IDEAS home Printed from https://ideas.repec.org/a/bit/bsrysr/v8y2017i1p51-59n5.html
   My bibliography  Save this article

Data Mining Usage in Corporate Information Security: Intrusion Detection Applications

Author

Listed:
  • Al Quhtani Masoud

    (Embassy of the Kingdom of Saudi Arabia in Bosnia and Herzegovina, Sarajevo, Bosnia and Herzegovina)

Abstract

Background: The globalization era has brought with it the development of high technology, and therefore new methods of preserving and storing data. New data storing techniques ensure data are stored for longer periods of time, more efficiently and with a higher quality, but also with a higher data abuse risk. Objective: The goal of the paper is to provide a review of the data mining applications for the purpose of corporate information security, and intrusion detection in particular. Methods/approach: The review was conducted using the systematic analysis of the previously published papers on the usage of data mining in the field of corporate information security. Results: This paper demonstrates that the use of data mining applications is extremely useful and has a great importance for establishing corporate information security. Data mining applications are directly related to issues of intrusion detection and privacy protection. Conclusions: The most important fact that can be specified based on this study is that corporations can establish a sustainable and efficient data mining system that will ensure privacy and successful protection against unwanted intrusions.

Suggested Citation

  • Al Quhtani Masoud, 2017. "Data Mining Usage in Corporate Information Security: Intrusion Detection Applications," Business Systems Research, Sciendo, vol. 8(1), pages 51-59, March.
    • Handle: RePEc:bit:bsrysr:v:8:y:2017:i:1:p:51-59:n:5
    DOI: 10.1515/bsrj-2017-0005
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/bsrj-2017-0005
    Download Restriction: no
    File URL: https://libkey.io/10.1515/bsrj-2017-0005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Stoel, M. Dale & Muhanna, Waleed A., 2011. "IT internal control weaknesses and firm performance: An organizational liability lens," International Journal of Accounting Information Systems, Elsevier, vol. 12(4), pages 280-304.
    2. Willison , Robert, 2006. "Understanding the Perpetration of Employee Computer Crime in the Organisational Context," Working Papers 2006-4, Copenhagen Business School, Department of Informatics.
    3. B Baesens & C Mues & D Martens & J Vanthienen, 2009. "50 years of data mining and OR: upcoming trends and challenges," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 16-23, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kenda Klemen & Mladenić Dunja, 2018. "Autonomous Sensor Data Cleaning in Stream Mining Setting," Business Systems Research, Sciendo, vol. 9(2), pages 69-79, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Feng, Yuan & Mao, Yihuan & Cai, Jing & Xu, Nan, 2024. "Can board IT expertise improve corporate internal control?," Finance Research Letters, Elsevier, vol. 62(PA).
    2. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    3. Wu, Tung-Hsien & Huang, Shaio Yan & Chiu, An-An & Yen, David C., 2024. "IT governance and IT controls: Analysis from an internal auditing perspective," International Journal of Accounting Information Systems, Elsevier, vol. 52(C).
    4. Igor Bernik & Kaja Prislan, 2016. "Measuring Information Security Performance with 10 by 10 Model for Holistic State Evaluation," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-33, September.
    5. J. D’Haen & D. Van Den Poel & D. Thorleuchter, 2012. "Predicting Customer Profitability During Acquisition: Finding the Optimal Combination of Data Source and Data Mining Technique," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/818, Ghent University, Faculty of Economics and Business Administration.
    6. Ali, Irfan, 2016. "The impact of ERP implementation on the financial performance of the firm : An empirical study," Other publications TiSEM 876506f5-1aed-4421-aa2c-0, Tilburg University, School of Economics and Management.
    7. Benaroch, Michel & Chernobai, Anna & Goldstein, James, 2012. "An internal control perspective on the market value consequences of IT operational risk events," International Journal of Accounting Information Systems, Elsevier, vol. 13(4), pages 357-381.
    8. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    9. Wei Yu & Huiqin Huang & Xinyan Kong & Keying Zhu, 2023. "Can Digital Inclusive Finance Improve the Financial Performance of SMEs?," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    10. Plant, Olivia H. & van Hillegersberg, Jos & Aldea, Adina, 2022. "Rethinking IT governance: Designing a framework for mitigating risk and fostering internal control in a DevOps environment," International Journal of Accounting Information Systems, Elsevier, vol. 45(C).
    11. Hyunjung Nam & Won Gyun No & Youngsu Lee, 2017. "Are Commercial Financial Databases Reliable? New Evidence from Korea," Sustainability, MDPI, vol. 9(8), pages 1-23, August.
    12. Lijuan Tao & Xiaoju Wei & Wenjing Wang, 2023. "Does Enterprise Internal Control Improve Environmental Performance—Empirical Evidence from China," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    13. Rose, Anna M. & Rose, Jacob M. & Obermire, Kara M. & Strand Norman, Carolyn & Frydenlund, Nicole, 2023. "How can firms repair their reputations when they discover information technology control material weaknesses?," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    14. Wei Yu & Huiqin Huang & Keying Zhu, 2023. "Enhancing Construction Enterprise Financial Performance through Digital Inclusive Finance: An Insight into Supply Chain Finance," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    15. J. D’Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.
    16. Amitava Dutta & Rahul Roy, 2008. "Dynamics of organizational information security," System Dynamics Review, System Dynamics Society, vol. 24(3), pages 349-375, September.
    17. Mai Dao & Trung Pham & Hongkang Xu, 2022. "Internal control effectiveness and trade credit," Review of Quantitative Finance and Accounting, Springer, vol. 59(4), pages 1423-1452, November.
    18. Yasmin MOBASHER, 2022. "The Importance Of Implementing Integrated Information Systems In Hospitals," Business Excellence and Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 12(5), pages 5-21, October.
    19. Saridakis, Charalampos & Katsikeas, Constantine S. & Angelidou, Sofia & Oikonomidou, Maria & Pratikakis, Polyvios, 2023. "Mining Twitter lists to extract brand-related associative information for celebrity endorsement," European Journal of Operational Research, Elsevier, vol. 311(1), pages 316-332.
    20. Martin-Barragan, Belen & Lillo, Rosa & Romo, Juan, 2014. "Interpretable support vector machines for functional data," European Journal of Operational Research, Elsevier, vol. 232(1), pages 146-155.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bit:bsrysr:v:8:y:2017:i:1:p:51-59:n:5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.