Strategies for Identifying Online Scams
ID:83 View Protection:ATTENDEE Updated Time:2024-08-20 10:36:18 Hits:1587 Oral Presentation

Start Time:2024-10-25 16:15(Asia/Bangkok)

Duration:15min

Session:RS2 Regular Session 2 » RS2-2Privacy, Security for Networks

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Abstract
With the rapid growth of online transactions and interactions, the threat landscape of scams and fraud has evolved, necessitating sophisticated detection mechanisms. This paper provides an extensive review of the latest advances in detecting online scams and fraud, covering technological solutions, machine learning techniques, and emerging trends in the field. Key methods discussed include advanced machine learning algorithms for anomaly detection, user behavior analytics, and the integration of threat intelligence. Additionally, the study highlights the role of public awareness and education in preventing scams, as well as the importance of international collaboration in law enforcement. By examining current trends and emerging technologies, this study provides strategies for organizations and individuals to enhance their digital security posture, effectively mitigating the risks associated with online scams and frauds.
Keywords
Industrial growth,fraud,scammer,detection,digital technology
Speaker
Wai Yie Leong
Senior Professor INTI International University

Submission Author
Wai Yie Leong INTI International University
Yuan Zhi Leong Schneider Electric Singapore Pte. Ltd.
Wai San Leong Schneider Electric Singapore Pte. Ltd
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Important Date
  • Conference Date

    Oct 24

    2024

    to

    Oct 27

    2024

  • Oct 14 2024

    Draft paper submission deadline

  • Oct 29 2024

    Registration deadline

  • Oct 31 2024

    Contribution Submission Deadline

Sponsored By
United Societies of Science
King Mongkut's University of Technology North Bangkok (KMUTNB)
IEEE Thailand Section
IEEE Thailand Section C Chapter
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