Securing the internet of things frontier: a deep learning ensemble for cyber-attack detection in smart environments - Venkataraya Premalatha, Deepa; Ramanujam, Sukumar (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
This study presents a novel and innovative approach using deep learning (DL) ensemble technique to improve the security of internet of things (IoT) by identifying intricate cyber-attacks. By utilising advanced DL models like deep neural network (DNN) and long short-term memory (LSTM), our approach significantly enhances the accuracy of categorization compared to basic models. The initial binary classifier achieved an accuracy of 85.2%, while the multi-class classifier achieved an accuracy of 79.7%. Both classifiers continually enhanced, achieving accuracies of 99.34% and 98.26%, respectively, after 100 epochs. Real-time scenario evaluations showed that the average execution time per sample record was 0.9439 ms, confirming its efficiency. The DL ensemble exhibited improved performance in comparison to traditional models, indicating its potential for wider implementation in IoT security. The study not only emphasises significant improvements in accuracy, but also emphasises the method’s ability to perform well across many evaluation measures. This study presents a thorough and pragmatic method for identifying cyber-attacks in IoT settings. The stacked ensemble technique outperforms earlier models and fulfils real-time processing requirements, offering substantial advancements in IoT security. These findings enhance both the theoretical comprehension and practical application, establishing a novel benchmark for protecting intelligent IoT systems.