Quantum Machine Learning in Cloud-Based Security Services and Applications Article Swipe

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C.P. Ravikumar , K Raghavendar , Isha Batra , Ghadah Naif Alwakid , Khulud Salem Alshudukhi ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.4018/979-8-3693-6925-8.ch010

The integration of Quantum Machine Learning (QML) into cloud-based security services represents a transformative shift in cybersecurity. By leveraging quantum computing's ability to process data exponentially faster than classical systems, QML enhances cloud security with real-time threat detection, anomaly recognition, and intrusion prevention. It introduces quantum-inspired algorithms, quantum versions of classical algorithms, and hybrid quantum-classical models, each offering unique advantages in processing speed and scalability. However, the rise of quantum computing also threatens conventional encryption methods like RSA and ECC, necessitating the development of quantum-resistant encryption techniques. Despite challenges posed by current quantum devices, QML's potential to revolutionize cloud security is immense, providing faster, more efficient, and robust protection against evolving cyber threats.

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Computer Science

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Cloud computing Computer science Operating system

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Landing Page: https://doi.org/10.4018/979-8-3693-6925-8.ch010
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OpenAlex ID: https://openalex.org/W4412958982

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DOI: https://doi.org/10.4018/979-8-3693-6925-8.ch010

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Title: Quantum Machine Learning in Cloud-Based Security Services and Applications

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Type: book-chapter

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Language: en

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Publication year: 2025

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Publication date: 2025-07-09

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Authors: C.P. Ravikumar, K Raghavendar, Isha Batra, Ghadah Naif Alwakid, Khulud Salem Alshudukhi

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Landing page: https://doi.org/10.4018/979-8-3693-6925-8.ch010

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Open access: Yes

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OA status: gold

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OA URL: https://doi.org/10.4018/979-8-3693-6925-8.ch010

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Concepts: Cloud computing, Computer science, Operating system

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Cited by: 0

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