Chapter Fifteen - Modeling of cyber threat analysis and vulnerability in IoT-based healthcare systems during COVID

Abstract

The fast advancement of IoT technology has transformed human existence by introducing the facilitation of home automation, traffic systems, e-healthcare, oil and gas stations, and smart vehicles. The cyber threat and vulnerabilities injection have increased to a great extent during the COVID-19 period, when the world went into virtual mode for technology usage. The security of IoT devices has been a major apprehension in recent years, particularly in the healthcare industry, where recent threats by cyber criminals and terrorists have shown severe IoT security flaws. The IoT-Attributes and Threat Analyzer for Tracing Malicious Traffic in Smart Sensor Environment, abbreviated as IoT-ATATMT, is presented in the proposed work to address the aforementioned challenge and is based on the IoT-Flock, an open-source sensor data generating tool. The IoT-Flock tool creates a use-case for both regular and suspicious smart devices to analyze traffic. In addition, the work also includes an open-source application for transforming IoT-recorded Flock’s traffic into an IoT dataset. We first created an IoT healthcare dataset using the suggested architecture in this study, which includes both regular and IoT attack traffic. After that, we used several machine learning algorithms for detecting cyber threats to safeguard the e-healthcare system toward using them via a created dataset.