Detecting hostile accesses through incremental subspace clustering
Detecting hostile accesses through incremental subspace clustering
复制标题
通过增量子空间聚类检测恶意访问
DOI：
10.1109/wi.2003.1241213
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Einoshin Suzuki
中科院分区：
文献类型：
--
作者：
Masaki Narahashi;Einoshin Suzuki
提出了一种增量子空间聚类方法，用于灵活检测对网站的恶意访问。典型的Web访问日志数据庞大，包含无关信息，并且呈现出动态特征。我们通过数据压缩、子空间聚类和增量算法克服了这些困难。通过修改其数据挤压功能，我们改进了子空间聚类方法SUBCCOM，以便它可以利用之前的结果。实验评估证实了我们的I-SUBCCOM在准确率、召回率和计算时间方面的优势。
We propose an incremental subspace clustering method for flexibly detecting hostile accesses to a Web site. Typical log data for Web accesses are huge, contain irrelevant information, and exhibit dynamic characteristics. We overcome these difficulties through data squashing, subspace clustering, and an incremental algorithm. We have improved, by modifying its data squashing functionality, our subspace clustering method SUBCCOM so that it can exploit previous results. Experimental evaluation confirms superiority of our I-SUBCCOM in terms of precision, recall, and computation time.