[1]许洪军,张洪,贺维. 一种基于鼠标行为的云用户异常检测方法[J].哈尔滨理工大学学报,2019,(04):127-132.[doi:10.15938/j.jhust.2019.04.021]
 XU Hong jun,ZHANG Hong,HE Wei. A Cloud User Anomaly Detection Method Based on Mouse Behavior[J].哈尔滨理工大学学报,2019,(04):127-132.[doi:10.15938/j.jhust.2019.04.021]
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 一种基于鼠标行为的云用户异常检测方法()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2019年04期
页码:
127-132
栏目:
计算机与控制工程
出版日期:
2019-08-25

文章信息/Info

Title:
 A Cloud User Anomaly Detection Method Based on Mouse Behavior
文章编号:
1007-2683(2019)04-0127-06
作者:
 许洪军12张洪2贺维3
 (1.哈尔滨理工大学 计算机科学与技术学院,黑龙江 哈尔滨 150080;
2.黑龙江农业工程职业学院 信息学院,黑龙江 哈尔滨 150088;
3.哈尔滨师范大学 计算机科学与信息工程学院,黑龙江 哈尔滨 150025)
Author(s):
 XU Hongjun12ZHANG Hong2HE Wei3
 (1.School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080,China; 
2.School of Information, Heilongjiang Agricultural Engineering Vocational College, Harbin 150088,China; 
3.School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025,China)
关键词:
 云安全深度学习卷积神经网络操作行为异常行为检测
Keywords:
 cloud security depth learning convolutional neural network(CNN) operational behavior Abnormal behavior detection
分类号:
TP301.6
DOI:
10.15938/j.jhust.2019.04.021
文献标志码:
A
摘要:
 针对由于云用户的非法操作产生的云安全威胁问题,提出一种在保障云用户隐私的前提下,利用深度学习技术对用户工作中的鼠标操作行为进行分析,实现检测云用户异常行为的方法。该方法首先通过鼠标追踪工具,记录一定时间内用户的基本鼠标操作行为轨迹,然后利用卷积神经网络对记录的行为轨迹图像进行特征学习和分类。通过实验可知,所提出的方法能够在保障用户隐私的前提下,有效的检测用户的异常行为,同时可以避免对系统高维特征数据分析和处理,降低了异常行为检测的难度。
Abstract:
 Aiming at the problem of cloud security threat caused by illegal operation of cloud users, this paper proposes a method to detect the abnormal behavior of cloud users by analyzing the mouse operation behavior in user’s work by using deep learning technology under the premise of ensuring the privacy of cloud users. Firstly, the mouse track tool is used to record the trajectory of the user’s basic mouse operation within a certain period of time. Then, the convolution neural network is used to learn and classify the recorded trajectories. The experimental results show that the proposed method can effectively detect abnormal behavior of users under the precondition of ensuring user privacy, meanwhile, it can avoid the analysis and processing of high dimensional feature data and reduce the difficulty of abnormal behavior detection.

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备注/Memo

备注/Memo:
 收稿日期: 2017-12-27
基金项目: 国家自然科学基金(61370031,61702142).
作者简介:
许洪军(1966—),男,硕士,教授;
张洪(1980—),男,硕士,讲师.
通信作者:
贺维(1980—),男,博士,讲师,E-mail: 64282426@qq.com.
更新日期/Last Update: 2019-09-04