Deep learning-based network anomaly detection and classification in an imbalanced cloud environment

Abstract

With the advancements in computer networking, communication between end-to-end systems has increased drastically. However, security issues have also been raised. Thus, detecting anomalies from a complex cloud environment is still challenging. Therefore, the present article proposes the deep Convolutional Neural Network (CNN) model for detecting and classifying near-real-time network intrusions from an imbalanced cloud environment. The random forest model is also offered and implemented to select the best suitable features as input to the CNN model. The experiments were carried out on CSE-CIC-IDS2018 datasets. The results show that the proposed CNN model achieved 97.07% testing accuracy with a 2.93% error rate. The performance of the proposed model was also measured using precision, recall, and f1-score with 98.11, 96.93, and 97.52%. The results are more accurate, precise, promising, and able to detect network anomalies with the highest accuracy and can be successfully used in real-time Industry 4.0 systems.