QHNEAD-Quantum Hyperdimensional Neuro Symbolic Evolving Adversarial Defense

Adversarial attacks pose a critical threat to machine learning models operating on tabular data, particularly in security-sensitive applications. This paper introduces QHNEAD, a robust defense framework that synergistically combines quantum-inspired computing, hyperdimensional computing, neuro-symbolic reasoning, and meta-learning. The system features a hybrid detector integrating a Deep Denoising Autoencoder, Graph Convolutional Network, Isolation Forests, and Light GBM to identify adversarial perturbations across FGSM, PGD, and Backdoor attacks. A multi-stage corrector— employing quantum-inspired diffusion (QTPN), hyperdimensional anomaly isolation (HAI), neuro-symbolic feature enforcement (NSFE), and incremental meta-learning (IMLC)—purifies compromised inputs and restores model integrity. Evaluated on the EMBER2018 dataset (400,000 training, 100,000 test samples), QHNEAD significantly outperforms traditional defenses in recovery performance and executes the full defense pipeline in approximately four hours on a 12GB RAM environment. Its modular architecture, noise resilience, and adaptive learning capability establish QHNEAD as a scalable and effective solution for adversarial robustness in cybersecurity-critical tabular systems.