吉林大学学报(理学版) ›› 2019, Vol. 57 ›› Issue (2): 339-344.

• 计算机科学 • 上一篇    下一篇

高级持续性威胁中攻击特征的分析与检测

董刚1, 余伟1, 玄光哲2   

  1. 1. 吉林大学 软件学院, 长春 130012; 2. 吉林大学 大数据和网络管理中心, 长春 130012
  • 收稿日期:2018-05-22 出版日期:2019-03-26 发布日期:2019-03-26
  • 通讯作者: 董刚 E-mail:donggang@jlu.edu.cn

Analysis and Detection of Attack Characteristics inAdvanced Persistent Threats#br#

DONG Gang1, YU Wei1, XUAN Guangzhe2   

  1. 1. College of Software, Jilin University, Changchun 130012, China;2. Center for Big Data and Network Management, Jilin University, Changchun 130012, China
  • Received:2018-05-22 Online:2019-03-26 Published:2019-03-26
  • Contact: DONG Gang E-mail:donggang@jlu.edu.cn

摘要: 针对高级持续性威胁的检测问题, 提出一种基于网络连接特征属性的检测方法. 通过数据采集、 特征提取、 异常检测和实时报警4个步骤, 选取网络连接的12种特征属性, 应用机器学习方法分析属性特征数据集, 建立高级持续性威胁攻击检测模型. 实验结果表明, 该方法对于高级持续性威胁攻击检测性能良好, 检测率较高, 误报率较低.

关键词: 高级持续性威胁, 攻击特征, 网络安全检测, 机器学习

Abstract: Aiming at the detection problems of advanced persistent threats, we proposed a detection method based on the attributes of network connection. Through four steps of data acquisition, characteristic extraction, anomaly detection and realtime alarm, we selected 12 kinds of attributes of network connection and applied machine learning methods to analyze attribute feature data set, and  established detection model of advanced persistent threat attacks. Experimental results show that the proposed method has good detection performance for advanced persistent threat attacks, high detection rate and low false alarm rate.

Key words: advanced persistent threat, attack characteristics, network security detection, machine learning

中图分类号: 

  • TP393