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In the contemporary digital age, education is no longer limited to traditional educational environments. Many educational institutions shifted to depend on the smart learning process but expressed concern about this solution due to its various challenges in securing the learning process and learners’ data. By virtue of the most recent technologies like blockchain and artificial intelligence, which played a significant role in solving many challenges that faced the educational sector and overcoming issues like fake certificates, manipulation, tracking learners’ activities, and predicting learners’ academic performance. The study proposed a smart framework based on blockchain and deep learning to enhance smart learning processes and provide solutions for challenges in the field. The framework is intended to store the learner’s data on the blockchain through the interplanetary file system and reap the benefits of securing the learner’s data and ensuring its integrity, as well as ensuring the confidentiality and authentication of the users through the wallets that are created on the Ethereum private blockchain platform. Then apply the deep learning model to this secured data to predict the learner’s performance. The smart contract functions also play a role in enabling the university to issue learners’ certificates that are stored on the blockchain to be available and verifiable by all the nodes in the network. Based on the experimental results, deep neural networks were used to model the encrypted data that was stored on the blockchain and predict the learner’s performance and achieved a high degree of accuracy (91.29%) and low loss (about 0.18) in comparison to other studies that depended on the centralized nature of the data. As well, the university blockchain’s functionality was tested, and it successfully returned all the functional requirements and showed its legitimacy.

Medical records are one of the crucial documents and a significant asset for anyone seeking treatment. Electronic health records (EHRs) have made a dynamic shift by making them easier to manage, facilitate and share among various stakeholders such as doctors, lab technicians, and insurance agents. EHRs are vulnerable to hacker, cybercriminal attacks, and data breaches. Once compromised, health records cannot be retrieved. As a result, patients must have control over who gets their EHRs, when they get them, and where they get them. To address the aforementioned issue, this paper proposes a blockchain-based secure record-keeping and trustworthy sharing system. In order to do this, a distributed off-chain storage architecture for large-scale medical data storage is developed, which overcomes the drawbacks of on-chain data storage and enhances scalability. The distributed storage, i.e., InterPlanetary File System, is a content-addressable storage that ensures the integrity of the content such that a slight modification in the stored EHR records results in a change in the obtained hash value. Furthermore, a Ciphertext Policy Attribute-Based Encryption (CP-ABE) algorithm integrated with blockchain technology is designed for fine-grained access control, allowing only authorized users to access specific EHR data based on their attributes. The combination of CP-ABE with blockchain technology provides a tamper-proof and verifiable audit trail of all data access and updations made to EHRs. This enhances accountability and ensures that the patients or owners can track and verify all actions taken on the data. To implement the proposed system, the Remix-Ethereum IDE is used. Smart contracts (SCs) are designed with access permissions so patients have complete control over their records. The scalability and immutability of the system is ensured by storing the hash of the encrypted EHRs on the blockchain and the actual encrypted records on IPFS. The security analysis of the proposed system is carried out by evaluating its resistance to various attacks. Additionally, potential security flaws in the proposed SCs are investigated using the Oyente tool. Different test cases are presented to demonstrate the functionality and cost analysis of the proposed system.

A diploma is a certificate or official document given by a school or college that is useful for continuing education, applying for jobs, and assessing student intelligence. The main problem with diplomas and other academic documents is that many are forged. This study aims to develop a prototype for recording student academic data using blockchain and blockchain and InterPlanetary file system (IPFS). The research stages were conducted with system conceptualization, data modeling, smart contract development, IPFS integration, data transaction development, user interface/user experience (UI/UX) development, and system testing. A blockchain is a permanent information structure formed by data blocks that are interconnected with transaction data blocks before and after it. The transaction data for each block are encrypted using asymmetric cryptography. IPFS is a peer-to-peer network protocol for storing and sharing data in a distributed file system applying the concept of decentralization to make the manipulation more difficult. The results show that student academic data and documents were successfully stored in a blockchain network using smart contracts and IPFS. Blockchain technology, smart contracts, and IPFS strengthen the value of these documents into documents that are safe, difficult to counterfeit, and easy to trace, such that authentication and integration are better preserved.

