An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach With ML Classifier

Aim:

            The aim of this study is to enhance the efficacy of Cloud Intrusion Detection Systems by proposing an optimized design that integrates a hybrid feature selection approach with a machine learning classifier. The goal is to improve the accuracy and efficiency of intrusion detection in cloud environments, addressing the challenges posed by diverse feature types and ensuring robust protection against cyber threats

Abstract:

         Cloud computing has become an integral part of modern IT infrastructure, offering scalability and flexibility. However, the increasing reliance on cloud services also attracts malicious activities and cyber threats. In this context, an effective Intrusion Detection System (IDS) is crucial to safeguard cloud environments. This paper presents an improved design for a Cloud Intrusion Detection System using a hybrid approach for feature selection and a machine learning classifier. The proposed system leverages label encoding, correlation analysis, and the Extra Tree algorithm to enhance the accuracy and efficiency of intrusion detection.

Introduction:

        As organizations increasingly migrate their services and data to cloud environments, the security of cloud infrastructures has become a paramount concern. The dynamic and scalable nature of cloud computing brings about new challenges, particularly in the realm of cyber security. The rise in sophisticated cyber threats and intrusions necessitates the development of advanced security measures, and an integral component of this defense is a robust Cloud Intrusion Detection System (IDS).

In this context, our study presents an improved design for a Cloud Intrusion Detection System, aiming to elevate the effectiveness and accuracy of intrusion detection mechanisms within cloud environments. The proposed system is distinguished by its utilization of a hybrid feature selection approach coupled with a machine learning classifier. This novel combination addresses the inherent complexities of cloud security datasets, where diverse types of features require specialized processing for optimal intrusion detection.

Proposed System:

Firstly, the feature selection process is enhanced by employing label encoding to transform categorical data into numerical format, making it suitable for machine learning algorithms. This ensures that the classifier can effectively process diverse types of features present in cloud security datasets.

Secondly, a correlation analysis is performed to identify and eliminate redundant features. This step aids in reducing dimensionality, enhancing the efficiency of the IDS, and improving the interpretability of results. Correlation analysis helps identify relationships between features, allowing for the retention of only the most informative ones.

Thirdly, the Extra Tree algorithm is applied for feature selection. This algorithm excels in identifying the most important features by constructing an ensemble of decision trees. The Extra Tree algorithm enhances the robustness of the feature selection process, ensuring that the selected features contribute significantly to the detection of intrusions.