Create an Innovative Intrusion Detection System for the Internet of Things by Improving Feature Weighting Through Heuristics - Gadewar, Amol B.; Patil, Ritesh V.; Mahajan , Surendra A. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Recently, the "Internet of Things (IoT)" industry has developed as a tool for developing intelligent models of operation. Real-world applications that rely on the IoT system view privacy and security as major issues. Security issues in IoT-enabled devices pose obstacles to progress in the smart economy. As a result, "Intrusion Detection Systems (IDSs)" tailored to the IoT industries are desperately needed to curb the escalating number of attacks based on the Internet of Things. Because of their limited processing power, memory size, and battery life, traditional IDSs cannot be used in broad IoT-aided networks. Several IDSs have been proposed in academic publications as potential solutions to these issues. However, many IDSs run into problems with false positives and false negatives when looking for anomalies. In order to detect intrusion in the IoT industry and fix the problems with traditional systems, a deep learning ensemble model is suggested. In the first stage, we obtain the raw data from established sources. Consequently, the model is verified using complementary metrics. The proposed approach, on the other hand, not only overcomes the greater detection rate, but also aids in avoiding intrusion from third parties.