摘要： 群体智能在解决非确定性多项式(NP)问题或搜索空间过大的问题时有着显著优势。将鸽群优化(Pigeon Inspired Optimization，PIO)算法应用于入侵检测系统的特征选择中。提出基于Sigmoid的PIO(SPIO)和基于Cosine余弦相似度的PIO(CPIO)算法对入侵检测数据集KDDCUP99进行特征选择，并用机器学习的方法进行实验，建立模型并评估结果。

中图分类号： TN97
文献标识码： A
DOI：10.16157/j.issn.0258-7998.200420
中文引用格式： 王康，霍朝宾，李青旭. 一种基于鸽群优化算法的入侵检测技术[J].电子技术应用，2021，47(2)：11-15.
英文引用格式： Wang Kang，Huo Chaobin，Li Qingxu. An intrusion detection technique based on pigeon inspired optimization algorithm[J]. Application of Electronic Technique，2021，47(2)：11-15.

An intrusion detection technique based on pigeon inspired optimization algorithm

Abstract： Swarm intelligence has significant advantages in solving nondeterministic polynomial(NP) problems or problems with too much search space. In this paper, pigeon inspired optimization(PIO) is applied to the feature selection of intrusion detection systems. The Sigmoid-based PIO(SPIO) and Cosine-based PIO(CPIO) algorithms were proposed to select the features of the intrusion detection data set KDDCUP99 and conduct experiments with the method of machine learning to build the model and evaluate the results.

Key words : PIO；KDDCUP99；machine learning