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Intrusion Detection And Prevention Framework Using Data Mining Techniques For Financial Sector

Author

Listed:
  • Gaurav Sharma

    (Research Scholar, Faculty of Mathematics and Computer Sciences, Motherhood University, Roorkee, Uttarakhand, India)

  • Anil Kumar Kapil

    (Research Scholar, Faculty of Mathematics and Computer Sciences, Motherhood University, Roorkee, Uttarakhand, India)

Abstract

Security becomes the main concern when the resources are shared over a network for many purposes. For ease of use and time saving several services offered by banks and other financial companies are accessible over mobile apps and computers connected with the Internet. Intrusion detection (ID) is the act of detecting actions that attempt to compromise the confidentiality, integrity, or availability of a shared resource over a network. Intrusion detection does not include the prevention of intrusions. A different solution is required for intrusion prevention. The major intrusion detection technique is host-based where major accountabilities are taken by the server itself to detect relevant security attacks. In this paper, an intrusion detection algorithm using data mining is presented. The proposed algorithm is compared with the signature apriori algorithm for performance. The proposed algorithm observed better results. This framework may help to explore new areas of future research in increasing security in the banking and financial sector enabled by an intrusion detection system (IDS).

Suggested Citation

  • Gaurav Sharma & Anil Kumar Kapil, 2021. "Intrusion Detection And Prevention Framework Using Data Mining Techniques For Financial Sector," Acta Informatica Malaysia (AIM), Zibeline International Publishing, vol. 5(2), pages 58-61, December.
    • Handle: RePEc:zib:zbnaim:v:5:y:2021:i:2:p:58-61
    DOI: 10.26480/aim.02.2021.58.61
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    References listed on IDEAS

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    1. Rohit Rana & Rajendra Kumar, 2019. "Performance Analysis Of Aodv In Presence Of Malicious Node," Acta Electronica Malaysia (AEM), Zibeline International Publishing, vol. 3(1), pages 1-5, January.
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    Cited by:

    1. Li, Zeyun & Kuo, Tsung-Hsien & Siao-Yun, Wei & The Vinh, Luu, 2022. "Role of green finance, volatility and risk in promoting the investments in Renewable Energy Resources in the post-covid-19," Resources Policy, Elsevier, vol. 76(C).

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