Illumio Security Research Intern at Illumio investigates real-world cyber threats and attacker behaviors to inform security capabilities. The role includes analyzing threat intelligence and experimenting with ML models for security use cases.
Responsibilities
Research emerging threats, vulnerabilities, attacker campaigns, and evolving adversary techniques.
Analyze threat intelligence including malicious IP addresses, domains, attacker infrastructure, and adversary behaviors.
Investigate network activity associated with command-and-control, suspicious communications, and other potentially malicious behavior.
Analyze security and network telemetry to identify patterns that may distinguish normal from suspicious activity.
Explore machine learning and statistical techniques for security use cases such as anomaly detection, behavioral analysis, clustering, and classification.
Experiment with ML models and detection approaches, evaluating their effectiveness, limitations, and potential false positives.
Help build and curate structured datasets connecting threats, campaigns, indicators, vulnerabilities, and attacker techniques.
Explore how threat intelligence and security telemetry can be combined to develop new detection ideas.
Collaborate with threat researchers, data scientists, engineers, and product teams to communicate findings and potential security improvements.
Present research, experiments, and key learnings at the end of the internship.
Currently enrolled in a full-time Bachelor’s or Master’s degree program in Computer Science, Cybersecurity, Data Science or a related technical field with an expected graduation date in Winter 2027/Spring 2028
Strong interest in cybersecurity, security research, threat intelligence, or machine learning
Qualification
Experience in these areas is helpfulParticipation in CTFs
Preferred
Experience in these areas is helpful, but not required:
Experience working with network or security telemetry, logs, or large datasets
Familiarity with Python data analysis tools such as pandas or NumPy
Basic understanding of machine learning or statistical techniques, such as classification, clustering, or anomaly detection
Experience experimenting with ML libraries such as scikit-learn, PyTorch, or similar frameworks
Interest in applying machine learning to cybersecurity problems such as behavioral analysis or threat detection
Participation in CTFs, security competitions, academic research, open-source projects, or personal security projects
You don’t need to already be an expert in cybersecurity or machine learning. We’re looking for students with strong technical fundamentals, curiosity, and an eagerness to learn across disciplines. Coursework, research projects, CTFs, ML projects, open-source contributions, or independent security research can all demonstrate relevant experience.
