Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment - Amal K. Alkhalifa; Nuha Alruwais; Wahida Mansouri; Munya A. Arasi; Mohammed Alliheedi; Fouad Shoie Alallah; Alaa O. Khadidos; Abdulrhman Alshareef (Alexandria Engineering Journal)
The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches.