For the unsupervised learning based clustering algorithm, the intrusion detection rate is low, and the training sample based on supervised learning clustering algorithm is insufficient. A semi-supervised kernel fuzzy C-means clustering algorithm based on artificial fish swarm optimization (AFSA-KFCM) is proposed. Firstly, the kernel function is used to change the distance function in the traditional semi-supervised fuzzy C-means clustering algorithm to define a new objective function, thus improving the probabilistic constraints of the fuzzy C-means algorithm. Then, the artificial fish swarm algorithm with strong global optimization ability is used to improve the KFCM sensitivity to the initial cluster center and easy to fall into the local extremum, thus improving the convergence speed and improving the classification effect. The test results in the Wine and IRIS public datasets show that the AFSA-KFCM clustering algorithm is superior to the traditional algorithm in clustering accuracy and time efficiency. At the same time, the experimental results in KDDCUP99 experimental data show that the algorithm can obtain the ideal detection rate and false detection rate in intrusion detection.

中文翻译：

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人工鱼群优化半监督核模糊聚类算法在网络入侵中的应用

对于无监督学习聚类算法，入侵检测率低，基于监督学习聚类算法的训练样本不足。提出了一种基于人工鱼群优化的半监督核模糊C-均值聚类算法（AFSA-KFCM）。首先，利用核函数改变传统的半监督模糊C-均值聚类算法中的距离函数，定义了新的目标函数，从而改善了模糊C-均值算法的概率约束。然后，采用具有较强全局优化能力的人工鱼群算法，提高了KFCM对初始聚类中心的敏感性，不易陷入局部极值，提高了收敛速度，提高了分类效果。在Wine和IRIS公开数据集中的测试结果表明，AFSA-KFCM聚类算法在聚类精度和时间效率方面优于传统算法。同时，在KDDCUP99实验数据中的实验结果表明，该算法在入侵检测中可以获得理想的检测率和错误检测率。