Secured and Privacy-Based IDS for Healthcare Systems on E-Medical Data Using Machine Learning Approach

Secured and Privacy-Based IDS for Healthcare Systems on E-Medical Data Using Machine Learning Approach

Sudhakar Sengan (PSN College of Engineering and Technology, India), Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq), Vidya Sagar P. (Koneru Lakshmaiah Education Foundation, India), Dilip Kumar Sharma (Jaypee University of Engineering and Technology, India), Arokia Jesu Prabhu L. (CMR Institute of Technology, India) and Abdulsattar Abdullah Hamad (Tikrit University, Iraq)
Copyright: © 2022 |Pages: 11
DOI: 10.4018/IJRQEH.289175
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Existing methods use static path identifiers, making it easy for attackers to conduct DDoS flooding attacks. Create a system using Dynamic Secure aware Routing by Machine Learning (DAR-ML) to solve healthcare data. A DoS detection system by ML algorithm is proposed in this paper. First, to access the user to see the authorized process. Next, after the user registration, users can compare path information through correlation factors between nodes. Then, choose the device that will automatically activate and decrypt the data key. The DAR-ML is traced back to all healthcare data in the end module. In the next module, the users and admin can describe the results. These are the outcomes of using the network to make it easy. Through a time interval of 21.19% of data traffic, the findings demonstrate an attack detection accuracy of over 98.19%, with high precision and a probability of false alarm.
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The technique used divide anomaly-based IDS into the motivation: statistical, supervised, unattended, clustering, soft computing, knowledge-based, and a combination of learners [4]. This research relies on unsupervised, hybrid systems and presents a comprehensive overview of the procedures requiring supervision (Hamad et al., 2021). IDS is tracking and searching for symbols of likely occurrences that intrude on information security, information protection laws, and conventional security procedures in a configured computer network (Hoang et al., 2021). Activities contain a combination of factors that cause, like malware, unauthorized Internet connectivity to system hackers, and authorized network operators endeavoring to misappropriate their protections or add other rights that they are not approved for. A software that optimizes the intrusion prevention process is an IDS (Jebril, 2021).

Although many alternatives for particular Botnet attacks have also been proposed, a practical approach that is not limited to any specific attacks remains uncommon. We are breaking away from such an alternative from related research classes: genetic, behavioral techniques to detect Botnet viruses and vulnerability scanning of Markov chains and Hidden Models (Keerthana et al. 2020; Khalaf & Abdulsahib, 2021).

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