Improving IoT Security with AI-Powered Anomaly Detection and Intrusion Prevention

Internet of Things(IoT) technologies are already a standard piece of technology in many sectors of business and government. Unfortunately, security attacks that target information integrity and service availability are proven to be quite effective against IoT devices and networks. In addition, contrasted to conventional Information Technology (IT) networks, the heterogeneity of the data gathered from diverse IoT gadgets, along with the disruptions experienced inside the IoT system, make it more difficult to spot aberrant behavior and compromised nodes. To ensure that fraudulent information won't be utilized in IoT-driven decision-support tools, there is an urgent requirement for robust and trustworthy anomaly detection. In this article, we suggest a Conjugate gradient-based improved GAN (CG-IGAN) anomaly detection system for IoT that can learn and collect reliable and practical features that are not severely impacted by unstable situations. The botnet dataset is collected and then it is extracted using Independent Component Analysis (ICA). The classifier then makes use of these traits to improve the precision with which it can identify malicious IoT data. The suggested CG-IGAN design is beneficial in terms of improving the accuracy of identifying malicious data when contrasted to the most advanced IoT-based anomaly detection methods, according to research findings based on real-world IoT datasets.