Improving the Security of IoT Devices by Analyzing Network Attack Data with Machine Learning Algorithms
AMAN MEHTA, Shreeya Tiwari · Research Square · 2023
Abstract This research article explores the security concerns in the Internet of Things (IoT) and aims to enhance the security of IoT devices by investigating network attacks using ordinary device network data and attack network data. The study focuses on smart healthcare systems with IoT devices and conducts botnet attacks to generate data. Statistical measures like the Pearson coefficient and entropy are used to extract relevant features from the collected data. Machine learning algorithms are employed to distinguish between normal and attack traffic, and data preprocessing techniques are applied to increase accuracy. The generated dataset is cross-evaluated with an existing dataset for authentication. The research provides insights into the behavior of normal and attack networks on IoT devices and the potential of machine learning for improving IoT device security. The article emphasizes the importance of adopting sophisticated strategies for detecting and mitigating network attacks.