Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques

Abstract

Cloud computing (CC) is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services. In addition, it aims to strengthen systems and make them useful. Regardless of these advantages, cloud providers suffer from many security limits. Particularly, the security of resources and services represents a real challenge for cloud technologies. For this reason, a set of solutions have been implemented to improve cloud security by monitoring resources, services, and networks, then detect attacks. Actually, intrusion detection system (IDS) is an enhanced mechanism used to control traffic within networks and detect abnormal activities. This paper presents a cloud-based intrusion detection model based on random forest (RF) and feature engineering. Specifically, the RF classifier is obtained and integrated to enhance accuracy (ACC) of the proposed detection model. The proposed model approach has been evaluated and validated on two datasets and gives 98.3% ACC and 99.99% ACC using Bot-IoT and NSL-KDD datasets, respectively. Consequently, the obtained results present good performances in terms of ACC, precision, and recall when compared to the recent related works.