Botnet Detection in IoT Networks: A Review of Deep Learning Techniques and Performance Metrics
Keywords:
Networks, Botnet Detection, Deep Learning, CNN, RNN, LSTM, Hybrid Models, Intrusion Detection, Network Security, Time-Series Analysis.Abstract
The exponential growth of Internet of Things (IoT) networks has transformed industries and daily life but has also exposed significant security vulnerabilities, making IoT devices highly susceptible to botnet attacks. This study examines the application of deep learning techniques to effectively detect and mitigate botnet threats in IoT environments. Models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and hybrid architectures have proven effective in analyzing complex network traffic and identifying malicious behaviors. The study evaluates the performance of these models, including hierarchical frameworks and hybrid approaches, across benchmark datasets like BoT-IoT and N-BaIoT. It also explores enhancements like feature extraction, quantization techniques, and adaptive learning strategies. While these methods show high accuracy and efficiency, challenges such as resource limitations, evolving attack strategies, and the lack of standardized datasets persist. This study emphasizes the need for innovative and scalable solutions, highlighting the potential of advanced techniques to strengthen IoT security and ensure robust botnet detection systems for evolving cyber threats.
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Copyright (c) 2025 Jahanvi Dubey , Deepshikha Patel
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