Securing IoT Devices Running PureOS from Ransomware Attacks: Leveraging Hybrid Machine Learning Techniques

Tariq Ahamed Ahanger (), Usman Tariq (), Fadl Dahan, Shafique A. Chaudhry and Yasir Malik
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Tariq Ahamed Ahanger: Management Information System Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
Usman Tariq: Management Information System Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
Fadl Dahan: Department of Management Information Systems, College of Business Administration-Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Shafique A. Chaudhry: Reh School of Business, Clarkson University, Potsdam, NY 13699, USA
Yasir Malik: Department of Computer Science, Faculty of Science, Bishops University, 2600 Rue College, Sherbrooke, QC J1M 1Z7, Canada

Mathematics, 2023, vol. 11, issue 11, 1-24

Abstract: Internet-enabled (IoT) devices are typically small, low-powered devices used for sensing and computing that enable remote monitoring and control of various environments through the Internet. Despite their usefulness in achieving a more connected cyber-physical world, these devices are vulnerable to ransomware attacks due to their limited resources and connectivity. To combat these threats, machine learning (ML) can be leveraged to identify and prevent ransomware attacks on IoT devices before they can cause significant damage. In this research paper, we explore the use of ML techniques to enhance ransomware defense in IoT devices running on the PureOS operating system. We have developed a ransomware detection framework using machine learning, which combines the XGBoost and ElasticNet algorithms in a hybrid approach. The design and implementation of our framework are based on the evaluation of various existing machine learning techniques. Our approach was tested using a dataset of real-world ransomware attacks on IoT devices and achieved high accuracy (90%) and low false-positive rates, demonstrating its effectiveness in detecting and preventing ransomware attacks on IoT devices running PureOS.

Keywords: ransomware detection; machine learning; malware analysis; feature extraction; Internet of Things (IoT) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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