AI-Assisted Security Engineer | DFIR & Threat Detection | Cloud Security
I'm a cybersecurity graduate student at the University of Maryland, College Park, currently pursuing my Master's in Cybersecurity along with a Graduate Certificate in Cloud Engineering. I'm drawn to hands-on security work understanding how systems break, how attackers think, and how to build defenses that actually hold up in the real world.
Outside of coursework, I enjoy writing technical blogs, contributing to open-source security tools, and participating in CTFs to keep learning by doing. I'm actively looking for cybersecurity full-time opportunities in the DMV area where I can grow through real-world security challenges and collaborative engineering.
Security Researcher Intern
📍 Remote, India
Cyber Security Intern
📍 Virtual, India
Building a system where multiple users/companies share the same infrastructure but data is isolated
Performed forensic analysis on a compromised disk image (VMDK), identifying malicious executables through file system analysis, execution timelines, and user activity artifacts. Conducted malware behavior analysis using static indicators and controlled execution, uncovering HTTP-based outbound communication and embedded attacker messages within URL paths. Correlated host-based artifacts and network traffic to reconstruct a defensible incident timeline, identifying encryption usage to conceal the final malware payload.
Assessed cyber risks for an AI-powered security robot in critical infrastructure. Modeled 10+ attack scenarios using STRIDE, evaluated 50+ vulnerabilities through DREAD and CVSS metrics, and delivered actionable mitigations using a tiered risk matrix.
Developed a Python-based CNN SOC Alert Snapshot Analyzer using AlexNet to classify alert severity from dashboard images. Integrated LLaMA 2 for alert summaries, reducing decision time by 30% and accelerating incident response by 25%.
Developed a real-time malicious user detection model using GANs and Random Forest Classifier. Generated 50,000 synthetic records from 10,000 initial samples, enhancing binary classification accuracy from 78% to 99.4% (21.4% improvement).
📍 College Park, MD, USA
📍 Visakhapatnam, India
Seeking opportunities in AI security, cloud-native security, vulnerability research, and security engineering roles that embed security into production systems. Open to internships, research collaborations, and full-time positions.