AN ENHANCED APPROACH FOR INTRUSION DETECTION IN VIRTUAL NETWORK OF CLOUD COMPUTING

Virtual networks are commonly employed in cloud computing, necessitating the incorporation of essential security protocols such as intrusion detection. The dynamic and distributed design of virtual networks in cloud computing, coupled with the volume and variability of traffic and the requirement for real-time monitoring, present several challenges to existing intrusion detection methods. The aforementioned issues can be categorised into three distinct groups. The present study provides a comprehensive examination of the existing methods employed for detecting intrusions in virtual networks of cloud computing. Additionally, it suggests a contemporary approach that utilises machine learning methodologies to enhance the accuracy and effectiveness of intrusion detection. The present document encompasses both the review and the proposal. The methodology outlined comprises various stages, including but not limited to data collection, preprocessing, feature extraction, model selection and training, assessment, deployment, and continuous monitoring and updating. The methodology is capable of overcoming the obstacle posed by the absence of openly available datasets for the purpose of assessing and validating intrusion detection systems in cloud-based scenarios. As per the assessment results, the suggested methodology attains a greater degree of precision compared to the presently employed techniques, specifically at 95%. A novel technique has been proposed to enhance the efficacy and efficiency of identifying and mitigating intrusion risks in virtual networks utilised for cloud computing.