Enhancing IoT Forensics through Deep Learning: Investigating Cyber-Attacks and Analyzing Big Data for Improved Security Measures

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

“…This is a commendable achievement, especially when juxtaposed with DeepAK-IoT [37] and Inception Time [34], which represent the upper echelon of performances in this category. Notably, CNN-LSTM-GRU showed marked superiority over LNKDSEA [38] and RNN [39], underscoring the efficacy of the ensemble approach in managing the increased complexity of fine-grained classifications.…”

“…A comparative analysis, as detailed in Table 7, situates the ensemble model within the context of recent advancements, delineating its standing against contemporary architectures in the field. In the binary classification domain, CNN-LSTM-GRU achieved parity with the unassailable accuracy of its peers, where models such as CNN-LSTM [35], VGG-16 [36], and RNN [39] also have perfect scores. This uniform excellence across models underscores a maturing understanding and effective handling of binary classification tasks in the IoT security domain.…”

“…The CNN component of our ensemble model is primarily responsible for spatial feature extraction. Unlike traditional models such as the DNN [33] and RNN [39], which may lack depth in feature extraction, the CNN layers in our model provide a comprehensive analysis of the input data's spatial characteristics. This is evident in the binary classification results, where our model matches the perfect accuracy of the CNN-LSTM [35] and the VGG-16 [36], which are known for their strong feature extraction capabilities.…”