A Novel Framework for Secure Cloud Computing Based IDS Using Machine Learning Techniques

Cloud computing has been promoted as one of the most effective methods of hosting and delivering services via the internet. But cloud security remains a serious concern for cloud computing. Many secure solutions have been developed to safeguard communication in such environments, the majority of which are based on attack signatures. These systems are often ineffective in detecting all forms of threats. To address this gap machine learning approaches are being explored. In this research, we present a novel firewall mechanism for safe cloud computing environments called machine learning system. Proposed Method identifies and classifies incoming traffic packets using a novel combination methodology named most frequent decision, in which the nodes' one previous decisions are coupled with the machine learning algorithm's current decision to estimate the final attack category classification. This method improves learning performance as well as system correctness. UNSW-NB-15, a publicly accessible dataset, is utilized to derive our findings. Our data demonstrate that it enhances anomaly detection to 97.68 percent.