Van Lam Le
This person can no longer be contacted through the School of Engineering and Computer Science at Victoria University of Wellington
Van Lam is a PhD student in Network Engineering Research Group
in the School of Engineering and Computer Science. His research is focused on detecting malicious web pages by exploiting machine learning techniques.
Malicious web pages have become an emerging security issue nowadays. Its number has been increased significantly and it has raised a special concern from communities. Some approaches have been proposed to analyze malicious web pages. They can be categorized into three main groups: Signature, State change (rule-based) and machine learning. While signature approach works quite well on detecting virus, it is inefficient to detect malicious web pages due to complexity of web pages. Moreover, state change (rule-based) approach shows its capabilities to detect malicious web page but it is based on system events as a result from successful exploits happening on the client-side system. Therefore, it is good for detecting malware delivery but not detecting web-based client-side exploits or malicious web pages. In addition, state-of-art machine learning approach focuses on static feature extracted from contents or properties of web pages. However, it is uncertain to distinguish malicious web pages from benign ones due to obfuscation and compromising legitimate websites. Van Lam's research is focused on creating classification model to detect malicious web pages by exploiting machine learning techniques. The research is targeted at creating a novel classification model to detect malicious web pages efficiently.
| ResearchAreas || Malicious web pages, Drive-by-download, Client Honeypot |
| ThesisTitle || Exploiting Machine Learning Techniques to Detect Malicious Web Pages |
| Supervisor || Dr Ian Welch, Dr Xiaoying Sharon Gao and Dr Peter Komisarczuk |
| Qualifications || BSc(VN), MIT Newcastle (AU) |
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