ESC
University of New Haven

Department of Electrical & Computer Engineering and Computer Science

CyTRAIL Lab carries out research in offensive security and digital forensics. We investigate software vulnerabilities and the ways they can be exploited. Based on this knowledge, we analyze the digital evidence produced by attacks and develop new forensic and defensive methods. We also aim to build realistic security and forensic datasets to support the development and evaluation of more effective AI models in these areas.

Effective cybersecurity defense requires understanding software vulnerabilities, how and by whom they can be exploited, and how resulting incidents can be investigated.

Our research pipeline

vulnerability threat evidence defense

Our researchers identify vulnerabilities that make attacks possible and study the threats and exploitation techniques related to them. Identified vulnerabilities are reported through responsible disclosure so that they can be fixed. We then examine the evidence generated by attacks and use these findings to develop improved forensic and defensive methods. In this way, our offensive security research and digital forensics research are closely connected.

Research themes

Vulnerability Discovery Finding the weaknesses that make attacks possible, and reporting them through coordinated disclosure. Threat Research Studying how adversaries exploit weaknesses and how they behave once inside. Digital Forensics Detecting contradictory and tampered digital evidence, and building investigative methods for new technologies. AI-Assisted Cyber Operations Applying AI to strengthen vulnerability identification and security operations.

Selected publications

  1. 2026
    Gunestas, M., Kunwar, K., & Pita, R. E. G. (2026). An Indicator of Inconsistency Framework for Detecting Contradictory Digital Artifacts. Forensic Science International: Digital Investigation.
    @article{gunestas2026ioi,
  title = {An Indicator of Inconsistency Framework for Detecting Contradictory Digital Artifacts},
  author = {Gunestas, M. and Kunwar, K. and Pita, R. E. G.},
  journal = {Forensic Science International: Digital Investigation},
  publisher = {Elsevier},
  year = {2026}
}
  2. 2026
    Daniels, M., & Gunestas, M. (2026). Role-Driven Comparative Forensic Analysis of an Android Marketplace Mobile Application. International Conference on Digital Forensics and Cyber Crime (ICDF2C), 357–373.
    @inproceedings{daniels2026android,
  title = {Role-Driven Comparative Forensic Analysis of an Android Marketplace Mobile Application},
  author = {Daniels, M. and Gunestas, M.},
  booktitle = {International Conference on Digital Forensics and Cyber Crime (ICDF2C)},
  pages = {357--373},
  year = {2026}
}
  3. 2025
    Kunwar, K., & Gunestas, M. (2025). An LLM-Driven Iterative Workflow for Ontological Mapping of Digital Forensic Artifacts. International Conference on Digital Forensics and Cyber Crime (ICDF2C), 380–399.
    @inproceedings{kunwar2025llm,
  title = {An LLM-Driven Iterative Workflow for Ontological Mapping of Digital Forensic Artifacts},
  author = {Kunwar, K. and Gunestas, M.},
  booktitle = {International Conference on Digital Forensics and Cyber Crime (ICDF2C)},
  pages = {380--399},
  year = {2025}
}

All publications →

Research Infrastructure

The Lab's vulnerability research, binary analysis, and forensic experimentation are conducted in the University of New Haven's Cyber Range Lab, which provides isolated network and system environments for safe threat research.

Join the Lab

We welcome motivated graduate and undergraduate students interested in vulnerability research, threat analysis, and digital forensics. Get in touch →

Latest news

  • Oct 2026

    Our researcher Prathamesh Anil Walunj's recent finding has been validated and resolved by Apple.

    Read more →
  • Sep 2026

    Indicator of Inconsistency (IoI) Framework published in Forensic Science International: Digital Investigation.

    Read more →
  • July 2026

    Yash Kitey awarded a competitive University Graduate Research Fellowship for 2026–27 for vulnerability identification research.

    Read more →