Abstract
In the recent past, Internet of Things (IoT) plays a significant role in different applications such as health care, industrial sector, defense and research etc.… It provides effective framework in maintaining the security, privacy and reliability of the information in internet environment. Among various applications as mentioned health care place a major role, because security, privacy and reliability of the medical information is maintained in an effective way. Even though, IoT provides the effective protocols for maintaining the information, several intermediate attacks and intruders trying to access the health information which in turn reduce the privacy, security and reliability of the entire health care system in internet environment. As a result and to solve the issues, in this research Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information. This method examines the medical information in different layers according to the Q-learning concept which helps to minimize the intermediate attacks with less complexity. The efficiency of the system has been evaluated with the help of experimental results and discussions.
Similar content being viewed by others
Change history
12 May 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s10916-022-01827-x
References
Nordrum, A., Popular Internet of Things Forecast of 50 Billion Devices by 2020 Is Outdated. IEEE. 2016.
Hsu, C.-L., and Lin, J. C.-C., An empirical examination of consumer adoption of internet of things services: Network externalities and concern for information privacy perspectives. Comput. Hum. Behav. 62:516–527, 2016. https://doi.org/10.1016/j.chb.2016.04.023.
Vongsingthong, S., Smanchat, S., Internet of Things: A review of applications & technologies" (PDF). Suranaree J. Sci. Technol. 2014.
Kang, W. M. , Moon, S. Y, Park, J. H., An enhanced security framework for home appliances in smart home. Human-centric Comput. Inform. Sci. 7 (6). 2017. doi:https://doi.org/10.1186/s13673-017-0087-4. Retrieved 3 November 2017.
Istepanian, R., Hu, S., Philip, N., and Sungoor, A., The potential of internet of m-health things "m-IoT" for non-invasive glucose level sensing. Ann Int. Conf IEEE Eng. Med. Biol. Soc. (EMBC)., 2011. https://doi.org/10.1109/IEMBS.2011.6091302.
Feamster, N., Mitigating the Increasing Risks of an Insecure Internet of Things. Freedom to Tinker. 2017.
Alshehri, S., Radziszowski, S. P., and Raj, R. K., Secure access foe healthcare data in the cloud using Ciphertext-policy attribute-based encryption. Arlington: IEEE 28th Int. Conf. on Data Engineering Workshops, 2012, 143–146.
Mxoli, A., Gerber, M., and Phipps, N. M., Information security risk measures for cloud based personal Heath Records. London: IEEE Int. Conf. on Information Society, 2014, 187–193.
Abu Alsheikh, M., Lin, S., Niyato, D., and Tan, H. P., Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Commun. Surv. Tutorial. 16(4):1996–2018, 2014.
Granjal, J., Monteiro, E., and Silva, J. S., Security for the internet of things: A survey of existing protocols and open research issues. IEEE Comm. Surv. Tutorial. 17(3):thirdquarter, 2015.
Kaikang, Z.-B., and Congwang, Security and privacy mechanism for health internet of things. J. Chin. Univ. Posts. Telecomm. 20(2):64–68, 2013.
Sultan Alasmari; Mohd Anwar, Security & Privacy Challenges in IoT-Based Health Cloud, International Conference on Computational Science and Computational Intelligence (CSCI) in IEEE, 2016.
Steele, R., and Clarke, A., The internet of things and next-generation public health information systems. Commun. Netw. 5:4–9, 2013.
Perera, C., Zaslavsky, A., Christen, P., and Georgakopoulos, D., Context aware computing for the internet of things: A survey. IEEE Comm. Surv. Tutorial. 16(1):First Quarter, 2014.
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., and Ayyash, M., Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Comm. Surv. Tutorial. 17(4):Fourthquarter, 2015.
Zanella, A., Bui, N., Castellani, A., Vangelista, L., Zorzi, M., Internet of Things for Smart Cities. IEEE Internet Things J. 1(1), 2014.
Li, F., Xiong, P., Practical Secure Communication for Integrating Wireless Sensor Networks Into the Internet of Things. IEEE Sensors J. 13(10), 2013.
Chze, P. L. R., Leong, K. S., A secure multi-hop routing for IoT communication. IEEE World Forum on Internet of Things (WF-IoT), 2014.
Tan, Z., Jamdagni, A., He, X., Nanda, P., and Liu, R. P., A system for denial-of-service attack detection based on multivariate correlation analysis. IEEE Trans. Paral. Distrib. Syst. 25(2):447–456, 2013.
Yan, Z., Zhang, P., and Vasilakos, A. V., A survey on trust management for internet of things. J. Netw. Comput. Appl. 42(3):120–134, 2014.
Szegedy, Christian; Toshev, Alexander; Erhan, Dumitru, Deep neural networks for object detection. Adv. Neu. Inform. Proc. Syst., 2013.
Polikar, R., Ensemble based Systems in Decision Making (PDF). IEEE Circ. Syst. Mag. 6(3):21–45, 2006 [permanent dead link] a tutorial article on ensemble systems including pseudocode, block diagrams and implementation issues for AdaBoost and other ensemble learning algorithms.
Pujol, J., The solution of nonlinear inverse problems and the Levenberg-Marquardt method. Geophysics. SEG. 72(4):W1–W16, 2007. https://doi.org/10.1190/1.2732552.
Riedmiller, M., Gabel, T., Hafner, R., and Lange, S., Reinforcement learning for robot soccer. Auton. Robot. 27:55–73, 2009.
Krizhevsky, A., Sutskever, I., and Hinton, G. E., Imagenet classification with deep convolutional neural networks (PDF). Adv. Neural Inf. Proces. Syst. 1:1097–1105, 2012.
Collobert, R., Bengio, S.. Links between Perceptrons, MLPs and SVMs. Proc. Int'l Conf. on Machine Learning (ICML), 2004.
Schneider, P., Hammer, B., and Biehl, M., Adaptive relevance matrices in learning vector quantization. Neural Comput. 21:3532–3561, 2009. https://doi.org/10.1162/neco.2009.10-08-892.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of Interest
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethical Approval
All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments.
Informed Consent
Informed consent was obtained from all individual participants included in the study.
Additional information
This article is part of the Topical Collection on Mobile & Wireless Health
About this article
Cite this article
Mohamed Shakeel, P., Baskar, S., Sarma Dhulipala, V.R. et al. RETRACTED ARTICLE: Maintaining Security and Privacy in Health Care System Using Learning Based Deep-Q-Networks. J Med Syst 42, 186 (2018). https://doi.org/10.1007/s10916-018-1045-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-018-1045-z