Performance Enhancement of Intrusion Detection System in Cloud by Using Boruta Algorithm

Presently, cloud computing stands as a dependable choice for enterprises seeking contemporary, adaptable IT solutions capable of managing vast volumes of business data. Its adoption holds the promise of enhancing operational efficiency and productivity. However, cloud computing remains a dynamic and evolving technology landscape, fraught with inherent security challenges. Malevolent actors perpetually scour for novel methodologies to compromise the integrity of data hosted within cloud environments. For instance, data theft, achieved through downloading or encrypting sensitive information, and Distributed Denial of Service (DDoS) assaults targeting cloud infrastructures, pose persistent threats. To address these pressing concerns, the solution outlined in this article advocates for intrusion detection within cloud environments employing a plethora of classification algorithms. To ensure the precision of outcomes, the proposed approach incorporates the meticulous selection of pertinent attributes from the dataset, leveraging the Boruta algorithm. Our research has demonstrated that combining Boruta with classifiers yields impressive results, achieving a recall of 100% with KNN on the CICIDS 2017 dataset and a precision of 100% with Naive Bayes on the CICDDOS 2019 dataset. These results underscore the significant role of feature selection in enhancing detection performance, affirming its importance for achieving optimal results in intrusion detection systems.