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An IoT-Based Intrusion Detection System Approach for TCP SYN Attacks

Abdelwahed Berguiga*, Ahlem Harchay
Department of Computer Science, College of Science and Arts in Gurayat, Jouf University, Sakakah, Saudi Arabia
* Corresponding Author: Abdelwahed Berguiga. Email:

Computers, Materials & Continua 2022, 71(2), 3839-3851. https://doi.org/10.32604/cmc.2022.023399

Received 06 September 2021; Accepted 27 October 2021; Issue published 07 December 2021

Abstract

The success of Internet of Things (IoT) deployment has emerged important smart applications. These applications are running independently on different platforms, almost everywhere in the world. Internet of Medical Things (IoMT), also referred as the healthcare Internet of Things, is the most widely deployed application against COVID-19 and offering extensive healthcare services that are connected to the healthcare information technologies systems. Indeed, with the impact of the COVID-19 pandemic, a large number of interconnected devices designed to create smart networks. These networks monitor patients from remote locations as well as tracking medication orders. However, IoT may be jeopardized by attacks such as TCP SYN flooding and sinkhole attacks. In this paper, we address the issue of detecting Denial of Service attacks performed by TCP SYN flooding attacker nodes. For this purpose, we develop a new algorithm for Intrusion Detection System (IDS) to detect malicious activities in the Internet of Medical Things. The proposed scheme minimizes as possible the number of attacks to ensure data security, and preserve confidentiality of gathered data. In order to check the viability of our approach, we evaluate analytically and via simulations the performance of our proposed solution under different probability of attacks.

Keywords

IoT; intrusion detection system; denial-of-service; TCP SYN flooding; attacks

Cite This Article

A. Berguiga and A. Harchay, "An iot-based intrusion detection system approach for tcp syn attacks," Computers, Materials & Continua, vol. 71, no.2, pp. 3839–3851, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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