A BERT-based recommender system for secure blockchain-based cyber physical drug supply chain management

【Author】 Yazdinejad, Abbas; Rabieinejad, Elnaz; Hasani, Tahereh; Srivastava, Gautam

【Source】CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS

【影响因子】2.303

【Abstract】Drug Supply Chain Management (DSCM) can be one of the most affected streams in healthcare due to pandemics. The delivery of medicine to patients through DSCM is a complex process. Also, DSCM has several challenges, including counterfeiting, fraud, and the availability of medicine. Therefore, there is a need for security and intelligence strategies to remove pharmaceutical fraud, which remains a significant challenge since ensuring fair and secure access to medicine, services, and assistance is essential in Cyber-Physical Systems (CPS)-based DSCM. The existing CPS-based DSCM systems do, however, have some limitations in security, intelligence, planning, scheduling, quality, and logistics. This paper proposes a secure drug supply chain management framework that can acheive more security and intelligence via machine learning models. The proposed framework utilizes Bidirectional Encoder Representations from Transformers (BERT)-based and machine learning-based attack detection modules to provide more intelligence and security in block chain-based DSCM. Evaluation results show that BERT-based recommender systems ideally suggest appropriate alternative drugs that are close to 99% similar to the prescribed medication based on public datasets. Moreover, attack detection in the proposed framework provides significant accuracy, precision, recall, and F-measure results in threat detection (phishing, scamming, and abnormal transactions) in the blockchain layer:

【Keywords】Supply chain; DSCM; CPS; BERT; Blockchain; Machine learning

【发表时间】2023 2023 JUN 23

【收录时间】2023-07-19

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-供应链