An attention mechanism based recurrent neural network with dimensionality reduction model for cyber threat detection in IoT environment

Besides the developing threat of cyberattacks, cybersecurity is one of the most significant Internet of Things (IoT) regions. While the IoT has formed a novel model where a network of devices and machines is efficient in collaborating and communicating with each other, it is a novel process invention in enterprises. The role of cybersecurity is to mitigate risks for institutions and users by safeguarding data confidentiality across networks. The growing tools and technologies for cybersecurity improve safety in IoT systems. AI is helpful in improving cybersecurity by providing real-time information for faster threat detection, rapid responses, and smarter decisions. Moreover, integrating blockchain (BC) with IoT illustrates promise but encounters threats like performance issues, security vulnerabilities, and scalability limits. Still, BC plays a key part in protecting low-energy IoT devices. In this study, I proposed a novel approach using an Attention Mechanism-Based Recurrent Neural Network and Dimensionality Reduction for Cyber Threat Detection (AMRNN-DRCTD) model. The main goal of the proposed AMRNN-DRCTD model was to enhance the detection system for cyberattacks in IoT networks. I considered possible security breaches in BC and their influence on network processes. At the initial stage, the data normalization applied zero-mean normalization to alter data into a consistent setup. The feature selection process employed the chaotic and terminal strategy-based butterfly optimization algorithm (CTBOA). Furthermore, the proposed AMRNN-DRCTD model utilized the hybrid convolutional neural network and bi-directional long short-term memory with an attention mechanism (CNN-BiLSTM-AM) technique for the classification process. Finally, the Honey Badger Algorithm (HBA)-based hyperparameter selection range was accomplished to optimize the detection outcomes of the CNN-BiLSTM-AM technique. The experimental evaluation of the AMRNN-DRCTD methodology was examined under the BoT-IoT dataset. The performance validation of the AMRNN-DRCTD methodology highlighted a superior accuracy output of 99.28% over existing approaches.