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With the growth of network technologies and the increase in data transmission, cyber threats have become more complicated. Artificial Intelligence allows automated detection of cyber-attacks, which is important for IoT security. To achieve this, this research examines the application of deep learning and machine learning methods to DoS protection strategies for the IoT. The study uses performance metrics, and validation methods to stimulate further research on IoT security and intrusion detection systems using ensemble learning. The objective of this study was to explore and enhance the present state of knowledge regarding IoT Security, as well as to produce intrusion detection systems employing ensemble learning. We conducted a comprehensive survey of existing literature by various authors, without undertaking any original experimentation.
Tyagi et al. (Fri,) studied this question.