Blue to purple gradient

Get in touch!

Ready to discuss cybersecurity solutions or potential collaborations? I'm always interested in connecting with fellow security professionals and exploring new opportunities.

About me

I recently graduated from Drexel University with a Bachelor's degree in Computing and Security Technology, specializing in Security Technology with a minor in Artificial Intelligence. My academic journey has been complemented by hands-on experience in IT support, penetration testing, and cybersecurity policy development.

With a strong foundation in both defensive and offensive security practices, I'm passionate about creating robust security solutions that protect organizations from evolving cyber threats while leveraging AI to enhance security postures.

Experience

IT Support Technician - Drexel University
  • Developed comprehensive information security policies that shaped organizational outlook on employee training and VPN service usage

  • Conducted social engineering tests to evaluate personnel response to cyber and physical threats, ensuring email system security

  • Performed penetration testing on proprietary programs to protect personally identifiable and financial information (PII)

  • Provided comprehensive IT support including telecommunications, networking access, and information security assistance

  • Successfully resolved over 500 support tickets involving student software and hardware issues

Featured Projects

IoT Security Monitoring System

This project simulates a basic IoT security monitoring system that collects device data (e.g., temperature, motion, signal strength) and uses anomaly detection to flag potentially malicious activity or system malfunction. It uses an unsupervised machine learning algorithm (Isolation Forest) to detect anomalies without needing labeled data.

DNS Tunneling detection tool

This project simulates a DNS tunneling detection system using statistical analysis of DNS query logs. DNS tunneling is a covert channel that encodes data within DNS queries. This tool uses a Random Forest Classifier trained on features like query length, entropy, and character frequency to detect suspicious queries.

Ai based Phishing detection

All scripts can be found on my Github Here:

This project trains a machine learning model to detect phishing messages based on text input, such as emails, SMS, or URLs. It uses a Random Forest Classifier and TF-IDF vectorization to build a predictive model from labeled training data.