Intrusion Detection System in Clouds Using Machine Learning

The widespread usage of cloud computing has brought about a dramatic change in the ways that consumers and companies access and utilize computer resources. It does this by enabling the shared, on-demand use of a collection of online, configurable computing resources. However, the advent of cloud computing poses security problems due to its reliance on shared resources, multi-tenancy structures and third-party service providers. challenge of security in cloud systems is the requirement for robust Intrusion Detection Systems (IDS) to guard against numerous online threats and attacks [1]. Cloud computing is an appealing solution for businesses of all sizes due to its affordability and adaptability [1].”This research investigates the importance of ensemble techniques with improving the prediction accurately [2] of various machine learning models used in IDS within cloud computing environments [3]. We assessed the effectiveness of specific classifiers, including Decision Trees [4],Random Forests (RF), naive Bayes regression, and Logistic Regression (LR), as well as an ensemble technique known as the Voting Classifier, through exhaustive testing and comparative study. According to our research, the ensemble approach generated extremely accurate rates, but its performance was only slightly better than the Random Forest classifier’s and almost exactly the same as the Decision Tree classifier’s. This finding highlights the potential of ensemble techniques to enhance the overall effectiveness in cloud-based intrusion detection systems by combining the predictive capacity of multiple models. However, further study is needed to find the optimal technique to coordinate the individual classifiers and enhance ensemble approaches in order to obtain optimum accuracy in prediction and model resilience in cloud computing systems.