skip to main content
10.1145/3548608.3559315acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccirConference Proceedingsconference-collections
research-article

Statistical Analysis of threatening IP in Universities Based Automated Script

Authors Info & Claims
Published:14 October 2022Publication History

ABSTRACT

The operation of cyber security has gradually become a significant part of informatization in the process of smart campus construction. However, universities rely too much on physical security devices in the current network security operation process, and lack of self-defined statistical analysis automation program. The current network logs have the following problems: difficult to gather data-sets of different application systems, difficult to unify attribute names of different data-sets, difficult to count the number of records with attacker and victim as the only items, and besieged to do repetitive work daily. In order to solve the above problems, this paper proposes an automatic statistical threatening IP algorithm, which can uniquely locate the attacker and victim and process other data through a programmatic way. Finally, it is used to report statistical data to departments related to cyber security. The results of experiment show that the proposed algorithm can effectively complete the statistical work of network attack threatening IP details.

References

  1. Al'Aziz B, Sukarno P, Wardana A A. (2020). Blacklisted IP Distribution System to handle DDoS attacks on IPS Snort based on Blockchain. 2020 6th Information Technology International Seminar (ITIS), 41-45.Google ScholarGoogle Scholar
  2. Li T, Li J, Liu Z L, Li P, Jia C F. (2018). Differentially private Naive Bayes learning over multiple data sources. Information Sciences. 444:89-104.Google ScholarGoogle ScholarCross RefCross Ref
  3. Tabash M, Allah M A, Tawfik B. (2020). Intrusion Detection Model Using Naive Bayes and Deep Learning Technique. International Arab Journal of Information Technology, 17(2):215-224.Google ScholarGoogle ScholarCross RefCross Ref
  4. Kumar BJS, Anaswara PP. (2018). Vulnerability detection and prevention of SQL injection. International Journal of Engineering and Technology, 7(2.31):16-18.Google ScholarGoogle Scholar
  5. Yunus M, Brohan M Z, Nawi N M, Surin E (2018). Review of SQL injection: problems and prevention.International Journal on Informatics Visualization.2(3-2):215-219.Google ScholarGoogle Scholar
  6. Licui M. (2017). Research on key technologies of security for smart grid.BeijingJiaotong University.1-12. (in chinese).M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, 1989.Google ScholarGoogle Scholar
  7. Park M C, Dong H L. (2020). Random CFI (RCFI):efficient fine-grained control-flow integrity through random verification. IEEE Transactions on Computers, 99:1-1.Google ScholarGoogle Scholar
  8. Shangru Z, Xuejun L,Yue F, (2019). A survey on automated exploit generation. Journal of Computer Research and Development.56(10):2097-2111. (in chinese).Google ScholarGoogle Scholar
  9. Khalil I, Khreishah A, Azeem M. (2014). Cloud computing security:A survey. Computers, 3(1):1-35.Google ScholarGoogle ScholarCross RefCross Ref
  10. Zand A, Modelo-Howard G, Tongaonker A, (2017). Demystifying DDoS as a service. IEEE Communications Magazine,55(7):14-21.Google ScholarGoogle ScholarDigital LibraryDigital Library
  1. Statistical Analysis of threatening IP in Universities Based Automated Script

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ICCIR '22: Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics
      June 2022
      905 pages
      ISBN:9781450397179
      DOI:10.1145/3548608

      Copyright © 2022 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 14 October 2022

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate131of239submissions,55%
    • Article Metrics

      • Downloads (Last 12 months)10
      • Downloads (Last 6 weeks)0

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format