Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. It is an Information Technology (IT) model that provides on-demand hardware and software services to customers. However, cloud computing systems are vulnerable to various cyber-attacks, often due to poor cybersecurity management or misconfigured services. Therefore, these systems must include Intrusion Detection Systems (IDSs) to safeguard each of their Virtual Machines (VMs) against attacks. Noteworthy is the trade- off between the security level of IDSs and system performance. If the IDS delivers greater security service by employing more rules or patterns, it will require more computer resources in proportion to the level of protection, thereby reducing resources allocated to consumers. Additionally, the large volume of logs in cloud computing may be difficult for system administrators to analyze. In this paper, we introduce a Multi-Level Intrusion Detection System with Log Management for Cloud Computing. This system is implemented on a hypervisor virtual machine (VM) and its efficiency is tested by comparing the algorithm with other existing algorithms. We employ a Machine Learning approach to study various patterns of intrusion using the KDD CUP’99 dataset. The proposed architecture is successfully implemented with Artificial Neural Network (ANN) model training and the integration of the Adaptive Fuzzy C-Means (AFCM) clustering algorithm. Key findings include a significant improvement in detecting intrusions while maintaining optimal resource allocation and system performance. This approach provides a robust solution for Cloud Computing systems to achieve both effective resource utilization and strong security services without compromising either