CLOUD-BASED DATA INTRUSION DETECTION SYSTEM USING ML TECHNIQUES

Abstract

Cloud computing (CC), is a new technology that simplifies access to network and computer resources, providing services like storage and data management, aiming to enhance systems. Despite advantages, cloud providers face security limits, especially concerning resources and services. To improve security, solutions involve monitoring resources, services, and networks to detect attacks. An enhanced mechanism, an intrusion detection system (IDS), controls network traffic and identifies abnormal activities. This paper introduces a cloud- based intrusion detection model utilizing random forest (RF) and feature engineering. Specifically, the RF classifier is integrated to boost the accuracy (ACC) of the detection model. The proposed model is evaluated on the NSL-KDD dataset, showcasing 99.16% ACC, respectively, indicating good performance compared to existing works. Keywords: Cloud security, anomaly detection, random forest.