HEDL-IDS2: An Innovative Hybrid Ensemble Deep Learning Prototype for Cyber Intrusion Detection

The growing volume of online activities exposes users to potential cyber-attacks. Consequently, the scientific community aims to develop pioneering approaches capable to mitigate the risk. To address this challenge, the authors introduce the second version of the Hybrid Ensemble Deep Learning (HEDL) Intrusion Detection System (IDS), that successfully detects nine serious cyber-attacks. The architecture of the introduced Ensemble comprises of four Deep Neural Networks (DNN), four Convolutional Neural Networks (CNN) and four Recurrent Neural Networks (RNN) using Long-Short Term Memory (LSTM) layers, running in parallel. The final classification of each Ensemble employs a Custom Vote process, following the Weighted Vote and the Majority Vote principles. The HEDL-IDS2 was successfully employed on the UNSW-NB15 dataset, achieving extremely high-performance indices (Accuracy, Sensitivity, Specificity, Precision and F-1 Score) in all Training, Validation and Testing phases. This multiclass classification effort followed the One-Versus all Strategy.