transaction and decodes it in reverse to obtain the encrypted model, ultimately extracting hidden data from it. The experimental results show that the model can achieve MB-level data transmission, and the resistance rate to commonly used attack methods can reach about 90%, with high concealment and security.
Wireless Body Area Network (WBAN) is a network that relies on the human body to realize the communication of users, gate ways and sensors, and it transmits personal real-time physiological data. However, due to security vulnerabilities and the unique characteristics of WBAN, because of the limited of resources, WBAN are prone to denial-of-service disruptions, intermediary interference, and are vulnerable to straightforward tracking and alteration, easy traceability and modification attacks. In view of these security threats, this paper proposes an efficient and lightweight hybrid authentication protocol of hash function and Physical unclonable function (PUF). Initially, the protocol creates a secure channel for verified users, adopting the hash of the PUF-derived response as the key for the session, thereby averting unauthorized entry to IoT sensors. The protocol reduces the computational burden of nodes by reducing the use of lightweight en cryption primitives (hash), and the three parties used different PUF to strengthen the security of information transmission. Compare with the traditional protocols, In terms of communication and computation overhead, the protocol has low consumption, high efficiency and novel strategy advantages. At the same time, the password update phase only requires the participation of the user, so the actual operation is more convenient.
Covert communication can be readily achieved by Quantization Index Modulation (QIM) steganography in Voice over Internet Protocol (VoIP) streams, which presents a significant threat to cyber security. The VoIP streams have theoretically unlimited length. The secrecy of steganography can be enhanced by lengthening the carrier and lowering the embedding rate. In the case of low embedding rates (1 % −9 %), the steganography algorithm has little impact on the original VoIP stream. The existing steganalysis methods fail to capture this change effectively and therefore the detection performances are poor. To address this potential risk, this paper focuses on the local change of codeword reliance and and presents a steganalysis approach based on Multi-stage Adjacent Reliance Leaching, termed MARL. The experimental results indicate that our approach is more effective than existing steganalysis methods when the embedding rate is low.
The swift advancement of blockchain technology has improved the transfer of value and assets between various blockchain systems. Nonetheless, the differing underlying architectures of these platforms have impeded effective value transfers, leading to the development of cross-chain technology. Cross-chain transactions face complexities due to varied user identity definitions, methods, and authentication mechanisms across blockchain networks. To address these challenges, this study optimizes identity identification and authentication methods with a privacy-preserving solution for cross-chain asset transactions. This enhances the identity management mechanism, safeguarding user privacy. Initially using ring signature algorithms to anonymize user identities, the study now employs Decentralized Identity (DID) and Verifiable Credentials (VC) for unified identity identification and authentication across chains. This approach grants users control over their identities, removes blockchain barriers, and adapts to multiple scenarios. Comparative analysis includes gas consumption, transaction delays, and performance evaluations of both solutions.
With the integration of digitalization and advanced technologies such as blockchain and artificial intelligence, the digital economy has become a significant driving force for social development. In real-world scenarios, optimizing resource allocation and product distribution to maximize benefits is a crucial research topic. To address this issue, this paper proposes a comprehensive model that includes both resource allocation and product distribution, abstracted as a hierarchical architecture comprising a central server, multiple resource scheduling units, and product distribution units across different locations. A distributed resource scheduling model is established, and a consortium chain is deployed to ensure data security. Building upon this foundation, we present an intelligent scheduling optimization solution to achieve global performance maximization under boundary constraints. Experimental results confirm the effectiveness of integrating resource scheduling with production scheduling.
Information technology is the prevailing trend of the present era, and information management has been widely applied across various domains. The construction of intelligent healthcare services is increasingly receiving attention. In this context, the development of electronic medical records (EMRs) must keep pace. While China's EMR system started relatively late, it has achieved certain effectiveness; however, there are still areas that require improvement such as legal validity, information sharing, and system security. Scholars have conducted research on information technology related to EMR development, laws and regulations governing EMRs, and grading systems; however, these studies tend to be specialized and lack comprehensive analysis. This paper aims to provide a comprehensive analysis of the current challenges in EMR development by integrating the existing status of EMR development. It also offers comprehensive recommendations for further advancing electronic medical records. Through this discussion on EMR development, we hope to promote the establishment of intelligent healthcare services and contribute to the modernization of medical services in China.
This paper seeks to improve the security and data leakage issues associated with the Internet of Vehicles by constructing a data storage and data sharing plan that utilizes a hybrid architecture and encrypted mechanism. Firstly, we use a lattice-based encryption technique to ensure security during data sharing because lattice-based cryptography operates on the premise of quantum theoretical immunity and offers robust security assurances. Secondly, an aggregate signature is incorporated to ensure data is safeguarded from tampering during the signing process. Last but not least, the consortium blockchain technology is incorporated to ensure the immutability of data while sharing and making the records permanent and transparent. As presented in the above scheme, all the integrated technologies are used to ensure an efficient and reliable security solution for data sharing in the context of IoV.
Smart grids are the core infrastructure of power systems, and the security of their data is of great significance to the normal operation of power systems and the privacy of users. This paper designs a complete Canvas fingerprint-based identity authentication process, including the initialization of Canvas elements, the generation of drawing commands, the extraction and analysis of image data, the calculation of feature points, and the final generation of Canvas fingerprints. By simulating attack scenarios, this paper conducts a security assessment, privacy protection capability test, and performance efficiency analysis of the Canvas fingerprint method and three other traditional methods (data aggregation technology, group signature technology, and homomorphic encryption technology). The results show that the Canvas fingerprint technology demonstrates significant advantages in user identity protection, data anonymization, encryption strength, and user behavior protection. Particularly in terms of system stability and attack resistance, the Canvas fingerprint method's comprehensive security score of 87.648 is significantly higher than that of other methods. In addition, the Canvas fingerprint technology also performs outstandingly in terms of performance efficiency.
Due to the independent nature of Blockchain systems in different organizations, data sharing and asset transfer between these Blockchain systems face extreme challenges. The phenomenon of data silos is gradually emerging, and Blockchain cross chain technology provides important technical support for bridging data silos, achieving interoperability and data sharing between Blockchains. The focus of this article is on the systematic study and comparison of four widely used cross-chain technologies and platforms, and designs a Blockchain cross-chain architecture that integrates notary and relay chain technologies based on the analysis results. Compared with using a single notary mechanism or a single relay cross-chain technology, it has better scalability, stronger customization, and higher security. This work has a certain promoting effect on the development of cross chain technology.
