Tao Hou
Ph.D. student,
Department of Computer Science and Engineering, University of South Florida.
4202 E Fowler Ave, ENB 118, Tampa, FL 33620.
Phone: 813.390.8851      
Email: taohou@usf.edu



 
I am currently pursuing the Ph.D. degree in the Department of Computer Science and Engineering at University of South Florida. My advisors are Dr. Zhuo Lu and Dr. Yao Liu. I received my B.E. degree and M.E. degree from Jilin University, China in 2013 and 2016, respectively. My research interests span quite a few areas, including network security, system security, machine learning for cybersecurity, high performance computing, and graph data analytics. I am especially interested in research problems that arise from practical domains, with a focus on both experimental/empirical study and sound theoretical footings. More recently, I am working mostly on web security, binary analysis, and adversarial machine learning.

One of my calligraphy works, the contents come from a poem by Tang poet Li Bai, entitled HARD TRAVELING.

"A time will come to ride the wind and cleave the waves, I’ll set my cloud-white sail and cross the sea which raves."

Recent News
[Jul, 2021] Our paper on combating adversarial network inference has been accepted by IEEE/ACM Transactions on Networking (ToN).
[Jan, 2021] I am invited to serve in the artifact evaluation committee of EuroSys 2021.
[Jul, 2020] I am to serve as a PC member in the ACSAC 2020 artifact program.
[Dec, 2019] The paper on proactive network topology obfuscation has been accepted by IEEE INFOCOM 2020.
[Nov, 2019] Our paper "Smart Spying via Deep Learning: Inferring Your Activities from Encrypted Wireless Traffic" won the Best Paper Award at IEEE GlobalSIP'19.
[Nov, 2019] I have been selected to receive a 2019 ACSAC Student Travel Grant.
[Oct, 2019] I received the International Travel Grant from USF OGS to attend the IEEE GlobalSIP'19 conference in Ottawa, Canada.
Education
Ph.D. student, University of South Florida, Tampa, Florida   ( 2016 - Present )
RA in The Communications, Security, and Analytics (CSA) Lab.
GPA: 4.0 / 4.0 (All my courses got A or A+)
Master degree, Jilin University, China   ( 2016 )
RA in Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education of China.
Bachelor degree, Jilin University, China   ( 2013 )
RA in Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education of China.
Publications


Combating Adversarial Network Topology Inference by Proactive Topology Obfuscation     [ PDF ] 
Tao Hou, Tao Wang, Zhuo Lu, and Yao Liu
IEEE/ACM Transactions on Networking (IEEE/ACM ToN), 2021
 

Binary Code Similarity Detection through LSTM and Siamese Neural Network
Zhengping Luo, Tao Hou, Xiangrong Zhou, Hui Zeng, Zhuo Lu
EAI Endorsed Transactions on Security and Safety, 2021
 

Log Analytics in HPC: A Data-driven Reinforcement Learning Framework     [ PDF ] 
Zhengping Luo, Tao Hou, Tung Thanh Nguyen, Hui Zeng and Zhuo Lu
IEEE Conference on Computer Communications (IEEE INFOCOM'20) DDINS Workshop, Toronto, Canada, 2020
 

ProTO: Proactive Topology Obfuscation Against Adversarial Network Topology Inference     [ PDF ] 
Tao Hou, Zhe Qu, Tao Wang, Zhuo Lu, and Yao Liu
IEEE Conference on Computer Communications (IEEE INFOCOM'20), Toronto, Canada, 2020
 

I Know Your Activities Even When Data Is Encrypted: Smart Traffic Analysis via Fusion Deep Neural Network      [ PDF ]   [ Poster ]   
Tao Hou, Tao Wang, Zhuo Lu, and Yao Liu
Annual Computer Security Applications Conference (ACSA ACSAC'19), Poster Session, San Juan, Puerto Rico, 2019
 

Smart Spying via Deep Learning: Inferring Your Activities from Encrypted Wireless Traffic      [ PDF ]   [ Slides ]
Tao Hou, Tao Wang, Zhuo Lu, and Yao Liu
IEEE Global Conference on Signal and Information Processing (IEEE GlobalSIP'19), Ottawa, Canada, 2019
Best Paper Award
 

Autonomous Security Mechanisms for High-Performance Computing Systems: Review and Analysis      [ PDF ]
Tao Hou, Tao Wang, Dakun Shen, Zhuo Lu, and Yao Liu
Adaptive Autonomous Secure Cyber Systems, Springer, 2018
 

Signal Entanglement based Pinpoint Waveforming for Location-restricted Service Access Control     [ PDF ] 
Tao Wang, Yao Liu, Tao Hou, Qingqi Pei, and Song Fang
IEEE Transactions on Dependable and Secure Computing (IEEE TDSC), 2016
 

Location-restricted Services Access Control Leveraging Pinpoint Waveforming     [ PDF ]   [ Video Demo ]
Tao Wang, Yao Liu, Qingqi Pei, and Tao Hou
ACM Conference on Computer and Communications Security (ACM CCS'15), Denver, Colorado, 2015
 

Works
Experience
I create and organize the China fan club of famous actor Lee Minho. There are more than 368,560 fans join our club's membership since 2009. We have more than 30 provincial club chapters all over the China mainland, and 4 special chapters for Hong Kong, Taiwan, Korea, and Japan.
Website: Minhofans.com     Weibo: @Minhofans     Wechat Page: LOVEMHF
Official anthem: We Promise. Produced by me. Composed and sang by fans.   

