Staff Profile

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Colin Tan Choon Lin

Lecturer
MIEEE, Grad. Eng. (BEM)
PhD in Computer Science (UNIMAS)
MSc in Computer Science (UNIMAS)
BEng (Hons) in Electronics & Computer Engineering (UNIMAS)

Faculty of Engineering, Computing and Science

Office No: +60 82 260 688
Fax No:+60 82 260 813
Room No: E322, Building E, FECS
Email: ctan@swinburne.edu.my
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Biography

Colin Tan received his BEng (Hons) in Electronics and Computer Engineering and MSc in Computer Science from Universiti Malaysia Sarawak (UNIMAS). After completing his Master’s study in 2016, he went to Kuala Lumpur and worked as a lecturer at Erican College. In 2017, he continued his study at UNIMAS in the information security domain, specializing in the detection of phishing websites. Throughout his research journey, Colin has published a number of articles in top-tier ISI indexed journals. In 2021, Colin obtained his PhD and joined Swinburne University of Technology Sarawak as a lecturer.

Research Interests

  • Information security
  • Anti-phishing
  • Machine learning
  • Feature selection
  • Image processing

Professional Memberships

  • Member, Institute of Electrical and Electronics Engineers (IEEE)
  • Graduate Member, Board of Engineers Malaysia (BEM)

PhD/Master by Research Opportunities

Potential research higher degree candidates are welcome to enquire about postgraduate opportunities in the areas listed above. Please e-mail ctan@swinburne.edu.my for more information.

Publications

    • Journal Paper Tan, C. L., Chiew, K. L., Yong, K. S. C., Sze, S. N., Abdullah, J., & Sebastian, Y. (2020). A graph-theoretic approach for the detection of phishing webpages. Computers & Security, 95, 101793.
    • Journal Paper Chiew, K. L., Tan, C. L., Wong, K., Yong, K. S. C., & Tiong, W. K. (2019). A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Information Sciences, 484, 153–166
    • Journal Paper Chiew, K. L., Chang, E. H., Tan, C. L., Abdullah, J., & Yong, K. S. C. (2018). Building Standard Offline Anti-phishing Dataset for Benchmarking. International Journal of Engineering & Technology, 7(4.31), 7–14.
    • Journal Paper Tan, C. L., Chiew, K. L., Musa, N., & Abang Ibrahim, D. H. (2018). Identifying the Most Effective Feature Category in Machine Learning-based Phishing Website Detection. International Journal of Engineering & Technology, 7(4.31), 1–6.
    • Journal Paper Chiew, K. L., Yong, K. S. C., & Tan, C. L. (2018). A survey of phishing attacks: Their types, vectors and technical approaches. Expert Systems with Applications, 106, 1–20.
    • Journal Paper Tan, C. L., Chiew, K. L., Wong, K., & Sze, S. N. (2016). PhishWHO: Phishing webpage detection via identity keywords extraction and target domain name finder. Decision Support Systems, 88, 18–27.
    • Conference Paper Yong, K. S. C., Chiew, K. L., & Tan, C. L. (2019). A survey of the QR code phishing: the current attacks and countermeasures. Proceedings of the 7th International Conference on Smart Computing & Communications, 1–5.
    • Conference Paper Tan, C. L., Chiew, K. L., & Sze, S. N. (2017). Phishing Webpage Detection Using Weighted URL Tokens for Identity Keywords Retrieval. Proceedings of the 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, 133–139.
    • Conference Paper Tan, C. L., Chiew, K. L., & Sze, S. N. (2014). Phishing website detection using URL-assisted brand name weighting system. Proceedings of the International Symposium on Intelligent Signal Processing and Communication Systems, 54–59.