Research and projects

Machine Learning-Based Detection of Forensic Artifacts and Anomalies in Tails OS

This project uses machine learning to find unusual patterns and digital traces in Tails OS, a privacy-focused operating system, to help investigators detect potential misuse or criminal activity.

Objectives

Collected and analyzed digital evidence from Tails OS to identify patterns and unusual activity
Built and tested machine learning models to detect forensic artifacts and anomalies
Prepared clear reports explaining findings and their relevance for digital investigations

Designing Interactive Labs for Quantum Machine Learning Applications in Cybersecurity Threat Detection through Experiential Learning

This project creates hands-on labs that teach students how to use quantum machine learning to detect cybersecurity threats, helping them learn by doing real-world experiments.

Objectives

Designed hands-on lab activities to teach quantum machine learning concepts for cybersecurity threat detection
Developed step-by-step guides and learning materials for students to apply QML in real-world scenarios
Tested and refined lab exercises to ensure clarity, engagement, and practical skill development

Source Code Vulnerability Detection using ChatGPT

This project uses ChatGPT to scan and analyze source code, helping identify security weaknesses so developers can fix them and make their programs safer.

Objectives

Utilized ChatGPT to review and analyze 3+ source code for potential security weaknesses
Created test cases and examples to evaluate code vulnerability detection accuracy
Documented 3+ findings and suggested fixes to improve code security in the work

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