Anomaly detection based on fuzzy rules

被引：0

作者：

Jiao W. ^{[1
]}

Li Q. ^{[2
]}

机构：

[1] School of Computer Science and Technology, Shandong University, Jinan

[2] Shandong University, Jinan

来源：

International Journal of Performability Engineering | 2018年 / 14卷 / 02期

关键词：

Anomaly detection; Intrusion detection; Network security; Web application;

D O I：

10.23940/ijpe.18.02.p19.376385

中图分类号：

学科分类号：

摘要：

Essentially, the fuzzy assert rule library is the fuzzy decision tree. A fuzzy decision tree growth algorithm based on local dynamic optimization is present. Following the idea of the greedy strategy, the algorithm ensures that once a continuous attribute is chosen as a branch node, the membership functions of this attribute after fuzzifying is dynamically optimized. On the other hand, according to fuzzy logic, enhanced Apriori algorithm is present to all the fuzzy frequent item sets composed of fuzzified attributes of multiple events. Then, the fuzzy frequent item sets are transformed into fuzzy association rules that compose the fuzzy association rule library. As for a multiple event sequence, eight different detection algorithms are provided and tested on the same platform. Experiments show that two new algorithms using the fuzzy decision tree and fuzzy association rule library detection models get the highest score. © 2018 Totem Publisher, Inc. All rights reserved.

引用

页码：376 / 385

页数：9

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