Privacy-Aware Framework of Robust Malware Detection in Indoor Robots: Hybrid Quantum Computing and Deep Neural Networks

2025-10-15

AI Summary

To address denial-of-service (DoS) attacks threatening indoor robots in cyber-physical systems (CPS), this paper proposes a privacy-preserving, quantum-enhanced malware detection framework. Methodologically, it integrates quantum feature encoding with interpretable, modular quantum circuits, coupled with deep neural networks and Dropout regularization—eliminating the need for manually defined thresholds or continuous beacon data. The key contributions are: (i) the first application of quantum-enhanced representation learning to robot DoS detection, enabling end-to-end inference under strict privacy constraints; and (ii) a quantum-classical hybrid architecture that significantly improves model generalizability, robustness, and resilience against training instability. Experimental evaluation demonstrates a detection accuracy of 95.2%, confirming the framework’s effectiveness and scalability for deployment in adversarial environments.

Technology Category

• Machine Learning: Quantum Machine Learning
• Intelligent Robots: State Estimation
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Detecting DoS malware in indoor robots while preserving privacy information
• Countering DoS attacks on robotic localization and control systems
• Developing quantum-enhanced deep learning for CPS security threats

Innovation

• Hybrid quantum computing with deep neural networks
• Quantum-enhanced feature encoding with dropout optimization
• Modular circuit design for robust generalization