An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments.

The fast development of Internet of Things (IoT) tools in smart cities has presented many advantages, improving sustainability, automation, and urban efficiency. Still, these interlinked systems further pose critical cybersecurity difficulties, including cyberattacks, data breaches, and unauthorized access that may compromise essential frameworks. Usually, cybersecurity is considered a group of processes and technologies intended to safeguard networks, computers, data, and programs against malicious attacks, harm, activities, or unauthorized access. IoT cybersecurity targets to minimize cybersecurity threats for users and organizations regarding the safety of IoT assets and confidentiality. Novel cybersecurity technologies are continually developing and give opportunities and challenges to IoT cybersecurity organizations. Deep learning (DL) is one of the main technologies of today's smart cybersecurity policies or systems for functioning intelligently. This paper presents a Fox Optimizer-Based Feature Selection with Deep Learning for Securing Cyberattack Detection (FOFSDL-SCD) model. This paper aims to analyze cybersecurity-driven approaches for enhancing IoT networks' resilience and threat detection capabilities using advanced techniques. Initially, the data pre-processing stage utilizes the min-max normalization method to transform the input data into a beneficial system. Furthermore, the FOFSDL-SCD model utilizes the Fox optimizer algorithm (FOA) method for the feature selection process to select the most significant features from the dataset. Moreover, the temporal convolutional network (TCN) model is employed for classification. Finally, the dung beetle optimization (DBO)-based hyperparameter selection method is performed to improve the classification outcomes of the TCN model. The performance validation of the FOFSDL-SCD approach is examined under the Edge-IIoT dataset. The comparison study of the FOFSDL-SCD approach demonstrated a superior accuracy, precision, recall, and F1-Score of 99.38%, 96.27%, 96.26%, and 96.27% over existing models.