Indoor robotic systems within Cyber-Physical Systems (CPS) are increasingly exposed to Denial of Service (DoS) attacks that compromise localization, control and telemetry integrity. We propose a privacy-aware malware detection framework for indoor robotic systems, which leverages hybrid quantum computing and deep neural networks to counter DoS threats in CPS, while preserving privacy information. By integrating quantum-enhanced feature encoding with dropout-optimized deep learning, our architecture achieves up to 95.2% detection accuracy under privacy-constrained conditions. The system operates without handcrafted thresholds or persistent beacon data, enabling scalable deployment in adversarial environments. Benchmarking reveals robust generalization, interpretability and resilience against training instability through modular circuit design. This work advances trustworthy AI for secure, autonomous CPS operations.

翻译：信息物理系统（CPS）中的室内机器人系统日益面临拒绝服务（DoS）攻击的威胁，此类攻击会损害定位、控制与遥测数据的完整性。本文提出一种面向室内机器人系统的隐私感知恶意软件检测框架，该框架融合混合量子计算与深度神经网络技术，在保护隐私信息的同时应对CPS中的DoS威胁。通过将量子增强特征编码与经Dropout优化的深度学习相结合，该架构在隐私受限条件下实现了高达95.2%的检测准确率。系统无需人工设定阈值或依赖持续信标数据即可运行，从而支持在对抗性环境中进行可扩展部署。基准测试表明，通过模块化电路设计，系统展现出强大的泛化能力、可解释性以及对训练不稳定性的鲁棒性。本研究推动了面向安全、自主CPS操作的可信人工智能发展。