Extreme Learning Machine Based System for DDoS Attacks Detections on IoMT Devices

TL;DR

This paper proposes an extreme learning machine-based system to detect DDoS attacks on IoMT devices, aiming to improve healthcare security with high accuracy and low implementation costs suitable for fog computing environments.

Contribution

It introduces a novel ELM-based detection approach specifically designed for IoMT devices, emphasizing low-cost, high-accuracy DDoS attack detection at the fog level.

Findings

Achieves high detection accuracy for DDoS attacks on IoMT.

Reduces implementation costs enabling fog-level deployment.

Supports enhanced security in healthcare IoMT networks.

Abstract

The Internet of Medical Things (IoMT) represents a paradigm shift in the healthcare sector, enabling the interconnection of medical devices, sensors, and systems to enhance patient monitoring, diagnosis, and management. The rapid evolution of IoMT presents significant benefits to the healthcare domains. However, there is a rapid increase in distributed denial of service (DDoS) attacks on the IoMT networks due to several vulnerabilities in the IoMT-connected devices, which negatively impact patients' health and can even lead to deaths. Thus, in this paper, we aim to save lives via investigating an extreme learning machine for detecting DDoS attacks on IoMT devices. The proposed approach achieves a high accuracy at a low implementation budget. Thus, it can reduce the implementation cost of the DDoS detection system, making the model capable of executing on the fog level.