Abstract
Securing its networks from cyber-attacks is of utmost importance as the Industrial Internet of Things (IIoT) becomes a lynchpin of contemporary industrial ecosystems. With the increasing complexity and sophistication of cyber threats, traditional machine learning approaches, while successful, frequently struggle with real-time identification due to computational restrictions. In this study, we present an adaptive quantum learning framework for real-time IIoT attack identification that takes advantage of quantum computing’s inherent parallelism and speed. We introduce a novel quantum technique that can quickly and accurately identify both new and existing attack vectors as they emerge in dynamic threat landscapes. The experimental results show improved accuracy rates and a drastic decrease in identification delay compared to classical methods. According to our research, quantum-enhanced learning frameworks have great potential to strengthen IIoT security in the face of more sophisticated cyber-attacks.
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Joshi, P., Goyal, S.B., Rajawat, A.S., Solanki, R.K., Chen, C. (2024). Adaptive Quantum Learning Frameworks for Real-Time IIoT Attack Identification. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 1167. Springer, Cham. https://doi.org/10.1007/978-3-031-73318-5_32
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