大数据 ›› 2023, Vol. 9 ›› Issue (6): 3-14.doi: 10.11959/j.issn.2096-0271.2023074

• 专题:大数据安全与隐私计算 • 上一篇    下一篇

大数据技术在数据安全治理中的应用

程伟1,2, 马成2, 凌捷3   

  1. 1 清华大学计算机科学与技术系,北京 100084
    2 联通(广东)产业互联网有限公司,广东 广州 510320
    3 广东工业大学计算机学院,广东 广州 510006
  • 出版日期:2023-11-15 发布日期:2023-11-01
  • 作者简介:程伟(1976- ),男,清华大学计算机科学与技术系博士生,联通(广东)产业互联网有限公司副总经理、高级工程师,主要研究方向为云计算、边缘计算及网络安全。
    马成(1996- ),男,联通(广东)产业互联网有限公司软件开发工程师,主要研究方向为数据安全。
    凌捷(1964- ),男,博士,广东工业大学计算机学院教授(二级)、博士生导师,兼任广东省大数据安全与服务工程技术研究中心主任、广东省电子政务信创企业重点实验室学术委员会主任等职。主要研究方向为网络信息安全、大数据安全、人工智能安全等,出版相关学术论著4部,在国内外重要期刊和国际会议上发表学术论文100多篇,获授权发明专利超过60件,获广东省科学技术奖一等奖1次、广东省科学技术奖二等奖2次,获南粤教书育人优秀教师等称号。
  • 基金资助:
    广州市重点领域研发计划项目(202007010004)

Application of big data technology in data security governance

Wei CHENG1,2, Cheng MA2, Jie LING3   

  1. 1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
    2 Unicom (Guangdong) Industrial Internet Co., Ltd., Guangzhou 510320, China
    3 School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
  • Online:2023-11-15 Published:2023-11-01
  • Supported by:
    Guangzhou Key Field Research and Development Project(202007010004)

摘要:

面对新形势下的数据安全治理挑战,顺应数据安全领域的技术发展趋势,针对大型国企在数据安全治理实际应用中突出的关键权限人员识别问题,提出了一种基于图算法的关键权限人员识别技术。该技术可以发现系统中潜在的权限影响因素,并可从多个角度衡量不同含义的权重影响力,识别结果可解释性强。针对数据安全治理中的用户与实体行为异常检测问题,提出一种基于生成对抗网络的用户与实体行为异常检测方法,实验结果表明,所提方法的精确率、召回率和F1值的平均值均优于对比基线模型方法。设计开发了数据安全平台,平台在降低数据安全风险、辅助企业合规建设、促进数据开发利用等方面起到了重要作用,已在多个数据集中管理项目中得到应用,能满足安全场景下的大数据处理需求,具有较好的应用推广价值。

关键词: 数据安全治理, 图算法, 用户与实体行为分析, 数据安全平台

Abstract:

Facing the challenges of data security governance in the new situation and following the technological development trends in the field of data security, in response to the prominent issue of identifying key authorized personnel in the practical application of data security governance in large state-owned enterprises, this article proposes a key authorized personnel identification technology based on graph algorithm, which can discover potential authorization influencing factors in the system and measure the weight influence of different meanings from multiple perspectives, The recognition results have strong interpretability.Aiming at the problems of user and entity behavior anomaly detection in data security governance, this paper proposes a user and entity behavior anomaly detection method based on the generative adversarial network.The experimental results show that the accuracy, recall rate and average F1-score of the proposed method are better than the comparison baseline model method.A data security platform has been designed and developed.The platform has played an important role in reducing data security risks, assisting enterprise compliance construction, promoting data development and utilization, and has been applied in multiple data centralized management projects.It can meet the needs of big data processing in security scenarios, and has good application and promotion value.

Key words: data security governance, graph algorithm, UEBA, data security platform

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