Artificial Intelligence-Augmented Intrusion Detection Systems for Advanced Threat Taxonomy in Cloud Computing Environments

Farhan Nisar, Arshad Farhad, Baseer Ali Rehman, Shum Yee Chan, Muhammad Nauman Khan, Muhammad Touseef Irshad
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Farhan Nisar, Arshad Farhad, Baseer Ali Rehman, Shum Yee Chan, Muhammad Nauman Khan, Muhammad Touseef Irshad: DepartmentofPhysicaland Numerical Sciences, Qurtaba University, Peshawar. DepartmentofComputer Science, Bahria University, E-8 Islamabad. DepartmentofApplied Social Sciences, University of Peshawar. DepartmentofAppliedSocialSciences, Hong Kong Polytechnic University. Department of Computer Science, Agriculture University, Peshawar6 Department of Computer Science, National University ofModern Languages, Peshawar

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 4, 2263-2278

Abstract: Over the past few decades, cyber-attacks have emerged as a grave form of criminal activity and a subject of intense scholarly and policy debate. The rapid proliferation of cloud computing services— particularly Software as a Service (SaaS)—has further motivated research to classify security threats and their corresponding countermeasures. Scholars have increasingly focused on the risks, vulnerabilities, and malicious intrusions inherent in such environments, with particular emphasis on MITM (MITM) attacks and their mitigation and detection mechanisms. Host-based virtual software has demonstrated considerable efficacy in detecting malware within localized environments. Building on this foundation, the present study classifies Man-in-the-Middle (MITM) attacks in SaaS platforms through the deployment of Cloud-based Intrusion Detection Systems (CIDS). Our investigation concentrates specifically on attacks that target cloud hosts deployed within SaaS infrastructures. The proposed methodology incorporates the roles of the source cloud, destination cloud, and directional flow of the attack vector. In this context, the cloud ecosystem is understood as a dynamic environment where any participating entity, equipped with sufficient technical expertise, may both launch and be subjected to sophisticated intrusions. Accordingly, adaptive CIDS monitoring architectures are essential to safeguard communication between cloud actors. Moreover, CIDS frameworks furnish modular components capable of aggregating alerts, conducting analysis, and notifying administrators of potential breaches. To further illustrate the threat landscape, we present a statistical analysis of vulnerabilities most frequently exploited in MITM scenarios. This classification not only highlights the evolving tactics of adversaries but also equips readers with a structured understanding of MITM attacks, thereby fostering greater familiarity with contemporary cloud security challenges

Keywords: SaaS; Malware; CIDS; Classification (search for similar items in EconPapers)
Date: 2025
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