Intrusion Detection Systems Using Quantum-Inspired Density Matrix Encodings

We investigate the application of quantum-inspired density matrix encodings to intrusion detection systems (IDS) in both supervised and unsupervised machine learning contexts. By leveraging the density matrix formulation from quantum mechanics, we introduce a novel perspective on generating aggregate flow data for network traffic classification. The proposed encoding method is evaluated on a benchmark dataset, demonstrating its feasibility and effectiveness. Experimental results showcase its strong predictive capabilities, achieving over 99.89% accuracy, precision, recall, and F1 scores, highlighting the competitive performance of our approach when compared with common encoding methods. Our findings promote further exploration of density matrices in the domain of intrusion detection systems, offering a promising avenue for improving the efficiency and accuracy of network traffic classification tasks.