Abstract:
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
