Enhancing IoT Security Through Sentiment Analysis: A Framework for Improved Threat Detection and Adaptive Response

Due to the massive deployment of IoT devices, security has become an issue of significant concern because conventional approaches do not suit the growing and ever-changing variety of threats. The possibility to apply such a sentiment analysis in the context of security frameworks of IoT networks can be considered a valuable addition to the current approaches to threat detection and response. This research has sought to design and test a sentiment-based IoT security model to deal with increased detection of correct, minimal false alarms, and the ability to respond to users' feelings. We used the novel BERT and other transformations of the sentiment to measure their effectiveness in strengthening IoT security besides regular security measures. The evaluation of the framework's performance was done by using different criteria such as the detection rate, number of false positive results, and response time. The enhanced detection rate was observed up to 94.2% through BERT with minimizing the false positive up to 6. 2% through sentiment analysis integration. However, it was complemented with a moderate response time impact. This is an illustration of how sentiment analysis can be used to improve the security of IoT systems as shown through the proposed framework. Although it brings enhanced threat detection and non-customer-oriented responses, optimization of its threat detection and operational efficiency is apparent. Subsequent studies will continue refining the sentiment analysis models for real-time operation while expanding the array of emotional domains to enhance the solidness of the system.