Work, Research &
Projects
Research Papers
- • AI Application in Cybersecurity: his research explores the integration of artificial intelligence in cybersecurity, specifically focusing on AI’s ability to enhance threat detection and mitigation processes. The paper examines various AI-driven techniques that can identify complex attack patterns, automate threat response, and adapt to emerging cyber threats.
- • APT Detection with Ensemble Learning:This research introduces a machine learning ensemble approach for detecting Advanced Persistent Threats (APTs), which are sophisticated and targeted cyber attacks. The study demonstrates how combining multiple machine learning models can improve detection accuracy and reduce false positives, thereby enhancing the defense against high-level, persistent threats.
Key Projects
ML-Powered Network Intrusion Detection System
- • 98% threat detection accuracy
- • Real-time packet analysis using LIGHTGBM
- • Processed over 1M network packets
AI-Driven Insider Threat Detection System
- • Behavioral analysis with Random Forest (F1-score: 0.92)
- • Identified behavioral indicators
- • Automated alert system for anomalies
Roles
- • Secretary, Yenepoya IT Club: Leading OWASP Yenepoya student chapter and managing GitHub Students Community projects. Delivered courses on ML integration with cybersecurity (August 2024 – Present).
- • Cybersecurity Intern, TUTLER: Conducted research on zero-trust architecture and AI-driven threat intelligence. Developed automated threat classification systems (Dec 2023 – Mar 2024).