Detecting Network Intrusion in Cloud Environment Through Ensemble Learning and Feature Selection Approach

The use of the Internet is enhanced drastically in the current era, which connects multiple computers in a network and a group of devices. In addition, every sector uses the Internet to communicate and send data digitally. However, the Internet is affected due to unwanted activities and cyber-attacks by attackers. Hence, intrusion detection systems have recently been used to detect incoming attacks. Therefore, the present study has designed and developed the intrusion detection scheme for cloud computing through ensemble learning and a feature selection approach. The proposed system is tested on NSL-KDD datasets. The critical features were selected from the dataset, and dimensionality was reduced using feature selection methods. The ensemble learning approach combined the single process to generate the robust way and successfully confirmed with high accuracy and negligible error rate. Two machine learning methods, such as decision tree and Naïve Bayes, have been used in training the ensemble learning models. The overall accuracy was 90 and 99%, with 9.61 and 0.21% error rates for Naïve Bayes and decision tree classifier, respectively. The present study can successfully detect network attacks and secure cloud-based platforms. The proposed approach is more stable and more accurate than the earlier research. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.