Abstract:The Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures are highly susceptible to many security vulnerabilities and network intrusions, which bring challenges such as lack of privacy, integrity, trust, and centralization. This research aims to implement an Artificial Intelligence… Show more
“…According to the computed outcomes, it can be seen that the suggested scheme's entropy is effectively raised for all key lengths. Furthermore, the data transfer and verification times are determined by the conventional standard 31 , and the suggested security models are evaluated concerning different cryptographic lengths, as shown in Fig 11 . Figure 8 Storage efficiency.…”
Section: Resultsmentioning
confidence: 99%
“…Based on the detailed review 31 , 32 , it is analyzed that the conventional works encountered some of the following potentially difficult issues: Maintaining data security and privacy in the electronic medical record system remains a key issue that must be resolved. Key pair management is also considered a big issue in healthcare security systems.…”
Many researchers have been interested in healthcare cybersecurity for a long time since it can improve the security of patient and health record data. As a result, a lot of research is done in the field of cybersecurity that focuses on the safe exchange of health data between patients and the medical setting. It still has issues with high computational complexity, increased time consumption, and cost complexity, all of which have an impact on the effectiveness and performance of the complete security system. Hence this work proposes a technique called Consultative Transaction Key Generation and Management (CTKGM) to enable secure data sharing in healthcare systems. It generates a unique key pair based on random values with multiplicative operations and time stamps. The patient data is then safely stored in discrete blocks of hash values using the blockchain methodology. The Quantum Trust Reconciliation Agreement Model (QTRAM), which calculates the trust score based on the feedback data, ensures reliable and secure data transfer. By allowing safe communication between patients and the healthcare system based on feedback analysis and trust value, the proposed framework makes a novel contribution to the field. Additionally, during communication, the Tuna Swarm Optimization (TSO) method is employed to validate nonce verification messages. Nonce message verification is a part of QTRAM that helps verify the users during transmission. The effectiveness of the suggested scheme has been demonstrated by comparing the obtained findings with other current state-of-the-art models after a variety of evaluation metrics have been analyzed to test the performance of this security model.
“…According to the computed outcomes, it can be seen that the suggested scheme's entropy is effectively raised for all key lengths. Furthermore, the data transfer and verification times are determined by the conventional standard 31 , and the suggested security models are evaluated concerning different cryptographic lengths, as shown in Fig 11 . Figure 8 Storage efficiency.…”
Section: Resultsmentioning
confidence: 99%
“…Based on the detailed review 31 , 32 , it is analyzed that the conventional works encountered some of the following potentially difficult issues: Maintaining data security and privacy in the electronic medical record system remains a key issue that must be resolved. Key pair management is also considered a big issue in healthcare security systems.…”
Many researchers have been interested in healthcare cybersecurity for a long time since it can improve the security of patient and health record data. As a result, a lot of research is done in the field of cybersecurity that focuses on the safe exchange of health data between patients and the medical setting. It still has issues with high computational complexity, increased time consumption, and cost complexity, all of which have an impact on the effectiveness and performance of the complete security system. Hence this work proposes a technique called Consultative Transaction Key Generation and Management (CTKGM) to enable secure data sharing in healthcare systems. It generates a unique key pair based on random values with multiplicative operations and time stamps. The patient data is then safely stored in discrete blocks of hash values using the blockchain methodology. The Quantum Trust Reconciliation Agreement Model (QTRAM), which calculates the trust score based on the feedback data, ensures reliable and secure data transfer. By allowing safe communication between patients and the healthcare system based on feedback analysis and trust value, the proposed framework makes a novel contribution to the field. Additionally, during communication, the Tuna Swarm Optimization (TSO) method is employed to validate nonce verification messages. Nonce message verification is a part of QTRAM that helps verify the users during transmission. The effectiveness of the suggested scheme has been demonstrated by comparing the obtained findings with other current state-of-the-art models after a variety of evaluation metrics have been analyzed to test the performance of this security model.
“…The study explores how blockchain technology might successfully address difficulties in e-commerce. Their research focuses on blockchain applications in payment systems, security, supply chain management, and e-commerce transparency [15]. The study concludes that blockchain technology promotes transparency and trust, providing customers with anti-fraud protection on e-commerce platforms.…”
The safety and security of e-commerce transactions are critical in today's digital landscape, where cyber risks abound. Blockchain technology's decentralized, transparent, and immutable ledger system offers a viable solution to these concerns. This study investigates the use of Distributed Ledger Technology (DLT), specifically Decentralized Identifiable Distributed Ledger Technology (DIDLT), in conjunction with the innovative Blockchain Consent Algorithm (BCA), to improve the safety and security of e-commerce transactions. Secure data storage and retrieval in e-commerce can be achieved by accurate digital signature production, key generation, blockchain building, and validation. This paper investigates how DIDLT, which combines decentralized identity management with blockchain technology, and BCA, a cutting-edge consensus algorithm designed for blockchain environments, work together to protect e-commerce transactions from identity theft, payment fraud, and data manipulation. This paper throws insight into DIDLT and BCA's potential to transform the e-commerce safety and security market by providing an in-depth analysis of their implementation. Using the incorporated DIDLT-BCA model significantly improves the safety effectiveness of the network, resulting in 98% security, shorter performance times of as much as 150 milliseconds, and mining times of up to 0.98 s.
“…It provides a transparent audit trail of security events, enhancing reliability and fault tolerance against cyber threats [87,90], and strengthening IDS security. The immutability of blockchain prevents unauthorized alterations once data are recorded, while distributed nodes fortify resilience and resistance against attacks [88,89,94]. Encryption further safeguards identity data, making any attempts to alter data detectable due to the blockchain's transparent nature [87,93].…”
Section: Role Of Blockchain In Enhancing Ids Securitymentioning
This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper’s structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT.
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