Professional Info

Teaching

CGS1540.701F16 Intro to Database (TA, Fall 2016)
COP4710.001S16 Database Design (TA, Spring 2016)

Paper Reviewers

IEEE CCNCPS 2017
IEEE CNS 2017, 2018
ACM CCS-MTD 2017
ACM WiseML 2019
IEEE/ACM Transactions on Networking (ToN)
International Journal of Security and Networks (IJSN)
Journal of Computer Security
IEEE SmartGridComm 2017
IEEE ICC 2018, 2019
IEEE GlobalSIP 2019
MILCOM 2109
IEEE Access
IEEE Transactions on Information Forensics & Security (TIFS)
IEEE Transactions on Dependable and Secure Computing (TDSC)

Program Committee

ACSAC Artifacts 2020
EuroSys Artifacts 2021

Awards

IEEE GlobalSIP Best Paper Award
ACSAC'19 Student Travel Grant
USF International Travel Grant
USENIX Security'18 Student Grant
Outstanding Thesis (Jilin University)
IEEE S&P'20 Student Registration Award
NeTS Early Career Workshop Travel Grant
Black Hat USA 2018 Student Scholarship
Provincial-Level Merit Student

Professional Memberships

Student member of IEEE
Student member of ACM
Member of Tau Beta Pi, the engineering honor society
Research Projects

Extensible and Customizable Binary Code Analytics Engine for Malware Identification Using Deep Learning     [2019 ~ Present]

The ubiquity of information technology has made malware a serious threat. Detecting malware in a system is a difficult task, particularly when the malware is stealthy. The core research agenda is development of lightweight malware detection mechanisms using deep learning. Specifically, this project is interested in (i) developing effective machine-learning classifier against malware that are relatively inexpensive to implement; and (ii) development of tools and methods for evaluating effectiveness and robustness of various solution alternatives.

Development of Vulnerability Measurement and Defense Techniques for Web Security     [2017 ~ Present]

World Wide Web is a critical infrastructure that serves our society by facilitating information exchange, business and education. Our works focus on improving the security of the Internet, including developing new techniques for vulnerability measurement, and risk defense at Internet wide scale in a timely, accurate, complete, and ethical manner.

Developing Automated and Practical Frameworks to Reinforce Neural Networks in Cyber Security Domain Leveraging GANs     [2019 ~ Present]

With great usage and wide adoption of neural networks, especially in sensitive domain such as healthcare diagnosis and network security, the reliability of their results is necessary. This project aims to develop a defense mechanism, which is practical in real-world application with pre-trained neural networks, leveraging generative adversarial networks (GANs). Since the vulnerability against adversaries is inherent to the world of neural networks, using an iterative offensive approach to generate new attacks to help strengthen the neural network is the best defense.

Towards Secure and Reliable Network Tomography in Wireline and Wireless Networks     [2018 ~ 2019]

Today's networks, such as the Internet, cellular networks, and the Internet of Things, provide ubiquitous wired or wireless connections over large areas. Secure and reliable operations are among the most important objectives in these networks. Network tomography has become a promising framework for accurate monitoring of network operation status, which is vital to ensure an efficient and reliable network environment. However, this measurement process can be exploited by malicious attackers to generate falsified, misleading measurement or monitoring results, which significantly affects follow-on network operations based on these results, and accordingly degrades the operational reliability and health of today's networks. The goal of this project is to analyze security vulnerabilities, understand potential security attack strategies and their impact, and design effective defense mechanisms against such attacks.

SELECT: Secure and Lightweight Computing Environment for HPC systems     [2016 ~ 2018]

Providing strong cyber security tools that can protect the data and prevent tampering is of critical importance to secure HPC systems. Nonetheless, there are still no comprehensive software design and implementation to systemically address cyber security issues in HPC systems. To address this need, we propose to develop a Secure and Lightweight Computing Environment (SELECT) software tool for HPC systems, and the key innovation is to integrate both coarse-grained security and fine-grained security with low overhead to provide sensitive data leakage detection and real-time tampering defense.

MSanalysis: A Push-based System for Molecular Simulation Data Analysis     [2016]

The frontend user interface of MSanalysis is a web application written by python based on Django, while the backend processing engine is developed by C++ following the proposed push-based data processing model.
Known scientific data analysis systems, as well as traditional DBMSs, follow a pull-based architectural design, where the executed queries mandate the data needed. Such design involves redundant and random I/Os, considerably affecting the data throughput in the system. We design and implement a push-based type system that allows high-throughput data analysis in the process of scientific discovery. By this way the system lowers the unnecessary I/O overhead imposed by the randomized, index-based scan and that of a multiple data reads if each query were to be fed separately.
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