Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms

Abstract

The Internet of Things (IoT) shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones. Contrarily, storing and operating information and maintaining its confidentiality and security is a concerning issue in the IoT. Throughout the whole operational process, considering transparency in its privacy, data protection, and disaster recovery, it needs state-of-the-art systems and methods to tackle the evolving environment. This research aims to improve the security of IoT devices by investigating the likelihood of network attacks utilizing ordinary device network data and attack network data acquired from similar statistics. To achieve this, IoT devices dedicated to smart healthcare systems were utilized, and botnet attacks were conducted on them for data generation. The collected data were then analyzed using statistical measures, such as the Pearson coefficient and entropy, to extract relevant features. Machine learning algorithms were implemented to categorize normal and attack traffic with data preprocessing techniques to increase accuracy. One of the most popular datasets, known as BoT-IoT, was cross-evaluated with the generated dataset for authentication of the generated dataset. The research provides insight into the architecture of IoT devices, the behavior of normal and attack networks on these devices, and the prospects of machine learning approaches to improve IoT device security. Overall, the study adds to the growing body of knowledge on IoT device security and emphasizes the significance of adopting sophisticated strategies for detecting and mitigating network attacks.

Keywords

• Internet of Things (IoT)
• botnet
• pearson coefficient
• random forest
• ensemble learning