Nikhil Kumar

The paper proposes an automated, LLM-enabled threat hunting framework integrated with Splunk to help SOC analysts autonomously monitor evolving threats and prioritize suspicious network traffic.

The paper introduces RoboKA, a KAN-based multimodal fusion framework, and a synthetic dataset (Robo-SAr) to significantly improve robocall surveillance by modeling complex adversarial interactions between acoustic and linguistic cues.

This paper develops an explainable and deployable machine learning system for highly accurate phishing detection across diverse, heterogeneous datasets, achieving up to 99.78% accuracy using transformer models.