IoT Network Security Anomaly Detection and Classification using Deep Learning

Abstract

The Internet of Things (IoT) is an expanding network of interconnected devices exposed to growing cyber security threats. Integrating AI-powered solutions presents a promising avenue for enhancing anomaly detection and classification. This study delves into developing a comprehensive methodology leveraging machine learning and deep learning techniques. Utilizing the BoTNeTIoT-L01 dataset, meticulously curated from IoT devices, the research focuses on data gathering, preprocessing, and exploratory data analysis to unearth underlying patterns and anomalies within network traffic data. Subsequently, a suite of machine learning models, including Logistic Regression, LightGBM (Light Gradient-Boosting Machine), and Decision Tree, along with a deep learning model optimized with the Adam optimizer, is employed to detect and classify anomalies effectively. The comparative analysis underscores the superior performance of advanced models such as LightGBM and Decision Tree, showcasing their efficacy in accurately identifying security threats within IoT environments. The study also addresses pertinent technical challenges, ethical considerations, and future directions, emphasizing the imperative for responsible deployment and ongoing innovation in AI-powered IoT security solutions.