Medical big data intrusion detection system based on virtual data analysis from assurance perspective

Medical information system is a comprehensive system which integrates the application of medicine, information, management, computer and other disciplines. It has been widely used in the social medical security system. But with the rapid development of Internet plus medical technology, the risk of malicious invasion has increased dramatically, which gradually exposes the problem of inadequate medical information security. Therefore, effective detection of medical information system network intrusion and timely prevention of network threats have become the focus of attention and research in this field. Intrusion detection is a common detection method in network security, it plays a very important role in network security. Traditional intrusion detection is mostly based on rule matching, statistics and other methods. With the advent of the era of big data, traditional intrusion detection can not play a good performance, especially in the face of massive, complex and unbalanced intrusion data. The privacy data access monitoring system based on virtual computing environment can monitor the access of privacy data in two levels, namely, tracking the flow of privacy data within the host and tracking the propagation of privacy data between hosts. In the host, we can customize the taint propagation rules to achieve fine-grained capture of privacy data violations in the virtual computing environment. Hence, this paper studies the medical data intrusion detection technology based on virtual data pipeline from the assurance perspectives. The model is designed and implemented with the discussions of the performance. The experimental results have proven that the proposed model is efficient.