| Autor: |
Mohamed Shakeel P; Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia. shakeelji@ieee.org., Baskar S; Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, India., Sarma Dhulipala VR; Department of Physics, Anna University, BIT-Campus, Tiruchirappalli, India., Mishra S; Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India., Jaber MM; Universiti Tun Hussein Onn Malasia, Parit Raja, Malaysia. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of medical systems [J Med Syst] 2018 Aug 31; Vol. 42 (10), pp. 186. Date of Electronic Publication: 2018 Aug 31. |
| DOI: |
10.1007/s10916-018-1045-z |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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