INTELLIGENT MODELS FOR INTRUSION DETECTION OVER CLOUD INFRASTRUCTURE: A LITERATURE REVIEW

Abstract

Cloud Computing has revolutionized the information technology (IT) landscape, enabling scalable and on-demand access to resources. However, its reliance on shared infrastructure introduces vulnerabilities, necessitating advanced security measures. Traditional intrusion detection systems (IDSs) struggle to cope with the complexity and scale of cloud environments. Machine Learning (ML) has emerged as a promising approach, offering automation, adaptability, and enhanced detection capabilities, thus, ensuring intelligence in intrusion detection systems. With the increasing reliance on cloud infrastructure for critical applications, ensuring robust security measures has become paramount. This paper reviews existing works that employ Machine Learning (ML) techniques for intrusion detection in cloud environments. By analyzing the strengths and weaknesses of these models, we identify gaps in current approaches and propose potential research directions. Furthermore, we recommend advanced ML techniques to enhance the security and reliability of cloud-based systems. The existing literature revealed that the transition from conventional methods to advanced learning approaches signals a critical shift in the landscape of cloud-based security, although the literature disclosed that further research is necessary to refine these models and enhance their effectiveness across diverse attack vectors.