Akter, Shakila and Uddin, Md Riyad and Mostafa, Md. Golam and Chowdhury, Sajidul Haque (2025) ADAPTIVE AI-DRIVEN SERVICE-PRESERVING CYBER CONTAINMENT FOR RESILIENT CRITICAL INFRASTRUCTURE. Journal of Artificial Intelligence and Digital Economy, 2 (2). ISSN 3032-1077

Text JAIDE_1917_Shakila+Akter_Adaptive+AI-Driven+Service-Preserving.pdf Download (1MB)

Abstract

Objective: Cyber containment in critical infrastructure is a dual-risk decision: insufficient isolation permits lateral movement, while excessive isolation can interrupt electricity, water, healthcare, transportation, and communications. This study introduces Adaptive Service-Preserving Containment (ASPC), which estimates compromise uncertainty, forecasts attack spread, models critical-service dependencies, predicts operational consequences, and repeatedly selects the smallest boundary satisfying safety and residual-risk constraints. Method: Unlike a purely conceptual treatment, ASPC was implemented in a stochastic graph-based test environment and evaluated against full isolation, rule-based containment, and a cyber-centric AI baseline. Results: Across 450 paired trials on seen topology families and 300 on held-out topologies, ASPC achieved attack spread statistically indistinguishable from full isolation while sharply reducing unnecessary isolation and safety violations. On unseen topologies, ASPC averaged 1.49 additional compromised nodes, 3.91% unnecessary isolation, 1.27 containment epochs, 5.76 recovery epochs, 0.51 safety violations, and 92.30% critical-service availability. Its availability exceeded the other methods by 14.49–74.75 percentage points. Ablations showed that continuous reassessment was necessary to contain delayed footholds and that the service-dependency model prevented coarse over-isolation. Novelty: Results support ASPC as a testbed-validated resilience controller, while remaining subject to the limitations of synthetic topology, simplified physical dynamics, and simulated telemetry.

Item Type:	Article
Uncontrolled Keywords:	Adaptive cyber defense, Critical infrastructure, Cyber-physical systems, Containment, Service continuity, Attack graphs, Uncertainty, Safe reinforcement learning, Resilience, Graph neural networks
Subjects:	H Social Sciences
Depositing User:	admin eprints
Date Deposited:	13 Sep 2026 10:34
Last Modified:	13 Sep 2026 10:34
URI:	http://eprints.umsida.ac.id/id/eprint/17080

Actions (login required)

View Item