collaborators

6 papers

cs.CR2026

Red-Teaming Claude Opus and ChatGPT-based Security Advisors for Trusted Execution Environments

Kunal Mukherjee, Spandan Mukherjee

Trusted Execution Environments (TEEs) (e.g., Intel SGX and ArmTrustZone) aim to protect sensitive computation from a compromised operating system, yet real deployments remain vulne…

cs.LG2026

Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection

Kunal Mukherjee, Zulfikar Alom, Tran Gia Bao Ngo +2

The rise of bot accounts on social media poses significant risks to public discourse. To address this threat, modern bot detectors increasingly rely on Graph Neural Networks (GNNs)…

cs.SI2026

MoltGraph: A Longitudinal Temporal Graph Dataset of Moltbook for Coordinated-Agent Detection

Kunal Mukherjee, Cuneyt Gurcan Akcora, Murat Kantarcioglu

Agent-native social platforms such as Moltbook are rapidly emerging, yet they inherit and amplify classical influence and abuse attacks, where coordinated agents strategically comm…

cs.CR2025

LLM-driven Provenance Forensics for Threat Investigation and Detection

Kunal Mukherjee, Murat Kantarcioglu

We introduce PROVSEEK, an LLM-powered agentic framework for automated provenance-driven forensic analysis and threat intelligence extraction. PROVSEEK employs specialized toolchain…

cs.CR2025

Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features

Kunal Mukherjee, Joshua Wiedemeier, Tianhao Wang +4

Advanced cyber threats (e.g., Fileless Malware and Advanced Persistent Threat (APT)) have driven the adoption of provenance-based security solutions. These solutions employ Machine…

cs.LG2025

PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes

Tianhao Wang, Simon Klancher, Kunal Mukherjee +4

The rise of graph-structured data has driven interest in graph learning and synthetic data generation. While successful in text and image domains, synthetic graph generation remain…