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Tao Hou

Title: Assistant Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, University of South Florida, 2022
    Major: Computer Science and Engineering

Current Scheduled Teaching

CSCE 5550.002Introduction to Computer SecuritySpring 2025
CSCE 5552.002Cybersecurity EssentialsFall 2024
CSCE 6940.874Individual ResearchFall 2024
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching

CSCE 6940.974Individual ResearchSpring 2024
CSCE 5550.002Introduction to Computer SecuritySpring 2024 SPOT
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Intellectual Contributions

    Conference Proceeding

  • Wang, T., Hou, T. (2023). Adaptive Feature Engineering via Attention- Based LSTM Towards High Performance Reconnaissance Attack Detection. Boston, MA, IEEE Military Communications Conference (IEEE MILCOM’23).
  • Wang, T., Hou, T. (2023). Exquisite Feature Selection for Machine Learning Powered Probing Attack Detection. Rome, Italy, IEEE International Conference on Communications (IEEE ICC’23).
  • Wang, T., Hou, T. (2023). SecDINT: Preventing Data-oriented Attacks via Intel SGX Escorted Data Integrity. Orlando, FL, IEEE Conference on Communications and Network Security (IEEE CNS’23).
  • Wang, T., Hou, T. (2022). DyWCP: Dynamic and Lightweight Data-Channel Coupling towards Confidentiality in IoT Security. San Antonio, Texas, ACM Conference on Security and Privacy in Wireless and Mobile Networks (ACM WiSec’22).
  • Wang, T., Hou, T. (2022). MUSTER: Subverting User Selection in MU-MIMO Networks. Virtual, IEEE International Conference on Computer Communications (IEEE INFOCOM’22).
  • Wang, T., Hou, T. (2022). Undermining Deep Learning Based Channel Estimation via Adversarial Wireless Signal Fabrication. San Antonio, Texas, ACM Workshop on Wireless Security and Machine Learning (ACM WiseML’22).
  • Wang, T., Hou, T. (2022). When Third-Party JavaScript Meets Cache: Explosively Amplifying Security Risks on The Internet. Austin, Texas, IEEE Conference on Communications and Network Security (IEEE CNS’22).
  • Wang, T., Hou, T. (2021). IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification. Virtual Conference, IEEE International Symposium on Dynamic Spectrum Access Networks (IEEE DySPAN’21).

    • Journal Article

  • Wang, T., Hou, T. (2021). Combating Adversarial Network Topology Inference by Proactive Topology Obfuscation. IEEE/ACM Transactions on Networking (ToN).

Contracts, Grants and Sponsored Research

    Grant - Research

  • Hou, T. (Principal), Wang, T. (Co-Principal), "Towards the Research and Education for Trustworthy and Resilient Artificial Intelligence of Things," sponsored by DoD Army Research Office (ARO), Federal, $222569 Funded. (2024).
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Overall
Summative Rating		 Challenge and
Engagement Index		 Response Rate

out of 5
out of 7		 %
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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