BSCSO-STNN: A Big Data-Driven IoT Intrusion Detection Model
S. Ravishankar, P. Kanmani · International Journal of Electrical and Electronics Engineering · 2025
The rapid expansion of the Internet of Things (IoT) and Big Data (BD) has led to security challenges. Securing IoT-BD against cyberattacks is necessary. An increasing number of applications are being implemented on BD platforms due to the rapid proliferation of data on the Internet. As the volume of data increases, the possibility of intrusions on the platform correspondingly increases. Conventional Intrusion Detection Systems (IDS) are ineffective for managing the extensive volume of historical data and unable to fulfil the security demands of BD platforms. This research aims to propose a novel intrusion detection model using Binary Sand Cat Swarm Optimization and Spatiotemporal Transformer Neural Network (BSCSO-STNN) model to address these issues. The CIC-IoT-23 and Bot-IoT datasets are collected and applied to train the model for evaluation. The developed BSCSO-STNN model is deployed in an Apache Spark (APS) framework. The datasets are initially preprocessed in this framework with data cleaning, oversampling, label encoding, and normalization. After preprocessing, the data is applied to the BSCSO for feature selection. Using the selected features, the STNN model performs binary and multiclass classification for both datasets. The BSCSO-STNN model attained 99.08% accuracy, 98.78% detection rate, 99.02% precision, and 98.94% F1-score using the CIC-IoT-23 dataset. The model attained 99.04% accuracy, 98.81% detection rate, 98.97% precision, and 98.95% F1-score for the BoT-IoT dataset in multiclass classification. The developed model outperformed all the current models in this research and demonstrated its accuracy in detecting intrusions.