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Personal health records and electronic health records are considered as the most sensitive information in the healthcare domain. Several solutions have been provided for implementing the digital health system using blockchain, but there are several challenges, such as secure access control and privacy is one of the prominent issues. Hence, we propose a novel framework and implemented an attribute-based access control system using blockchain. Moreover, we have also integrated artificial intelligence (AI) based approach to identify the behavior and activity for security reasons. The current methods only focus on the related clinical records received from a medical diagnosis. Moreover, existing methods are too inflexible to resourcefully sustenance metadata changes. A secure patient data access framework is proposed in this research, integrating blockchain, trust chain, and blockchain methods to overcome these problems in the literature for sharing and accessing digital healthcare data. We have used a neural network and classifier to categorize the user access to our proposed system. Our proposed scheme provides an intelligent and secure blockchain-based access control system in the digital healthcare system. Experimental results surpass the existing solutions by collecting attributes such as the number of transactions, number of nodes, transaction delay, block creation, and signature verification time. [ABSTRACT FROM AUTHOR]

As nearly half of the incidents in enterprise security have been triggered by insiders, it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software (malware) in real-time. Failing to do so may cause a serious loss of reputation as well as business. At the same time, modern network traffic has dynamic patterns, high complexity, and large volumes that make it more difficult to detect malware early. The ability to learn tasks sequentially is crucial to the development of artificial intelligence. Existing neurogenetic computation models with deep-learning techniques are able to detect complex patterns; however, the models have limitations, including catastrophic forgetfulness, and require intensive computational resources. As defense systems using deep-learning models require more time to learn new traffic patterns, they cannot perform fully online (on-the-fly) learning. Hence, an intelligent attack/malware detection system with on-the-fly learning capability is required. For this paper, a memory-prediction framework was adopted, and a simplified single cell assembled sequential hierarchical memory (s.SCASHM) model instead of the hierarchical temporal memory (HTM) model is proposed to speed up learning convergence to achieve onthe-fly learning. The s.SCASHM consists of a Single Neuronal Cell (SNC) model and a simplified Sequential Hierarchical Superset (SHS) platform. The s.SCASHMis implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/anomalies. The experimental results show that the proposed memory model can predict users' traffic behavior with accuracy level ranging from 72% to 83% while performing on-the-fly learning. [ABSTRACT FROM AUTHOR]