Integrating Quantum Computing and NLP for Advanced Cyber Threat Detection

Abstract

The exponential growth of digital data and the increasing sophistication of cyber threats demand more advanced methods for threat analysis. This paper explores the integration of quantum computing and natural language processing (NLP) to enhance cyber threat analysis. Traditional computing methods struggle to keep up with the scale and complexity of modern cyber threats, but quantum computing offers a promising avenue for accelerated data processing, while NLP provides sophisticated tools for interpreting and understanding human language, crucial for analysing threat intelligence. Our proposed framework leverages quantum algorithms for rapid anomaly detection and advanced NLP techniques for precise threat identification and analysis. The methodology includes data collection from diverse sources, pre-processing for normalization, quantum-assisted data processing using Grover's search and Quantum Approximate Optimization Algorithm (QAOA), NLP analysis with transformers and BERT-based models, and integration of findings to build comprehensive threat profiles. Experimental results demonstrate significant improvements: quantum algorithms reduced data processing time by up to 50%, NLP models achieved 92% accuracy in threat identification, and the false positive rate was reduced by 30%. These findings indicate a promising direction for next-generation cybersecurity solutions, enabling more proactive and efficient threat mitigation. Future work will focus on refining quantum algorithms, enhancing NLP models, and expanding the framework for real-time threat detection capabilities.