Privacy-Aware Framework of Robust Malware Detection in Indoor Robots: Hybrid Quantum Computing and Deep Neural Networks Article Swipe
Le Thanh Tan
,
Van Hoa Le
,
Sachin Shetty
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2510.13136
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2510.13136
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.
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Metadata
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- preprint
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- http://arxiv.org/abs/2510.13136
- https://arxiv.org/pdf/2510.13136
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All OpenAlex metadata
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https://doi.org/10.48550/arxiv.2510.13136Digital Object Identifier
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Privacy-Aware Framework of Robust Malware Detection in Indoor Robots: Hybrid Quantum Computing and Deep Neural NetworksWork title
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preprintOpenAlex work type
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2025Year of publication
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2025-10-15Full publication date if available
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Le Thanh Tan, Van Hoa Le, Sachin ShettyList of authors in order
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https://arxiv.org/abs/2510.13136Publisher landing page
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https://arxiv.org/pdf/2510.13136Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2510.13136Direct OA link when available
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0Total citation count in OpenAlex
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