FTSS: Hybrid Quantum-Classical Computing for Intrusion Detection

Ensuring the security of information transmission in advanced traffic management systems is crucial for maintaining the integrity and reliability of traffic operations. In this research, we present a novel intrusion detection system that leverages a hybrid quantum-classical approach to enhance network security within advanced traffic management systems. By combining the strengths of quantum computing and classical techniques, our system effectively detects and mitigates intrusions in real time. We address the challenges posed by noisy quantum environments and computational overhead, developing a model that optimizes accuracy while minimizing resource demands. To comprehensively assess the capabilities of our system, we conducted rigorous evaluations using two distinct datasets: KDD99 and CICIDS. This dual-dataset approach enables a thorough evaluation of our model’s performance against both new and old attack types. Our intrusion detection system exhibits outstanding performance on the KDD99 dataset, surpassing an accuracy rate of 98.96% and an impressive 99.40% accuracy on CICIDS. In addition, our system demonstrates superior memory usage efficiency, outperforming all existing solutions in this domain. This achievement underscores our approach’s ability to maintain high accuracy while minimizing computational resource demands. These findings highlight the effectiveness of our approach in fortifying the security of advanced traffic management systems and demonstrate its potential for real-world deployment.
➡️ Original seminar: February 16, 2024
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[@jorgeirai27]

Hi there,
I was wondering if there's any paper about this research that you did. It would be awesome for me to have it because i'm doing my master's research project about the same topic.
Thank you so much.