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STAP Journal of Security Risk Management

ISSN: 3080-9444 (Online)

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Article

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Securing Trust: Rule-Based Defense Against On/Off and Collusion Attacks in Cloud Environments

by 

Qais Al-Na’amneh ;

Mahmoud Aljawarneh ;

Ahmad Saleh Alhazaimeh ;

Rahaf Hazaymih ;

Shahid Munir Shah ;

Walid Dhifallah

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Published: 2025/12/01

Abstract

The pervasive adoption of cloud computing, underscored by its distributed, multi-tenant characteristics, introduces intricate vulnerabilities concerning trust assurance. Malicious entities increasingly deploy sophisticated stratagems, such as on/off behavioral subterfuge and orchestrated collusion, to subvert conventional trust assessment mechanisms. This manuscript introduces a Hierarchical Rule-Based Trust Orchestration System (HRTOS), a non-learning, deterministically governed architectural framework designed for the proactive identification and mitigation of these insidious threats within federated cloud environments. HRTOS operates through a synergistic ensemble of modules dedicated to multi-vector behavioral fingerprinting, contextual anomaly evaluation, feedback integrity validation, and collusion pattern grammar analysis. The system’s core philosophy emphasizes operational transparency, imposing minimal computational burden while exhibiting acute sensitivity to nuanced deviations from normative interaction patterns. Rigorous simulations employing diverse synthetic user archetypes—spanning consistent integrity, strategic deception, and coordinated malevolence—demonstrate HRTOS’s pronounced capability to accurately discern legitimate activities from complex reputation manipulation endeavors. Conventional trust paradigms, frequently reliant on computationally intensive machine learning or opaque probabilistic models, often falter when confronted by adaptive adversaries exploiting systemic latencies, sparse data conditions, or the inherent ”black-box” nature of such models. HRTOS circumvents these limitations by employing a layered, context-aware rule engine that processes interaction telemetry and feedback metadata in near real-time. Abrupt behavioral transitions are identified via a multi-faceted deviation index; anomalous feedback is systematically de-weighted through source credibility and content plausibility checks; collusive engagements are surfaced by analyzing reciprocity dynamics and group behavioral coherence. Trust state adjudication is effectuated through deterministic rule sets, fostering auditable enforcement and low-latency response. The presented evaluations, encompassing varied attack vectors including sophisticated on/off attacks and multi-entity collusion schemes, affirm the model’s high fidelity in threat differentiation, its negligible false positive incidence, and its inherent interpretability, rendering HRTOS exceptionally suitable for securing dynamic, federated cloud ecosystems where accountability, efficiency, and proactive threat neutralization are paramount.

Keywords

Security of Cloud ComputingMalicious AttacksRule-Based Defense

Doi Link

https://doi.org/10.63180/jsrm.thestap.2025.1.5

How to Cite the Article

Al-Na’amneh, Q., Aljawarneh, M., Alhazaimeh, A. S., Hazaymih, R., & Shah, S. M. (2025). Securing Trust: Rule-Based Defense Against On/Off and Collusion Attacks in Cloud Environments. STAP Journal of Security Risk Management, 2025(1), 85–114. https://doi.org/10.63180/jsrm.thestap.2025.1.5

Securing Trust: Rule-Based Defense Against On/Off and Collusion Attacks in Cloud Environments is licensed under CC BY 4.0CCBY

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