Prospective Students and Research Ideas

Does my research work interest you? Do you want to work with me? Take a look at these overall recommendations and suggestions for research ideas.

Prospective Students

• I have funded positions for Ph.D. students. You don’t need to email me in advance (although you can). You can apply directly via the university website. I may/will invite students who mention me in their applications and/or any other students who have a security background for an interview in the appropriate time.

• I recommend you to email me if you are a special case, like a self-funded Ph.D. student and/or Post-Doc candidate.

• Stay tuned to the admission deadlines at the university website. I cannot admit you in other dates.

• Master students must be enrolled in the program before electing me as dissertation supervisor. If you are already a master student, reach me out for scheduling an interview. If you are a prospective student, enroll in the master program first. Notice that master students are usually self-funded, even though I may pay complementary research hours for students writing their dissertations with me, depending upon our agreement.

• If you are an enrolled undergraduate student and want to have your first research experience, reach me out. I can attest your research experience even if you are a voluntary. It will be my pleasure to recommend you for a graduate program if we have a great research experience.

Research Ideas

I following present a (non-exhaustive) list of research topics/questions that I have been working with and/or would like to work on:

Malware

• Longitudinal analyses of in-the-wild threats.
• Static and Dynamic detection mechanisms.
• Sandbox development.
• Malware analysis frameworks.
• Fuzzing and Symbolic execution of malware samples.

Debuggers

• Development of new debuggers.
• How analysts use debuggers to reverse engineer code?
• Debugger enhancement with AI.

Anti-Viruses (AVs)

• Metrics to evaluate real solutions.
• Design of next-gen solutions.

Artificial Intelligence (AI)/Machine Learning (ML)

• Development of ML models for malware detection.
• Evaluation of ML-based solutions in actual scenarios.
• Adversarial ML attacks against malware detectors.
• Using AI to automatically create new malware samples.

Hardware

• How to move AVs from software to hardware?
• Development of secure-by-design systems.

Theoretical Aspects

• What is a good definition for malware?
• Developing a theory of maliciousness.

Methodological Aspects

• Challenges and Pitfalls in the security field.
• Development of good evaluation metrics.
• Bibliographic analysis of the malware literature.

Operating Systems

• Resilient and Self-healing systems.

Data Science and Analytics

• Similar malware clustering.
• Threat intelligence from malware logs.