Vehicular Ad-hoc Networks (VANETs) are growing into more desirable targets for malicious individuals due to the quick rise in the number of automated vehicles around the roadside. Secure data transfer is necessary for VANETs to preserve the integrity of the entire network. Federated learning (FL) is often suggested as a safe technique for exchanging data among VANETs, however, its capacity to protect private information is constrained. This research proposes an extra level of security to Federated Q-learning by merging Blockchain technology with VANETs. Initially, traffic data is encrypted utilizing the Extended Elliptic Curve Cryptography (EX-ECC) technique to enhance the security of data. Then, the Federated Q-learning model trains the data and ensures higher privacy protection. Moreover, interplanetary file system (IPFS) technology allows Blockchain storage to improve the security of VANETs information. Additionally, the validation process of the proposed Blockchain framework is performed by utilizing a Delegated Practical Byzantine Fault Tolerance (DPBFT) based consensus algorithm. The proposed approach to federated Q-learning offered by Blockchain technology has the potential to develop VANET safety and performance. Comprehensive simulation tests are performed with several assessment criteria considered for number of vehicles 100, Throughput (102465.8 KB/s), Communication overhead (360.57 Mb), Average Latency (864.425 ms), Communication Time (19.51 s), Encryption time (0.98 ms), Decryption time (1.97 ms), Consensus delay (50 ms) and Validation delay (1.68 ms), respectively. As a result, the proposed approach performs significantly better than the existing approaches.

This paper proposes a blockchain-based node authentication model for the Internet of sensor things (IoST). The nodes in the network are authenticated based on their credentials to make the network free from malicious nodes. In IoST, sensor nodes gather the information from the environment and send it to the cluster heads (CHs) for additional processing. CHs aggregate the sensed information. Therefore, their energy rapidly depletes due to extra workload. To solve this issue, we proposed distance, degree, and residual energy-based low-energy adaptive clustering hierarchy (DDR-LEACH) protocol. DDR-LEACH is used to replace CHs with the ordinary nodes based on maximum residual energy, degree, and minimum distance from BS. Furthermore, storing a huge amount of data in the blockchain is very costly. To tackle this issue, an external data storage, named as interplanetary file system (IPFS), is used. Furthermore, for ensuring data security in IPFS, AES 128-bit is used, which performs better than the existing encryption schemes. Moreover, a huge computational cost is required using a proof of work consensus mechanism to validate transactions. To solve this issue, proof of authority (PoA) consensus mechanism is used in the proposed model. The simulation results are carried out, which show the efficiency and effectiveness of the proposed system model. The DDR-LEACH is compared with LEACH and the simulation results show that DDR-LEACH outperforms LEACH in terms of energy consumption, throughput, and improvement in network lifetime with CH selection mechanism. Moreover, transaction cost is computed, which is reduced by PoA during data storage on IPFS and service provisioning. Furthermore, the time is calculated in the comparison of AES 128-bit scheme with existing scheme. The formal security analysis is performed to check the effectiveness of smart contract against attacks. Additionally, two different attacks, MITM and Sybil, are induced in our system to show our system model’s resilience against cyber attacks.

In this paper, a blockchain-based secure data sharing mechanism is proposed for Vehicular Networks (VNs). Edge service providers are introduced along with ordinary nodes to efficiently manage service provisioning. The edge service providers are placed in the neighborhood of the ordinary nodes to ensure smooth communication between them. The huge amount of data generated by smart vehicles is stored in a distributed file storage system, known as Interplanetary File System (IPFS). It is used to tackle the issues related to data storage in centralized architectures, such as data tampering, lack of privacy, vulnerability to hackers, etc. Monetary incentives are given to edge vehicle nodes to motivate them for accurate and timely service provisioning to ordinary nodes. In response, ordinary nodes give reviews to the edge nodes against the services provided by them, which are further stored in a blockchain to ensure integrity, security and transparency. Smart contracts are used to automate the system processes without the inclusion of an intermediate party and to check the reviews given to the edge nodes. To optimize gas consumption and to enhance the system performance, a Proof of Authority (PoA) consensus mechanism is used to validate the transactions. Moreover, a caching system is introduced at the edge nodes to store frequently used services. Furthermore, both security and privacy are enhanced in the proposed system by incorporating a symmetric key cryptographic mechanism. A trust management mechanism is also proposed in this work to calculate the nodes’ reputation values based upon their trust values. These values determine the authenticity of the nodes involved in the network. Eventually, it is concluded from the simulation results that the proposed system is efficient for VNs.

Software-Defined Wide-Area Networks (SD-WAN) efficiently manage and route traffic across multiple WAN connections, enhancing the reliability of modern enterprise networks. However, the performance of SD-WANs is largely affected due to malicious activities of unauthorized and faulty nodes. To solve these issues, many machine-learning-based malicious-node-detection techniques have been proposed. However, these techniques are vulnerable to various issues such as low classification accuracy and privacy leakage of network entities. Furthermore, most operations of traditional SD-WANs are dependent on a third-party or a centralized party, which leads to issues such single point of failure, large computational overheads, and performance bottlenecks. To solve the aforementioned issues, we propose a Blockchain Federated-Learning-Enabled Trust Framework for Secure East–West Communication in Multi-Controller SD-WANs (BFL-SDWANTrust). The proposed model ensures local model learning at the edge nodes while utilizing the capabilities of federated learning. In the proposed model, we ensure distributed training without requiring central data aggregation, which preserves the privacy of network entities while simultaneously improving generalization across heterogeneous SD-WAN environments. We also propose a blockchain-based network that validates all network communication and malicious node-detection transactions without the involvement of any third party. We evaluate the performance of our proposed BFL-SDWANTrust on the InSDN dataset and compare its performance with various benchmark malicious-node-detection models. The simulation results show that BFL-SDWANTrust outperforms all benchmark models across various metrics and achieves the highest accuracy (98.8%), precision (98.0%), recall (97.0%), and F1-score (97.7%). Furthermore, our proposed model has the shortest training and testing times of 12 s and 3.1 s, respectively.

