A novel approach to IoT security for intrusion detection system using ensemble network and heuristic-assisted feature fusion

The area of "Internet of Things (IoT)" has gained popularity recently as a method for developing intelligent models. Security and privacy are regarded as the two main issues in every real-world IoT application. The security risks posed by IoT-enabled devices are serious threats to the development of the smart industry. Thus, to reduce the security dangers that emanate from IoT devices and give birth to numerous security concerns, Intrusion Detection Systems (IDSs) specifically tailored for the IoT sectors are important. Conventional intrusion detection systems (IDSs) are unsuitable for use in standard Internet of Things (IoT) networks for a variety of reasons, including memory, battery life, and processing power. Various Intrusion Detection Systems (IDSs) have been proposed in academic research to address these problems. However, while identifying anomalies, various intrusion detection systems (IDSs) have issues with low accuracy and numerous false alarms. To counteract the shortcomings of conventional systems, it is recommended that an ensemble deep learning model be utilized to identify intrusions within the Internet of Things (IoT) domain. The final result is then determined by averaging the scores, which is accomplished by combining them. Divergent measurements are thus used to validate the model. On the other hand, by prohibiting unwanted access, the recommended method not only increases the detection rate but also improves network security.