Improving intrusion detection in the IoT with African vultures optimisation algorithm-based feature selection

The security of the system may be jeopardised by unsecured data transmitted through IoT devices, and ensuring the reliability of data is critical to maintaining the integrity of information over the internet. To enhance the intrusion detection rate, several investigations have been conducted to develop methodologies capable of identifying the minimum required secure features. One such method is the use of the feature selection procedure with metaheuristic algorithms. In this study, the African vulture optimisation algorithm was used in two wrapper FS approaches to select the most secure features in IoT. The first approach used AVO, while the second employed OBL-AVO, a hybrid model combining AVO with opposition-based learning (OBL) to enhance exploration. Based on the outcomes, it was found that the OBL-AVO is superior to the AVO in enhancing FS. Furthermore, the proposed methods' were evaluated and compared to four recent approaches.