Tapadhir Das

Tapadhir Das

Assistant Professor
Stockton
Office
Chambers 119
Email Address
tdas@pacific.edu
Phone Number
209.932.3604

My name is Tapadhir, and I am an Assistant Professor in the Department of Computer Science at the University of the Pacific. My research focuses on leveraging the usage of machine learning and artificial intelligence to protect infrastructures like the Internet of Things and cyber-physical systems from cyber-attacks. I enjoy teaching undergraduate and graduate-level courses in computer security, networks, machine learning, and interdisciplinary cybersecurity.  More information can be found on my personal website at tapadhirdas.com. 

Education

Ph.D., Computer Science & Engineering, University of Nevada, Reno, 2023 

M.S., Computer Science & Engineering, University of Nevada, Reno, 2020 

B.S., Computer Engineering Technology, Oregon Institute of Technology, 2018  

Teaching Interests
  • Computer Networks  
  • Computer Security  
  • ML-enabled Cybersecurity  
  • Interdisciplinary Cybersecurity  
Research Focus
  • Internet of Things Security  
  • Cyber-physical Systems Security  
  • Machine Learning for Cybersecurity  
  • Adversarial Machine Learning  


SELECTED PUBLICATIONS

For a full list of publications, please check out my website: 

  • Lochana Telugu Rajesh, Tapadhir Das, Raj Mani Shukla, and Shamik Sengupta, "Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection," in 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), IEEE, 2023. (Tier A Conference, Acceptance Rate: 30%) 
  • Amber Hankins, Tapadhir Das, Shamik Sengupta, and David Feil-Seifer, "Eyes on the Road: A Survey on Cyber Attacks and Defense Solutions for Vehicular Ad-Hoc Networks," in 2023 IEEE Computing and Communication Workshop and Conference (CCWC), IEEE, 2023. 
  • Tapadhir Das,  Osama Abu Hamdan, Raj Mani Shukla, Shamik  Sengupta,  and Engin Arslan “UNR-IDD: Intrusion Detection Dataset using Network Port Statistics,” in 2023 IEEE Consumer Communications and Networking Conference (CCNC), IEEE, 2023. 
  • Tapadhir Das,  Osama Abu Hamdan, Shamik  Sengupta,  and Engin Arslan “Flood Control: TCP-SYN Flood Detection for Software-Defined Networks using OpenFlow Port Statistics,” in 2022 IEEE International Conference on Cyber Security and Resilience (CSR), IEEE, 2022. 
  • Tapadhir Das,  Raj Mani Shukla,  and  Shamik  Sengupta,  “What Could Possibly Go Wrong?: Identification of Current Challenges and Prospective Opportunities for Anomaly Detection in Internet of Things," in IEEE Network Magazine, IEEE, 2022. (Impact Factor: 10.69) 
  • AbdelRahman Eldosouky*, Tapadhir Das*,  Anuraag Kotra,  and  Shamik  Sengupta,  “Finding the Sweet Spot for Data Anonymization: A Mechanism Design Perspective,” in IEEE Access, IEEE, 2022. (* denotes co-first authorship) (Impact Factor: 3.9) 
  • Aaron Walker, Tapadhir Das, Raj Mani Shukla, and Shamik Sengupta, "Friend or Foe: Discerning Benign vs Malicious Software and Malware Family," in 2021 IEEE Global Communications Conference (GLOBECOM), IEEE, 2021. (IEEE ComSoc Flagship Conference, Tier A Conference) 
  • Tapadhir Das, Raj Mani Shukla, and Shamik Sengupta, "The Devil is in the Details: Confident & Explainable Anomaly Detector for Software-Defined Networks,” in 2021 IEEE 20th International Symposium on Network Computing and Applications (NCA), IEEE, 2021. (Tier A Conference)