An artificial immune system based learning algorithm for abnormal or fraudulence detection in data stream

  • Authors:

  • Vincent C. S. Lee;Xingjian Yang

  • Affiliations:

  • Clayton School of Information Technology, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia;Clayton School of Information Technology, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia

  • Venue:
  • ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper proposes a prototype artificial immune based abnormal transaction detection system (ATDS) for the real time detection of abnormal or fraud transaction. We assess the performance of ATDS in discovery of rare patterns using a proprietary stock tick-time transaction data. We find that ATDS is capable of detecting the abnormal transactions and triggers the generation of alert signals for subsequent investigation to identify the abnormal or fraudulence transactions.