Fusing nature inspired fuzzy neural networks for hypervisor intrusion detection

In the realm of cloud computing, hypervisors play a pivotal role as virtualization layers, managing and orchestrating virtual machines on physical host systems. The data generated within hypervisor environments holds the key to the operational integrity of cloud infrastructures. However, this critical data is increasingly susceptible to security threats, with intrusion posing a substantial risk. Intrusion within hypervisor networks can lead to severe consequences, ranging from compromised data integrity and confidentiality to potential disruptions in service availability. Traditional security measures often fall short in addressing the dynamic and sophisticated nature of these intrusions, necessitating advanced methodologies for robust detection and response. This paper addresses the intricate challenge of hypervisor intrusion by introducing Nature Inspired Fuzzy Neural Networks (NIFNN) as an innovative model. By synergizing the strengths of fuzzy logic and neural networks, the proposed NIFNN model enhances the precision and efficiency of hypervisor intrusion detection. The process begins with pre-processing of hypervisor data to ensure quality and reliability. The NIFNN classifier then analyses and classifies this pre-processed data to detect and identify malicious activities within hypervisor networks. Notably, we introduce the Nature Inspired Lyrebird Optimization algorithm (NILOA) to optimize the Fuzzy Neural Network model, further elevating the effectiveness and adaptability of our intrusion detection system. The effectiveness of the NIFNN model is evaluated by comparing it with various existing techniques based on metrics such as accuracy, recall, precision, F-measure, specificity, false positive rate (FPR), and false negative rate (FNR). The experimental findings demonstrate that the NIFNN model outperforms the existing approaches across these metrics.