Detecting Iot Botnet Attacks: A Machine Learning Paradigm

The rise of IoT gadgets has provided immense connectivity and ease, yet it has also created opportunities for malicious individuals to exploit weaknesses and launch botnet attacks. This paper introduces a inclusive and extensive strategy for preventing and detecting IoT botnet attacks, leveraging integrated machine learning approaches.Our methodology comprises a dual-layered system that combines traditional machine learning and deep learning techniques to establish a robust defense mechanism. Initially, a machine learning model is trained on a dataset which is labelled encompassing normal and botnet traffic patterns from devices of IoT. This adaptive model evolves alongside emerging attack patterns, providing an initial layer of defense against known and evolving threats.To enhance precision and discern sophisticated attack patterns, we incorporate deep learning using neural network architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). These architectures analyze raw IoT network data, uncovering intricate patterns that may elude traditional security measures.Real-world experiments validate the efficacy of our integrated approach, demonstrating superior accuracy and robustness in preventing and detecting IoT botnet attacks. The results position our strategy as a potent safeguard for IoT ecosystems, offering proactive defense against the dynamic landscape of evolving threats.