Intrusion detection is crucial in computer network security issues; therefore, this work is aimed at maximizing network security protection and its improvement by proposing various preventive techniques. Outlier detection and semisupervised clustering algorithms based on shared nearest neighbors are proposed in this work to address intrusion detection by converting it into a problem of mining outliers using the network behavior dataset. The algorithm uses shared nearest neighbors as similarity, judges whether it is an outlier according to the number of nearest neighbors of a data point, and performs semisupervised clustering on the dataset where outliers are deleted. In the process of semisupervised clustering, vast prior knowledge is added, and the dataset is clustered according to the principle of graph segmentation. The novelty of the proposed algorithm lies in outlier detection while effectively avoiding the dependence on parameters, thus eliminating the influence of outliers on clustering. This article uses real datasets: lypmphography and glass for simulation purposes. The simulation results show that the algorithm proposed in this paper can effectively detect outliers and has a good clustering effect. Furthermore, the experimentation reveals that the outlier detection-based SCA-SNN algorithm has the best practical effect on the dataset without outliers, clearly validating the clustering performance of the outlier detection-based SCA-SNN algorithm. Furthermore, compared to the other state-of-the-art anomaly detection method, it was revealed that the anomaly detection technology based on outlier mining does not require a training process. Thus, they overcome the current anomaly detection problems caused due to incomplete normal patterns in training samples.

中文翻译：

---

入侵检测技术在传感器网络计算机网络安全中应用的详尽研究

入侵检测在计算机网络安全问题中至关重要；因此，这项工作旨在通过提出各种预防技术来最大化网络安全保护及其改进。在这项工作中提出了基于共享最近邻居的离群点检测和半监督聚类算法，通过将入侵检测转换为使用网络行为数据集挖掘离群点的问题来解决入侵检测问题。该算法以共享最近邻为相似度，根据数据点的最近邻个数判断是否为离群点，对删除离群点的数据集进行半监督聚类。在半监督聚类过程中，加入了大量的先验知识，按照图分割的原理对数据集进行聚类。该算法的新颖之处在于异常点检测的同时有效避免了对参数的依赖，从而消除了异常点对聚类的影响。本文使用真实数据集：lypmphography 和 glass 用于模拟。仿真结果表明，本文提出的算法能够有效地检测异常值，并具有良好的聚类效果。此外，实验表明，基于异常值检测的SCA-SNN算法对没有异常值的数据集具有最佳的实际效果，从而明确验证了基于异常值检测的SCA-SNN算法的聚类性能。此外，与其他最先进的异常检测方法相比，发现基于异常值挖掘的异常检测技术不需要训练过程。因此，