IoT Information Security System Based on Artificial Intelligence
Yanyan Sai, Tao Wang · 2024
Due to the large amount of data collected and sent by Internet of Things devices, they are vulnerable to security threats. In this study, a security protection system is designed by using convolutional neural network. Through the indicator algorithm to detect the habitual behavior and traffic pattern of equipment, the security protection system can identify abnormal behavior and respond in time. In addition, the system supplements natural language processing technology to analyze and respond to security alarms, reports and events. In the experiment, based on the average response time, false alarm rate and decision stability, the performances of CNN, f CNN and RNN are compared. The results show that CNN provides lower false positive rate and false negative rate, $0.9 \%$ false positive rate and $0.31 \%$ false negative rate, which proves the efficacy of processing visual data.