Blockchain-Assisted Cybersecurity for the Internet of Medical Things in the Healthcare Industry

The Internet of Medical Things (IoMT) plays an important role in strengthening sustainable healthcare systems. IoMT significantly influences our healthcare because it facilitates monitoring and checking patient medical information before transferring the data to a cloud network for future use. The IoMT is a big-data platform which is growing rapidly, so it is critical to maintain all data safely and securely. In this study, Blockchain-Assisted Cybersecurity (BCCS) for the IoMT in the healthcare industry is proposed. Blockchain is a decentralized digital ledger that allows end-to-end communication and provides interaction between untrustworthy persons. BCCS uses a conventional in-depth approach and blockchain to create a procedure for collecting medical information from the IoMT and integrated devices. The proposed system utilizes blockchain to record and extract the accumulated information in a secure and distributed manner within a closed environment suitable for healthcare professionals, such as nursing homes, hospitals, and the healthcare industry where data exchange is needed. The experimental outcomes show that the proposed system has a high security rate of 99.8% and the lowest latency rate of 4.3% compared to traditional approaches. In all, the reliability of the proposed system gives the highest rate of 99.4%

Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

Vehicular Adhoc Networks (VANETs) enable vehicles to act as mobile nodes that can fetch, share, and disseminate information about vehicle safety, emergency events, warning messages, and passenger infotainment. However, the continuous dissemination of information from vehicles and their one-hop neighbor nodes, Road Side Units (RSUs), and VANET infrastructures can lead to performance degradation of VANETs in the existing host-centric IP-based network. Therefore, Information Centric Networks (ICN) are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile, dynamic, and error-prone domains. In ICN-based Vehicular-IoT networks, consumer mobility is implicitly supported, but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular nodes. This paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information dissemination. The proposed protocol, called Network Cording Multiple Solutions Scheduling (NCMSS), is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information sources. Experimental results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching, data retrieval delay, and end-to-end application throughput. The end-to-end throughput in proposed NCMSS is 22% higher (for 1024 byte data) than existing solutions whereas delay in NCMSS is reduced by 5% in comparison with existing solutions

Fingerprinting of Relational Databases for Stopping the Data Theft

The currently-emerging technology demands sharing of data using various channels via the Internet, disks, etc. Some recipients of this data can also become traitors by leaking the important data. As a result, the data breaches due to data leakage are also increasing. These breaches include unauthorized distribution, duplication, and sale. The identification of a guilty agent responsible for such breaches is important for: (i) punishing the culprit; and (ii) preventing the innocent user from accusation and punishment. Fingerprinting techniques provide a mechanism for classifying the guilty agent from multiple recipients and also help to prevent the innocent user from being accused of the data breach. To those ends, in this paper, a novel fingerprinting framework has been proposed using a biometric feature as a digital mark (signature). The use of machine learning has also been introduced to make this framework intelligent, particularly for preserving the data usability. An attack channel has also been used to evaluate the robustness of the proposed scheme. The experimental study was also conducted to demonstrate that the proposed technique is robust against several malicious attacks, such as subset selection attacks, mix and match attacks, collusion attacks, deletion attacks, insertion attacks, and alteration attacks

Fingerprinting of Relational Databases for Stopping the Data Theft

The currently-emerging technology demands sharing of data using various channels via the Internet, disks, etc. Some recipients of this data can also become traitors by leaking the important data. As a result, the data breaches due to data leakage are also increasing. These breaches include unauthorized distribution, duplication, and sale. The identification of a guilty agent responsible for such breaches is important for: (i) punishing the culprit; and (ii) preventing the innocent user from accusation and punishment. Fingerprinting techniques provide a mechanism for classifying the guilty agent from multiple recipients and also help to prevent the innocent user from being accused of the data breach. To those ends, in this paper, a novel fingerprinting framework has been proposed using a biometric feature as a digital mark (signature). The use of machine learning has also been introduced to make this framework intelligent, particularly for preserving the data usability. An attack channel has also been used to evaluate the robustness of the proposed scheme. The experimental study was also conducted to demonstrate that the proposed technique is robust against several malicious attacks, such as subset selection attacks, mix and match attacks, collusion attacks, deletion attacks, insertion attacks, and alteration attacks