Abstract
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.
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References
Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795
Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435
Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021a) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609
Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021b) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250
Agostino C, Bellandi V, Bezzi M (2018) Model-based big data analytics-as-a-service: take big data to the next level. IEEE Trans Serv Comput PP(99):1–1
Alipour J, Karimi A, Ebrahimi S et al (2017) Success or failure of hospital information systems of public hospitals affiliated with Zahedan University of Medical Sciences: a cross sectional study in the Southeast of Iran. Int J Med Inform 108(10):49–54
Arafa P, Tchamgoue GM, Kashif H et al (2017) QDIME: QoS-aware dynamic binary instrumentation. In: Proceedings of the IEEE international symposium on modeling, pp 132–142. IEEE
Banerjee S, Devecsery D, Chen PM et al (2019) Iodine: fast dynamic taint tracking using rollback-free optimistic hybrid analysis. In: Proceedings of the network and distributed system security symposium, pp 1–15. IEEE
Chen X, Gao Y (2017) Review on classification of unbalanced data. J Shaoyang Univ (nat Sci Ed) 14(2):1–11
da Silva AC, Sierra-Franco CA, Silva-Calpa GF, Carvalho F, Raposo AB (2020) Eye-tracking data analysis for visual exploration assessment and decision making interpretation in virtual reality environments. In: 2020 22nd symposium on virtual and augmented reality (SVR), pp 39–46. IEEE
Dash T (2017) A study on intrusion detection using neural networks trained with evolutionary algorithms. Soft Comput 21(10):2687–2700
Ding S (2018) Research on key technologies of intrusion detection based on deep learning. Beijing Jiaotong University, Beijing
Kakihata EM, Sapia HM, Oikawa RT (2017) Intrusion detection system based on flows using machine learning algorithms. IEEE Lat Am Trans 15(10):1988–1993
Kaur K, Garg S, Kaddoum G, Guo S (2020) ESP-VDCE: energy, SLA, and price-driven virtual data center embedding. In: ICC 2020–2020 IEEE international conference on communications (ICC), pp 1–7. IEEE
Kong L (2018) Research on intrusion detection based on deep learning and transfer learning. Shandong University, Jinan
Lin TY, Goyal P, Girshick R et al (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988
Lu J, Cai L, Zhang Y (2017) Path reduction of multiple test points in dynamic symbolic execution. In: Proceedings of the international conference on computer & information science, pp 857–863. IEEE
Miao X, Liu Y, Zhao H et al (2018) Distributed online one-class support vector machine for anomaly detection over networks. IEEE Trans Cybern PP(99):1–14
Network security analysis report of China's medical and health industry. http://www.sohu.com/a/198849226_ 490113. 19 Oct 2017
Ohsaki M, Wang P, Matsuda K et al (2017) Confusion-matrix-based kernel logistic regression for imbalanced data classification. IEEE Trans Knowl Data Eng 29(9):1806–1819
Potteti S, Parati N (2018) Intrusion detection system using hybrid fuzzy genetic algorithm. In: Trend in electronics and informatics (ICEI). Tirunelveil, India, pp 613–618. IEEE, 2017
Ruan B (2018) China Network Security Report 2017. Comput Netw (5)
Safaldin M, Otair M, Abualigah L (2021) Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. J Ambient Intell Humaniz Comput 12(2):1559–1576
Schramm M, Pebesma E, Milenković M, Foresta L, Dries J, Jacob A, Wagner W et al (2021) The openeo api–harmonising the use of earth observation cloud services using virtual data cube functionalities. Remote Sens 13(6):1125
Sohu net (2017) Worldwide blackmail virus "eternal blue" what is going on [EB]. http://www.sohu.com/a/140466216_744523. May 2017
Sujata B, Kiran R (2017) Combining fuzzy C-means and KNN algorithm in performance improvement of intrusion detection system. Lect Not Data Eng Commun Technol 9(21):359–370
Wang Y, Zhou H, Feng H et al (2018) Network traffic classification method based on deep convolution neural network. Acta Commun Sin 39(1):14–23
Wei C, Xiaoshuang Y, Chengrui D (2017) Leakdetector: automated privacy leak detection method. Eng Sci Technol 49(1):169–175
Xin X (2017) Discussion on computer network database security technology scheme. Electron World 14:101–101
Xu C, Shen J, Du X et al (2018) An intrusion detection system using a deep neural network with gated recurrent units. IEEE Access 6:48697–48707
Yan C (2017) Blackmail virus, a cyber crime that makes the world panic [EB] http://www.sohu.com/a/142124029_505860. May 2017
Zhang F (2018) Research on ensemble classification algorithm of unbalanced data based on oversampling. Zhengzhou University, Zhengzhou
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Cai, Y., Li, D. & Wang, Y. Medical big data intrusion detection system based on virtual data analysis from assurance perspective. Int J Syst Assur Eng Manag 12, 1106–1116 (2021). https://doi.org/10.1007/s13198-021-01279-5
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DOI: https://doi.org/10.1007/s13198-021-01279-5