Acta Informatica Malaysia (AIM)

INTRUSION DETECTION AND PREVENTION FRAMEWORK USING DATA MINING TECHNIQUES FOR FINANCIAL SECTOR

ABSTRACT

INTRUSION DETECTION AND PREVENTION FRAMEWORK USING DATA MINING TECHNIQUES FOR FINANCIAL SECTOR

Journal: Acta Informatica Malaysia (AIM)
Author: Gaurav Sharma, Anil Kumar Kapil

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Doi:10.26480/aim.02.2021.58.61

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).

Pages 58-61
Year 2021
Issue 2
Volume 5

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