In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Technology of Information Security
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 4
1128-1131

Research of database user behavior anomaly detection based on K-means and naive Bayes

Wang Xuren1,2
Feng Anran1,2
He Famei2,3
Ma Huizhen1,2
Yang Jie1,2
1. College of Information Engineering, Capital Normal University, Beijing 100048, China
2. Key Laboratory of Network Assessment Technology, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
3. Library, Beijing Institute of Technology, Beijing 100081, China
DOI: 10.19734/j.issn.1001-3695.2018.09.0755

Abstract

Aiming at database leakage caused by abnormal database user behavior, this paper proposed a database user ano-maly detection method based on K-means and naive Bayes algorithm. Firstly, the K-means clustering method obtained users' grouping based on the user's query statements and query results in the database historical audit logs; then, the naive Bayes classification algorithm constructed the user anomaly detection model. Compared with the model constructed by naive Bayes classification alone, the simplified representation of user behavior profile reduces computational redundancy and reduces trai-ning time by 81%. Applying K-means clustering method to obtaining users' grouping improves the detection accuracy by 7.06% and the F1 value by 3.33%. Experiments show that the proposed method greatly reduces the training time and achieves better detection results.

Foundation Support

国家自然科学基金资助项目(61373161)
中国科学院信息工程研究所中国科学院网络测评技术重点实验室2018开放课题

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.09.0755
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 4
Section: Technology of Information Security
Pages: 1128-1131
Serial Number: 1001-3695(2020)04-036-1128-04

Publish History

[2020-04-05] Printed Article

Cite This Article

王旭仁, 冯安然, 何发镁, 等. 基于K-means和naive Bayes的数据库用户行为异常检测研究 [J]. 计算机应用研究, 2020, 37 (4): 1128-1131. (Wang Xuren, Feng Anran, He Famei, et al. Research of database user behavior anomaly detection based on K-means and naive Bayes [J]. Application Research of Computers, 2020, 37 (4): 1128-1131. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)