Advances in IoT networks using privacy-preserving techniques with optimized multi-head self-attention model for...

The advances in the Internet of Things (IoT) involve a technology of interconnected devices that interact over the internet, providing convenience and efficiency while also posing significant security risks. Privacy-preserving techniques play a vital role in safeguarding sensitive user data while maintaining system efficiency. The rising tendency of cybersecurity threats and the need to recognize harmful activities in heterogeneous but resource-constrained settings have led to the development of sophisticated intrusion detection systems (IDSs) for quickly identifying intrusion efforts. Conventional IDSs are becoming more inefficient in classifying new attacks (zero-day attacks) whose designs are similar to any threat signatures. To reduce these restrictions, projected IDS depend on deep learning (DL). Due to DL techniques learning from vast amounts of data, they can identify novel, emerging attacks, making them an alternative method to classical cybersecurity. This study proposes an Optimised Multi-Head Self-Attention Model for an Intelligent Intrusion Detection Framework Using Plant Rhizome Growth Optimisation (OMHSA-IDPRGO) method to advance IoT security. The primary focus is on developing an automated cyberattack detection system for an IoT environment by employing advanced techniques. Initially, the mean normalization process is used to measure input data into a structured format. Furthermore, the Crayfish Optimisation Algorithm (COA) is used for optimal feature subset selection, identifying the most relevant features from the dataset. For the cybersecurity detection process, the OMHSA-IDPRGO method uses a hybrid model that encompasses a convolutional neural network and a bidirectional gated recurrent unit with a multi-head self-attention mechanism (CNN-BiGRU-MHSAM) technique. Finally, the hyperparameter selection is performed using the plant rhizome growth optimization (PRGO) approach to enhance classification performance. The experimentation of the OMHSA-IDPRGO model is examined under Edge-IIoT and ToN-IoT datasets. The comparison study of the OMHSA-IDPRGO model showed superior accuracy values of 99.11 and 99.18% compared to existing techniques on the dual datasets.