Faculty Profile

Tao Hou

Title
Assistant Professor
Department
Computer Science and Engineering
College
College of Engineering

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


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





          

        
                

          
Current Scheduled Teaching*


          
No current or future courses scheduled.


          
*
  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 6940.974, Individual Research, Spring 2024 
                    

                    CSCE 5550.002, Introduction to Computer Security, Spring 2024 Syllabus 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 Publications



  

  
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). 



	
		
		
	  

	

	

  
    
	

	

	  

		

		   
Awarded Grants



  
  
  	  
          
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. (March 1, 2024 – Present).



	
		
		
	  

	

	

	
	

  
  
  

  	

		

		,  
			
				

					
					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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