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Graduate Research Intern – Cybersecurity (AI-based IDS)
Innovating Tomorrow, Today
Cybersecurity graduate student (M.S., May 2026) with hands-on experience in SOC operations, threat detection, AI-based intrusion detection, and digital forensics. Proven ability to analyze large-scale security data, identify vulnerabilities, and transform raw logs into actionable threat intelligence. Seeking Cybersecurity Analyst / SOC Analyst roles and Forensic Analyst roles.
Creative & Professional
Developed AI-based Intrusion Detection Systems (IDS) for threat and anomaly detection in network environments
Trained and evaluated models on large-scale intrusion datasets using TensorFlow, Keras, and Scikit-learn
Improved detection performance through data analysis and model optimization
Produced technical reports outlining findings, risks, and security recommendations
Conducted cybersecurity research focused on vulnerability analysis and secure system design
Built structured datasets from software test cases to support security analysis and verification
Applied AI and formal methods to improve reliability of software systems
Delivered technical reports with findings, risks, and mitigation strategies
Managed digital systems and platforms ensuring secure handling of data and user access
Coordinated workflows across multiple platforms (Discord, YouTube, Twitch)
Improved operational efficiency through structured content and system management
Promoted secure computing practices and responsible data usage
Supported documentation and workflow organization
Analyzed 50,000+ log records in a simulated SOC environment. Detected brute-force attempts, suspicious logins, and abnormal traffic patterns. Built Kibana dashboards to visualize threats and system behavior. Converted raw logs into actionable threat intelligence.
Developed machine learning models to detect network intrusions and anomalies. Trained models using real-world datasets and optimized performance. Reduced false positives through data preprocessing and tuning.
Conducted static & dynamic analysis of IoT firmware. Identified vulnerabilities including command injection (CWE-78), weak authentication, and outdated kernel exploits. Analyzed network traffic for insecure communication.
Developed a SCADA-based system using Raspberry Pi for real-time monitoring. Simulated industrial control system (ICS) vulnerabilities. Demonstrated potential attack vectors and system risks.
Analyzed 389+ data breaches to identify attack trends and high-risk industries. Built regression models to assess breach severity. Generated insights for cybersecurity risk assessment.
University at Albany (SUNY)
GPA: 3.6
Navrachana University, India