Data Enhancing Cybersecurity through Botnet Security Isolation for Smart IoT Devices: A Deep Learning Approach

Abstract

The Internet of Things (IoT) sector continues to expand rapidly to connect more than billions of devices throughout healthcare settings as well as transportation domains and smart residential spaces. Amazing network connectivity affords IoT systems to multiple security problems especially through botnet attacks that utilize illegally gained control over IoT devices to carry out harmful operations. Security solutions from the past struggle to stop and address these attacks because IoT devices present various resource limitations together with their diverse operational characteristics. The proposed research presents a deep learning botnet detection system for IoT networks by applying Convolutional Neural Networks (CNNs) together with Long Short-Term Memory Networks (LSTMs) and Autoencoders for analyzing IoT traffic patterns. The models received training using Bot-IoT dataset to find their optimal performance through accuracy and precision and recall and F1-score evaluations. CNN generates better results than other examined models by achieving 94% accuracy while LSTM obtains 92% and Autoencoder provides 88%. The research established CNN as the best model for traditional botnet detection yet LSTM showed exceptional capability in detecting temporal patterns and Autoencoder achieved the best results for identifying new botnet traffic types. The system displays encouraging performance however it encounters technical challenges because of its issues with processing real-time data and model scalability issues as well as unbalanced IoT datasets. According to research findings deep learning models specifically Convolutional Neural Networks demonstrate substantial potential for enhancing botnet detection but ongoing research must focus on performance enhancement techniques for realistic ecosystem deployment and handling various IoT network configurations and scalability considerations.