Quantum Genetic Optimization for Negative Selection Algorithms in Anomaly Detection

TL;DR

This paper introduces QGNSA, a quantum-enhanced algorithm for anomaly detection that improves detector generation efficiency and accuracy over classical methods, demonstrating the potential of quantum computing in immune-inspired systems.

Contribution

It presents a novel Quantum Genetic Negative Selection Algorithm integrating quantum principles into anomaly detection, enhancing search and convergence capabilities.

Findings

QGNSA outperforms classical NSA in detection accuracy

Quantum features improve search space exploration

Algorithm maintains robustness across hyperparameters

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.