Security-Aware Routing on Wireless Communication for E-Health Records Monitoring Using Machine Learning

Security-Aware Routing on Wireless Communication for E-Health Records Monitoring Using Machine Learning

Sudhakar Sengan, Osamah Ibrahim Khalaf, Ganga Rama Koteswara Rao, Dilip Kumar Sharma, Amarendra K., Abdulsattar Abdullah Hamad
Copyright: © 2022 |Pages: 10
DOI: 10.4018/IJRQEH.289176
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Abstract

An ad hoc structure is self-organizing, self-forming, and system-free, with no nearby associations. One of the significant limits we must focus on in frameworks is leading. As for directions, we can send the packet or communications from the sender to the recipient node. AODV Routing Protocol, a short display that will make the tutorial available on demand. Machine Learning (ML) based IDS must be integrated and perfected to support the detection of vulnerabilities and enable frameworks to make intrusion decisions while ML is about their mobile context. This paper considers the combined effect of stooped difficulties along the way, problems at the medium get-right-of-area to impact layer, or pack disasters triggered by the remote control going off route. The AODV as the Routing MANET protocol is used in this work, and the process is designed and evaluated using Support Vector Machine (SVM) to detect the malicious network nodes.
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Introduction

They're also known as the base system and structure-less compositions. A structured system has a segment or a base station part of a cooperative association (Abdulsahib & Khalaf, 2018). Each center point in this form of system would be connected to an intermediate BS, so the cell framework falls under structure sorting (Alkhafaji et al., 20201; Al-Khanak et al., 2021). The frameworks that do not have any sections are called establishment-less frameworks. Since they don't depend on joining the association, ad hoc structures fall in this category. One of the essential techniques in collective transmission from the source to the target is guiding. There are a few different types of regulating shows in MANETs (Anandakumar & Umamaheswari, 2017). We'll talk about the AODV show here. The open shows group includes AODV organizing shows (Ayman Dawood et al., 2019) (Carlos et al., 2021; Dalal & Khalaf, 2021; Ghaida & Osamah, 2018; Hamad et al., 2021; Hoang et al., 2021).

In most cases, receptive shows are approached as solicitation guiding shows. AODV uses bi-directional connections, with the primary objective, of course, help and revelation (Keerthana et al., 2020; Khalaf & Abdulsahib, 2021; Khalaf & Abdulsahib, 2019). Transmit less often than the others and do not use TCP affiliations in most cases. Due to help floods in the daily AP bolster overwhelmed by the significant TCP affiliations, these sporadic-transmitting sensors suffer the adverse effects of starvation (Khalaf & Sabbar, 2019). In this way, increasingly semantic equivalent organizations may be crammed together, allowing for the assembly of recommendation consideration. The security concerns in MANET are demonstrated in Figure 1. This article suggests an SVM-based Intrusion Detection System (IDS) for detecting malicious nodes at the network level (Li et al., 2021; Ogudo et al., 2019). All nodes' packet routing activity is investigated using an ML method to identify and identify nodes carrying out the threat throughout this technique. An ML algorithm is designed to produce more reliable data (Perkins & Bhagwat, 1994). SVM is because it scales well with resistance to high and can function adequately with semi-structured or unstructured and structured data without complexity (Prasad et al., 2020; Priyadarshini & Sudhakar, 2015). The black hole attack is hazardous and significantly impacts the network's Average Throughput, Packet Delivery Ratio (Rajasoundaran et al., 20201; Romero et al., 2021; Sudhakar & Chenthur Pandian, 2016; Sengan et al., 2020).

Figure 1.

Security issues in MANET

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