Artificial intelligence-based threat detection and prevention methods for securing Internet of Things devices
Abstract
This article discusses methods for early threat detection and prevention using artificial intelligence to ensure the security of Internet of Things devices. Numerous scientific studies by scientists on this topic and the experimental methods used are studied and analyzed. Ways to improve the security of Internet of Things devices using Artificial intelligence -based systems such as machine learning and deep learning, the impact of network attacks on Internet of Things devices, anomaly detection using artificial intelligence, anomaly prevention, mitigation of the effects of attacks, and promising solutions for cyber threat management are analyzed. The most important methods for preventing and detecting threats are analyzed and proposed, such as anomaly detection, attack type classification, predictive analysis, real-time analysis, adaptability to resource-constrained devices, and vulnerability detection in the network. As a result of research and analysis, an Internet of Things security architecture is developed and described that uses artificial intelligence technologies to improve security and detect threats. The results of the study are aimed at promoting advanced research and the development of practical software solutions for using artificial intelligence -based systems to ensure the security of Internet of Things devices used in various fields.