Intrusion detection maintains network security by detecting intrusion behaviors. There are many clustering algorithms that can be used directly for intrusion detection. K-means is a simple and efficient method used in data clustering. However, k-means has a tendency to converge to local optima and depends on the initial value of cluster centers. Therefore, we present an efficient hybrid clustering algorithm referred to as QALO-K, whereby, we combine k-means with quantum-inspired ant lion optimized. This algorithm combines the advantages of quantum computing and swarm intelligence algorithms to improve the k-means algorithm and make the k-means algorithm converge towards the global optimal direction. Our proposed algorithm is tested on several standard datasets from UCI Machine Learning Repository for cluster analysis and its performance is compared with other well-known algorithms. The proposed method was applied on KDD Cup 99 large datasets for intrusion detection. The simulation results infer that the proposed algorithms can be efficiently used for data clustering and intrusion detection.

入侵检测通过检测入侵行为来维护网络安全。有许多聚类算法可直接用于入侵检测。K-means是一种用于数据聚类的简单有效的方法。但是，k均值趋于收敛到局部最优值，并且取决于聚类中心的初始值。因此，我们提出了一种称为QALO-K的高效混合聚类算法，其中，我们将k-means与优化的量子启发式蚁群结合在一起。该算法结合了量子计算和群体智能算法的优点，改进了k-means算法，使k-means算法向全局最优方向收敛。我们提出的算法在UCI机器学习存储库的几个标准数据集上进行了测试，以进行聚类分析，并将其性能与其他知名算法进行了比较。将该方法应用于KDD Cup 99大型数据集进行入侵检测。仿真结果表明，所提出的算法可以有效地用于数据聚类和入侵检测。