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Intelligent and Quantum Secure Advanced Cyber Defense Research (IQSeC) Lab

Research Overview

The IQSeC Lab conducts research across three main areas: Machine Learning for Endpoint Security, Machine Learning for Network Security, and Quantum Security. Our work addresses practical and emerging challenges in these fields, aiming to enhance security and privacy through innovative approaches and collaborations.

💡 Machine Learning for Endpoint Security

Our endpoint security research focuses on creating adaptive malware detection systems that can learn from new data over time. Using continual learning techniques, we build models that improve as they encounter new types of malware, enabling more accurate detection even as threats evolve. By addressing issues like "catastrophic forgetting," where models may lose accuracy on older threats when updated with new data, our approach seeks to make malware detection systems both responsive and reliable.

💡 Machine Learning for Network Security

In network security, we explore how machine learning can uncover vulnerabilities in encrypted traffic, such as that in Tor, where unintended information leaks may expose users to risk. Our work includes not only identifying these vulnerabilities but also designing effective methods to defend against them, enhancing user privacy and network resilience. By developing attack and defense models, we contribute to understanding and mitigating privacy risks in encrypted network environments.

💡 Quantum Security

As quantum computing advances, traditional encryption methods may become vulnerable to quantum-based attacks. Our quantum security research addresses this challenge by developing secure communication protocols based on quantum key distribution (QKD) and post-quantum cryptography (PQC). We aim to build robust systems that can resist quantum-enabled threats, focusing on both theoretical foundations and practical implementations to secure communications for the future.


Current Students

The IQSeC Lab is fortunate to have a group of bright and dedicated students who contribute to advancing our research.

PhD Students

  • Md Ahsanul Haque

Bachelor Students

  • Jesus Lopez
  • Eduardo Menendez
  • Viviana Cadena

Collaborators

The IQSeC Lab collaborates with respected academic institutions and industry to advance research. These partnerships enable us to leverage complementary expertise and perspectives in pursuit of shared research goals.

Prospective Students

If you are a current UTEP student, please email me to set up a meeting to discuss further.
If you are not a current student and are interested in working with me, please continue reading.

Qualifications

A prospective PhD student should hold a bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related field. The ideal candidate is motivated to conduct research in machine learning and cybersecurity and demonstrates proficiency in at least three of the preferred skills listed.

My goal is to provide resources and guidance to help students become strong, independent researchers.

Preferred Skills/Experience

  • Programming in Python, PyTorch, and/or TensorFlow
  • Research or hands-on experience in Malware Analysis
  • Experience in Wired and Wireless Networking
  • Research or practical experience in ML/AI
  • Experience with Large Language Models (LLM)
  • Basic understanding of Quantum Information Science
  • Familiarity with Kali Linux, MITRE ATT&CK framework, Pentesting, and Hugging Face is a plus

Instructions for Emailing

  • Review relevant publications on the Lab's website and the PI’s Google Scholar profile before reaching out.
  • For general admissions information, refer to the CS@UTEP PhD page.
  • Use the email subject line: "Prospective PhD Student - <Intended Term>".
  • Include the following documents in your email:
    • (i) CV/Resume
    • (ii) Transcripts (partial transcript is acceptable)
    • (iii) A one-page research statement detailing your background and research interests
    • (iv) TOEFL/GRE scores, if applicable

Some Useful Resources