Reaching a Consensus on Access Detection by a Decision System

被引：0
|
作者：
Guevara, Cesar [1 ]
Santos, Matilde [1 ]
Antonio Martin, Jose [1 ]
Lopez, Victoria [1 ]
机构：
[1] Univ Complutense Madrid, Dept Comp Architecture & Automat Control, Madrid, Spain
来源：
PROCEEDINGS OF 2014 IEEE INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATICS AND COMPUTING (PIC) | 2014年
关键词：
artificial intelligence; heuristic methodologies; intrusion detection (IDS); decision trees; supervised classifying system UCS; naive Bayes; INTRUSION DETECTION; CLASSIFIER;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Classification techniques based on Artificial Intelligence are computational tools that have been applied to detection of intrusions (IDS) with encouraging results. They are able to solve problems related to information security in an efficient way. The intrusion detection implies the use of huge amount of information. For this reason heuristic methodologies have been proposed. In this paper, decision trees, Naive Bayes, and supervised classifying systems UCS, are combined to improve the performance of a classifier. In order to validate the system, a scenario based on real data of the NSL-KDD99 dataset is used.
引用
收藏
页码：119 / 122
页数：4
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