CONDITIONAL RANDOM FIELDS BASED REAL-TIME INTRUSION DETECTION FRAMEWORK

被引：0

作者：

Gu, Jiaojiao ^{[1
]}

Jiang, Wenzhi ^{[1
]}

Hu, Wenxuan ^{[1
]}

Zhang, Xiaoyu ^{[1
]}

机构：

[1] Naval Aeronaut & Astronaut Univ, Yantai, Peoples R China

来源：

3RD INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND COMPUTER SCIENCE (ITCS 2011), PROCEEDINGS | 2011年

关键词：

intrusion detection; anomaly; CRFs; Machine Learning; layered framework;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Intrusion detection systems are now an essential component in the all kinds of network even including wireless ad hoc network. With the rapid advancement in the network technologies the focus of intrusion detection has shifted from simple signature matching approaches to detecting attacks based on analyzing contextual information that employed in anomaly and hybrid intrusion detection approaches. This paper proposed a layered anomaly intrusion detection framework using Conditional Random Fields to detect a wide variety of attacks. With this framework attacks can be identified and intrusion response can be initiated in real time. Experiments show that the CRF model can detect attacks effectively.

引用

页码：186 / 189

页数：4

共 15 条

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