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All Journal The Eastasouth Journal of Information System and Computer Science
S. Satish Kumar
Associate Professor, AMET University, Chennai, India
Computer Science & IT

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Intelligent Cybersecurity Frameworks for Data Protection in Cloud-Integrated Management Information Systems Kesavan Sundara Mudaliyar; S. Satish Kumar
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i02.1165

Abstract

Enterprise workloads keep moving to public, private, and hybrid cloud environments, and that shift is widening the attack surface available to would-be intruders just as organizations lean harder on Management Information Systems (MIS) to run day-to-day decisions. This paper works through fifteen recent studies sitting at the crossing point of artificial intelligence (AI), cybersecurity, cloud computing, and MIS governance, and tries to say something coherent about what they add up to. Rather than proposing and testing one new tool, the review pulls out the themes that keep resurfacing, AI and machine-learning-based threat detection, cyber threat intelligence, big-data analytics, data governance, federated and privacy-preserving learning, sustainable data-center design, and the human side of security that technical papers tend to skip and organizes them into a five-layer conceptual framework meant to help later empirical work. The review follows an explicit search, screening, and synthesis process, described in Section II; each proposed framework layer is traced back, in the discussion itself, to the specific literature that motivates it, and a thematic distribution chart shows how attention is split across sub-topics in the corpus. What comes out of this is that detection capability and MIS governance are comparatively well covered, while a handful of cross-cutting issues, explainability at the implementation level, how employees behave once AI is in the loop, energy-aware security operations, and the practical limits of federated learning, are thinner than their real-world importance would suggest. The paper closes by naming its own limitations and laying out where empirical work still needs to happen.
Deadline Performance and Tenant Fairness Under Security Screening in Cloud Workload Scheduling Kesavan Sundara Mudaliyar; S. Satish Kumar
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i01.1250

Abstract

Cloud applications share execution capacity while imposing different deadlines and security screening requirements. A scheduler can therefore face congestion even when aggregate capacity appears sufficient, particularly when a large tenant reaches its concurrency limit. This study investigates interaction through a reproducible discrete event simulation of screened cloud jobs. Four nonpreemptive policies are compared: first in first out, shortest job first, earliest deadline first, and a deadline policy with a bounded penalty for tenants that have already consumed more virtual service. The primary experiment contains 240 runs covering three arrival rates, 20 random seeds, and 1,200 jobs per run. An additional 80 runs remove the restrictive tenant concurrency limit at the highest arrival rate. With eight workers and a four-worker tenant limit, shortest job first has a mean deadline miss rate of 5.26% at 1.5 jobs per simulated second, compared with 44.57% for first in first out and 23.97% for earliest deadline first. The service balance candidate reaches 28.91%, showing that the added fairness penalty does not improve deadline performance in this workload. Increasing the tenant limit from four to eight reduces the earliest deadline miss rate to 1.41%. These findings identify tenant capacity fragmentation and job length variation as stronger influences than the tested fairness adjustment. The results are generated from synthetic workloads and do not measure production cloud performance or resistance to cyberattacks. The contribution is an executable comparative study of scheduling under a mandatory modeled screening stage, with explicit workload assumptions and a tenant capacity sensitivity analysis.
Co-Authors Kesavan Sundara Mudaliyar