Ensemble random forest and deep convolutional neural networks in detecting and classifying the multiple intrusions from near real-time cloud datasets

Due to rapidly growing Internet facilities, intruders can steal and misuse the data saved and stored digitally. In this case, securing digital data is challenging but prominent for various purposes. However, the traditional techniques are insufficient to secure these computer networks and cloud information with a 100% success rate. Recently, machine- or deep-learning-enabled methods have been used to secure network information, but with some limits. Therefore, the study emphasizes detecting and classifying network intrusion using the proposed ensemble and deep learning models. In this case, we developed the ensemble learning-enabled random forest algorithm and deep learning-enabled deep convolutional neural network (CNN) models for securing near real-time cloud information and designed the intrusion detection system accordingly. The complex and high-volume CSE-CICIDS2018 datasets were used to test the developed model in Python programming language implemented with several Python libraries. The outcome of the proposed models indicates that the developed models are promising in securing the cloud information with 97.73% and 99.91% accuracies via ensemble-random forest and deep CNN models. Thus, the present study models can be applied to other real-time datasets and computer networks to detect cyber threats effectively.