Open Access
ARTICLE
Resilient Security Framework for Lottery and Betting Kiosks under Ransomware Attacks
Independent Researcher, Mount Holly, NJ 08060, USA
* Corresponding Author: Sapan Pandya. Email:
Journal of Cyber Security 2025, 7, 637-651. https://doi.org/10.32604/jcs.2025.073670
Received 23 September 2025; Accepted 26 November 2025; Issue published 24 December 2025
Abstract
Ransomware has evolved from opportunistic malware into a global economic weapon, crippling critical services and extracting billions in illicit revenue. While most research has centered on enterprise networks and healthcare systems, an equally vulnerable frontier is emerging in lottery and betting kiosks—self-service financial Internet of Things (IoT) devices that handle billions of dollars annually. These terminals operate unattended, rely on legacy operating systems, and interact with sensitive transactional data, making them prime ransomware targets. This paper introduces a Resilient Security Framework (RSF) for kiosks under ransomware threat conditions. RSF integrates three defensive layers: (1) prevention through application allow-listing, secure boot, and Zero Trust (ZT) segmentation, (2) detection via artificial intelligence (AI) driven anomaly monitoring of system and transaction telemetry, and (3) response employing secure rollback, blockchain-backed forensic logging, and remote wipe capabilities. A synthetic testbed emulating 500 kiosks over a 72-h continuous simulation under ransomware campaigns representing WannaCry, Ryuk, and Conti variants demonstrates the RSF’s effectiveness. Compared with a baseline antivirus-only configuration, the RSF reduced mean time to detection (MTTD) by 41% (from 52 to 31 min), mean time to recovery (MTTR) by 53% (from 120 to 56 min), and downtime-related operational losses by 37% over the three-day experiment window. These findings validate the RSF’s ability to enhance resilience and recovery speed in large kiosk deployments while maintaining compliance with regulatory uptime requirements.Keywords
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Copyright © 2025 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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