Guardians of IoT: Malware Analysis of IoT Devices Using Machine Learning

Abstract

The proliferation of Internet of Things (IoT) devices has introduced a new frontier for cyber threats, with malware targeting these devices becoming increasingly prevalent. This research paper presents an in-depth analysis of IoT malware using machine learning algorithms. We leverage the IoT-23 dataset, a comprehensive collection of network traffic data from both malicious and benign IoT devices, to develop and evaluate machine learning models for malware detection. Our study begins with data preprocessing, including data cleaning and feature engineering, to prepare the dataset for analysis. We explore the characteristics of the IoT-23 dataset, revealing insights into the protocols and behaviors of IoT malware. To enhance the predictive capabilities of our models, we employ techniques such as one-hot encoding to handle categorical variables effectively. We experiment with several machine learning algorithms, including Random Forest, Logistic Regression, K-Nearest Neighbors, and Naive Bayes, to classify network traffic into either benign or malicious categories. We evaluate the performance of these models using metrics such as accuracy, precision, recall, and F1-score. Additionally, we investigate the feature importance and correlations among different attributes to better understand the dataset. Our research findings shed light on the effectiveness of machine learning in detecting IoT malware, with implications for enhancing the security of IoT ecosystems. Employing machine learning models makes it possible to detect and mitigate IoT malware threats, ultimately safeguarding the integrity and privacy of IoT devices and networks. This paper contributes to the growing body of knowledge in IoT security and provides a foundation for further research in this critical domain.