Federated Cybersecurity Testbed as a Service (FCTaaS): A framework to federate cybersecurity testbeds

📅 2026-07-10
📈 Citations: 0
Influential: 0
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🤖 AI Summary
Existing cybersecurity testbeds struggle to support geographically distributed collaborative research due to rapid technological evolution, low resource utilization, and restricted accessibility. To address these limitations, this work proposes a Federated Cybersecurity Testbed-as-a-Service (FCTaaS) framework, which enables, for the first time, federated collaboration across heterogeneous testbeds spanning multiple administrative and geographic domains. The framework integrates diverse testbed resources through virtual private networks and incorporates Suricata IDS/IPS to facilitate remote discovery, experiment orchestration, and joint execution. Evaluation through DoS attack and intrusion detection experiments demonstrates that FCTaaS incurs only 1% performance overhead, maintains network utilization below 49%, and achieves inter-node latencies as low as 5.63 ms and cross-regional latencies up to 147 ms, confirming its efficiency and practicality for collaborative cybersecurity research.
📝 Abstract
Rapid technological change is reshaping society through emerging domains such as autonomous vehicles and smart manufacturing, creating new research challenges in system design, operation, security, and training. Researchers often rely on testbeds to reproduce experimental scenarios, collect and analyze data, observe system behavior, and evaluate proposed solutions. However, the fast pace of innovation makes it difficult and costly for individual testbeds to remain representative of state-of-the-art systems, as doing so requires frequent upgrades and new capabilities. Moreover, access to specialized testbeds is often limited to a small group of researchers, leaving valuable infrastructure underutilized during its operational lifetime. This paper presents FCTaaS, a Federated Cybersecurity Testbed as a Service framework that enables heterogeneous cybersecurity testbeds to participate in a single experiment across geographical boundaries. By connecting independently managed testbeds through a Virtual Private Network (VPN), FCTaaS supports remote testbed discovery, experiment design, and coordinated experimentation. We evaluate FCTaaS across three case studies involving denial-of-service scenarios on smart infrastructure and intrusion detection and prevention workflows using a Suricata-based IDS/IPS testbed. The results show that FCTaaS enables effective cross-testbed experimentation while preserving visibility into attack traffic, IDS alerts, and detection-system resource stress. Even under resource-intensive attack scenarios, FCTaaS achieves limiting network utilization of 49%, introduces only 1% overhead, and supports latency ranging from 5.63 ms between local nodes to 147 ms between geographically dispersed nodes.
Problem

Research questions and friction points this paper is trying to address.

cybersecurity testbed
federated infrastructure
resource underutilization
technological obsolescence
geographically distributed experimentation
Innovation

Methods, ideas, or system contributions that make the work stand out.

Federated Testbed
Cybersecurity Experimentation
Virtual Private Network (VPN)
Intrusion Detection System (IDS)
Cross-platform Collaboration
J
Josh Dean
Department of Electrical and Computer Engineering, University of Arizona, Tucson, USA
Yu-Zheng Lin
Yu-Zheng Lin
University of Arizona
Digital-TwinCyber-SecurityMachine LearningParticle Accelerator
J
John Paul Martin Encinas
Department of Electrical and Computer Engineering, University of Arizona, Tucson, USA
I
Ibrahim Almazyad
Department of Electrical and Computer Engineering, University of Arizona, Tucson, USA
Q
Qinxuan Shi
School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, USA
Z
Zhanglong Yang
School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, USA
S
Shalaka Satam
Department of Electrical and Computer Engineering, University of Arizona, Tucson, USA
T
Tingjun Lei
School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, USA
J
Jielun Zhang
School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, USA
Sicong Shao
Sicong Shao
Assistant Professor, University of North Dakota
cybersecuritymachine learningdigital forensicssoftware engineering
Salim Hariri
Salim Hariri
University of Arizona
autonomic computingsecuritycloud computing
Pratik Satam
Pratik Satam
Assistant Professor, University of Arizona
Smart ManufacturingCyber SecurityMachine Learning