Advanced Network Technologies and Services Laboratory

蕭舜文 Shun-Wen Mike Hsiao is an Associate Professor in the Department of Management Information Systems at the College of Commerce at National Chengchi University, Taiwan (國立政治大學資訊管理學系, 政大資管). My research interests include cybersecurity, data analysis (language model, deep learning), computer virtualization, and FinTech (Blockchain and Smart Contract). My information is listed in Google Scholar Citation, Linkedin Profile, GitHub, and ORCID (0000-0003-0780-8144).

E-mail: hsiaom at nccu.edu.tw

Office: 商學院 261220; Lab: 商學院 260521

Tel: +886-2-29393091 #81220; Fax: +886-2-29393754 ; Lab: #85020, #85021

Address: No. 64, Sec. 2, Zhinan Rd., Wenshan Dist., Taipei City 11605, Taiwan

I am the principal investigator of Advanced Network Technologies and Service Laboratory (ANTSLab). I am also a member of the Information Security Center (國立政治大學資安科技研究中心) at NCCU and Taiwan Academic Cybersecurity Center (TACC). I am also a joint associate professor of Master Program in Information Security, NCCU.

News and Announcements

Recruiting Ph.D. Students, Graduate Students and Part-Time Assistants

(Updated in 2023/09/04)

This lab is looking for students/assistants to work with me and other lab members. Please email me with your interests and qualifications. Members of the lab share an interest in comprehending various advanced technologies, such as computer networks, cloud computing, cybersecurity, data science, and FinTech, and providing practical services to solve real-world problems. We value students of diverse technology backgrounds with strong skills and a passion for science and critical thinking. Usually, a graduate student in the lab needs to solve a real-world problem by reviewing past solutions, developing his/her own theory, building a prototype system, verifying or evaluating the effectiveness of your system, and clarifying the impact on the community. In short, being an architect of problem-solving.

(The following is adapted from the University of Minnesota.) Graduate students in the lab will need to 1) have personal motivation, curiosity, and enthusiasm for learning how the world works, 2) develop a plan to build background skills, 3) become an expert, i.e., subject matter expert, in some aspect(s) of the way the lab functions and share them with your colleagues, 4) communicate with your advisers about your classes, research, and teaching workload to make the best use of your time, 5) make sure that you read and keep up with the published literature so that you understand what is novel and important in your area of research, and 6) present regularly in laboratory meetings.

Currently, several indicative research topics of interest include (but are not limited to)

Security-Related System: Windows/Android/IoT malware behavior profiling and analysis, Cloud security, and virtualization. Students in this group will be familiar with several monitoring techniques on different platforms, process execution details, and malware behaviors. It is suitable for students who are interested in software engineering, system design, and implementation.
FinTech: the applicability of blockchain or smart contract, blockchain re-engineering for financial applications. If you are interested in cryptocurrency, smart contracts, and underlying proof-of-work mechanisms, this group might be one of your options. You will be able to build your own blockchain system and try to improve it for further applications, such as autonomous organization and application.
Artificial Intelligence: malware analysis (both static and dynamic) by using AI techniques, the recommendation system for financial applications, and language models of attack inferencing. You will work as a data scientist who cleans data, analyzes data, designs new AI architecture, implements models in Tensorflow, and tests them by real-world data.

Research Fingerprint

The research fingerprint map is generated from the manuscript of my published paper. (Updated in 2019/09/07)

All-time research fingerprint (2008~)

I was working on network-based intrusion detection system that can inspect network service behavior and identify so-called 'attack symptom' anomalies to infer the current execution state of a server process with a customized finite state machine (FSM) model and a probabilistic attack inference model.

Recent research fingerprint (2016~)

Currently, I focus on building security systems that leverage different neural networks (VAE, transformer, BNN, GAN) to analyze malware behavior and the characteristic of malware family, especially using text-based sequential data (e.g., api call sequence) obtained from the dynamic analysis tool by using real-world (Android/Windows/IoT) malware samples.

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