Suvin Majithia

Developed threat detection pipelines in Imperva DSF Hub using UEBA models, enabling proactive identification of anomalous database activities and early threat detection. Conducted data analysis using Imperva DSF dashboards, extracting actionable insights that enhanced real-time monitoring and expedited incident response.

Spearheaded 5 applied ML projects using TensorFlow and Keras, spanning recruitment, document automation, and e-commerce user feedback, with datasets ranging from 5 K- 10 K+ records. Developed a semantic similarity engine using BERT and Doc2Vec to match 5,000+ candidate profiles with job roles, improving accuracy by 20% through cosine similarity and weighted scoring.

Monitored network activity using Splunk and Azure Monitor, escalating 89 high-severity threats and automating detection workflows to reduce incident response time by 91%. Analyzed IOCs and log patterns in Splunk to uncover lateral movement and privilege escalation, applying Zero Trust principles (NIST, ISO 2700x) to improve detection accuracy and reduce attack surface by 15%.

Activities and societies: Coursework: Network Security, Intrusion Detection and Prevention, Cryptography, Incident Response

Completed a Master of Science in Cybersecurity, focusing on network security, intrusion detection, and incident response. Developed an ML-based DDoS detection system for the capstone project, achieving 91% accuracy.

Obtained a Bachelor of Engineering in Information Technology, gaining foundational knowledge in various aspects of information technology.