arXiv:2605.21915v1 Announce Type: cross Abstract: Congestion controllers (CCs) are critical to network performance, and yet their robustness under adverse conditions remains insufficiently understood. While recent learning-based CCs have demonstrated strong performance in controlled environments, it is unclear how they compare to traditional CCs when controllers' input signals are corrupted or when environmental conditions become systematically challenging. In this paper, we introduce CCLab, an adversarial testing framework for systematically evaluating the robustness of both learning-based an
The proliferation of learning-based congestion controllers necessitates robust testing amidst growing complexity and reliance on AI in critical infrastructure.
This development ensures the reliability and security of network performance, particularly as AI integrates into foundational systems, affecting data flow and operational stability.
The introduction of a systematic adversarial testing framework for congestion controllers elevates the standard for evaluating network robustness and the security of AI-driven systems.
- · Cybersecurity firms
- · Network infrastructure providers
- · Organizations relying on robust digital communications
- · Developers of unrobust AI/ML network solutions
- · Organizations with vulnerable network architectures
Improved network resilience and reduced incidents of service disruption due to vulnerable congestion controllers.
Increased demand for advanced AI security testing tools and expertise across various critical infrastructure sectors.
Potential for an arms race between adversarial AI testing and AI-driven defense mechanisms in network operations.
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