Abstract

Organizations have witnessed a significant transformation in the realm of data storage and processing owing to the advent of cloud computing. Nonetheless, with its advantages, cloud computing has also brought forth new security concerns that need to be addressed. In this regard, the use of Intrusion Detection Systems (IDSs) has become indispensable for identifying and preventing a wide range of attacks that may occur in cloud computing environments, thereby ensuring data security. In modern years, machine learning (ML) algorithms have emerged as a promising approach for IDSs, as they can analyze huge amounts of data and identify patterns that may not be detectable by traditional rule-based IDSs. This review paper presents a comprehensive analysis of ML-based IDSs and Tradition-based IDSs for intrusion detection in cloud computing environments. The literature review covers various Traditional and ML algorithms used for intrusion detection, including decision trees, Neural Networks (NN), Support Vector machines (SVM), random forests, and k-nearest neighbours. The performance evaluation metrics used in this review paper include accuracy and false positive rate. These metrics are generally used to evaluate the performance of IDS and ML algorithms. The results of the analysis indicate that ML-based IDSs outperform traditional IDSs in terms of accuracy and false positive rate. However, ML-based IDSs may also have limitations, such as a high rate of false negatives and the usefulness of huge amounts of training data. Overall, the analysis suggests that ML-based IDSs have the potential to improve the usefulness of intrusion detection in cloud computing environments, but further research is needed to address the limitations of these systems