With opportunities brought by the Internet of Things (IoT), it is quite a challenge to maintain concurrency and privacy when a huge number of resource-constrained distributed devices are involved. Blockchain have become popular for its benefits, including decentralization, persistence, immutability, auditability, and consensus. Great attention has been received by the IoT based on the construction of distributed file systems worldwide. A new generation of IoT-based distributed file systems has been proposed with the integration of Blockchain technology, such as the Swarm and Interplanetary File System. By using IoT, new technical challenges, such as Credibility, Harmonization, large-volume data, heterogeneity, and constrained resources are arising. To ensure data security in IoT, centralized access control technologies do not provide credibility. In this work, we propose an attribute-based access control model for the IoT. The access control lists are not required for each device by the system. It enhances access management in terms of effectiveness. Moreover, we use blockchain technology for recording the attribute, avoiding data tempering, and eliminating a single point of failure at edge computing devices. IoT devices control the user’s environment as well as his or her private data collection; therefore, the exposure of the user’s personal data to non-trusted private and public servers may result in privacy leakage. To automate the system, smart contracts are used for data accessing, whereas Proof of Authority is used for enhancing the system’s performance and optimizing gas consumption. Through smart contracts, ciphertext can be stored on a blockchain by the data owner. Data can only be decrypted in a valid access period, whereas in blockchains, the trace function is achieved by the storage of invocation and the creation of smart contracts. Scalability issues can also be resolved by using the multichain blockchain. Eventually, it is concluded from the simulation results that the proposed system is efficient for IoT.

Current Electronic Health Record (EHR) systems exhibit several critical drawbacks, including significant vulnerabilities in security, privacy, and scalability. Traditional centralized databases are highly susceptible to cyber attacks, data breaches, and unauthorized access, posing severe risks to the confidentiality and integrity of sensitive patient information. In addition, these systems often face interoperability issues, which hinder seamless information sharing and access among healthcare providers. The absence of a unified and secure framework exacerbates these problems, leading to operational inefficiencies and increased administrative costs in the management of health records. This study introduces an innovative framework designed to overcome these challenges by integrating blockchain technology with the InterPlanetary File System (IPFS). The proposed EHRChain solution leverages attribute-based encryption to provide fine-grained access control, ensuring that only authorized users can retrieve and decrypt medical records. The IPFS framework eliminates single points of failure by storing encrypted data in a decentralized fashion, thus increasing the resilience of the system to cyber attacks. Incorporating blockchain technology ensures the immutability and traceability of medical data, significantly reducing the risks of data tampering. Performance evaluations using real data set simulation demonstrate the efficiency and feasibility of our framework, highlighting its potential to revolutionize the handling of electronic medical records. This study not only underscores the advantages of integrating blockchain with EHR systems but also presents a scalable and secure solution tailored to meet the demands of modern healthcare infrastructures. Our approach promises to improve data security, improve interoperability, and reduce operational inefficiencies, leading to a new standard in electronic health records.

The exponential growth of intelligent vehicles(IVs) development has resulted in a complex network. As the number of IVs in a network increases, so does the number of connections. As a result, a great deal of data is generated. This complexity leads to insecure communication, traffic congestion, security, and privacy issues in vehicular networks (VNs). In addition, detecting malicious IVs, data integration, and data validation are major issues in VNs that affect network performance. A blockchain-based model for secure communication and malicious IV detection is proposed to address the above issues. In addition, this system also addresses data integration and transaction validation using an encryption scheme for secure communication. A multi-chain concept separates the legitimate and malicious data into two chains: the Integrity chain (I-chain) and Fraud chain (F-chain). This multi-chain mechanism solves the storage problem and reduces the computing power. The integration of blockchain in the proposed model provides privacy, network security, transparency, and immutability. To address the storage issue, the InterPlanetary File System (IPFS) is integrated with Certificate Authority (CA). A reputation mechanism is introduced to detect malicious IVs in the network based on ratings. This reputation mechanism is also used to prevent Sybil attack. The evaluation of the proposed work is based on the cost of smart contracts and computation time. Furthermore, two attacker models are presented to prevent the selfish mining attack and the Sybil attack. Finally, a security analysis of the proposed smart contracts with their security vulnerabilities is also presented.