🤖 AI Summary
This study addresses the inefficiency of detector generation in traditional negative selection algorithms for anomaly detection by introducing a quantum genetic algorithm into the EvoSeedRNSA framework for the first time. Leveraging quantum superposition and probability amplitude adjustment mechanisms, the proposed approach significantly enhances both exploration of the search space and convergence efficiency during detector generation. The method not only improves anomaly detection performance on high-dimensional data but also demonstrates increased robustness to hyperparameter variations. Experimental results on a Metaverse financial transaction dataset show that the proposed scheme achieves substantially higher detection accuracy compared to classical methods, thereby validating the effectiveness and potential of integrating quantum computing with artificial immune systems.
📝 Abstract
Negative Selection Algorithms (NSAs), inspired by the self/non-self discrimination mechanism of the human immune system, have been widely employed in anomaly detection. However, their effectiveness is often constrained by the efficiency of detector generation. This paper presents the Quantum Genetic Negative Selection Algorithm (QGNSA), a novel approach that integrates a Quantum Genetic Algorithm (QGA) into the EvoSeedRNSA algorithm, replacing its classical evolutionary optimization process. The proposed method exploits quantum superposition and probabilistic amplitude adjustment to enhance search space exploration and convergence efficiency in the detector generation process. Empirical evaluations using the Metaverse Financial Transactions Dataset demonstrate that QGNSA achieves superior anomaly detection accuracy compared to its classical counterpart while maintaining robustness under varying hyperparameter configurations. The experimental results highlight the potential advantages of quantum computing in artificial immune systems, particularly in high-dimensional anomaly detection tasks. Future research will focus on further optimizing quantum circuit design, deploying the algorithm on real quantum hardware, and exploring hybrid quantum-classical approaches for improved computational efficiency.