NONE 20260624104147389 wseas/wseas content-registration-form@crossref.org content-registration-form PROOF 2944-9162 2732-9941 Anas Dahabiah Al-Sham Private University Damascus, SYRIA This paper presents the Quantum-inspired Fuzzy Destructive Interference (QFDI) model, a novel hybrid intrusion detection system that treats network flows as wave phenomena rather than static feature vectors. Normal traffic is encoded as a multi-dimensional reference wave; any test flow is subjected to destructive interference with this reference. Perfect cancellation indicates benign traffic, while residual energy—graded by a lightweight Fuzzy Inference System (FIS) comprising eight data-driven If-Then rules—indicates an attack. Experiments on the UNSW-NB15 benchmark dataset demonstrate that QFDI achieves a False Positive Rate (FPR) of 0.079, representing a 77.5% reduction compared to Random Forest (0.350) and a 76.5% reduction compared to XGBoost (0.336), while attaining a Precision of 0.903. QFDI deliberately trades higher recall (0.600) for significantly lower false positives, making it ideal as a high-precision second-stage filter in layered IDS architectures. The complete model requires only 39 numerical parameters and 0.30 KB of memory, enabling deployment on embedded and IoT devices where state-of-the-art deep learning models are computationally infeasible. QFDI is fully interpretable through eight linguistic rules, addresses alert fatigue in security operations centres, and requires no offline training phase. 06 24 2026 06 24 2026 29 4 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232020.2026.6.4 https://wseas.com/journals/proof/2026/a08proof-004(2026).pdf Y. Imrana, Y. Xiang, L. Ali, and Z. AbdulRauf, A bidirectional LSTM deep learning approach for intrusion detection, Expert Syst. Appl., Vol. 185, 115524, 2021. I. Ullah and Q. H. Mahmoud, A two-level flow-based anomalous activity detection system for IoT networks, Electronics, Vol. 9, No. 3, p. 530, 2021. W. Lu and I. Traore, Detecting new forms of network intrusion using genetic programming, Comput. Intell., Vol. 20, No. 3, pp. 475–494, 2004. J. E. Dickerson and J. A. Dickerson, Fuzzy network profiling for intrusion detection, Proc. NAFIPS, pp. 301–306, 2000. N. Moustafa and J. Slay, UNSW-NB15: A comprehensive data set for network intrusion detection systems, Proc. MilCIS, pp. 1–6, 2015. D. Useche-Pelaez et al., A quantum-inspired classifier for intrusion detection in communication networks, IETE Tech. Rev., 2022. L. Gong et al., Quantum-inspired neural network for intrusion detection, IEEE Trans. Inf. Forensics Security, Vol. 18, pp. 3314– 3327, 2023. S. Ganapathy et al., Intelligent feature selection and classification techniques for intrusion detection in networks, EURASIP J. Wireless Commun. Netw., Vol. 2013, No. 1, p. 271, 2014. N. Moustafa and J. Slay, The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 dataset, J. Inf. Security Appl., Vol. 31, pp. 18– 31, 2016. K. Julisch, Clustering intrusion detection alarms to support root cause analysis, ACM Trans. Inf. Syst. Security, Vol. 6, No. 4, pp. 443–471, 2003. R. Sommer and V. Paxson, Outside the closed world: On using machine learning for network intrusion detection, Proc. IEEE S&P, pp. 305–316, 2010.