A Quantum-Inspired Algorithm for Clustering and Intrusion Detection

The Intrusion Detection System (IDS) is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities. Clustering algorithms are often incorporated into IDS; however, conventional clustering-based methods face notable drawbacks, including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions. To overcome the performance limitations of existing methods, this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm (SC-QGA) and an improved quantum artificial bee colony algorithm hybrid K-means (IQABC-K). First, the SC-QGA algorithm is constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities. For the subsequent clustering phase, the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search. Simultaneously, the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy. Through experimental evaluation with multiple algorithms and diverse performance metrics, the proposed algorithm confirms reliable accuracy on three datasets: KDD CUP99, NSL_KDD, and UNSW_NB15, achieving accuracy of 98.57%, 98.81%, and 98.32%, respectively. These results affirm its potential as an effective solution for practical clustering applications.