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Param Desai

Research

Areas of inquiry, open questions, and ongoing investigations.

7 Publications
5 Active Projects
5 Research Areas

Cybersecurity

Intrusion DetectionFDI AttacksV2X SecurityBotnets

Focus on securing IoT gateway architectures and connected autonomous vehicle sensor streams against false data injection, botnets, and eavesdropping.

4 publications • 2 projects

Explainable AI

SHAPLIMEInterpretabilityModel Auditing

Developing interpretability methods to make ML models trustworthy in high-stakes applications. Work spans SHAP, LIME, and attention-based explanations for adversarial and IoT threat detection scenarios.

4 publications • 2 projects

Machine Unlearning

Right-to-be-ForgottenFisher InformationModel ScrubbingPrivacy

Investigating algorithms that enable deep learning networks to selectively forget training samples on demand, complying with right-to-be-forgotten privacy mandates without full model retraining.

2 publications • 1 projects

NLP & LLMs

RAGLLM AgentsNL-to-SQLWhisper Models

Engineering Retrieval-Augmented Generation (RAG) and conversational agents, with focus on securing natural language to SQL translators and voice-assisted workflows.

0 publications • 3 projects

Quantum Security

CV-QKDQuantum CryptographyKey DistributionV2X Security

Applying quantum cryptographic primitives like Continuous-Variable Quantum Key Distribution (CV-QKD) to secure resource-constrained IoT nodes and vehicular networks.

4 publications • 1 projects

Research Journey

My research began in early 2025 under the supervision of Prof. Sudeep Tanwar and Prof. Rajesh Gupta at Sudeep Tanwar's Research Group, initially exploring how explainability tools like LIME could make intrusion detection systems legible. What started as a question about smart-home security quickly expanded: if you could explain a prediction, could you also diagnose a poisoned training set? That forensic inversion became a thread connecting papers across cybersecurity, autonomous vehicles, and healthcare AI. At IEEE ICSC in Tampa, presenting in person to a room of practitioners taught me something no peer review ever had — that correctness and deployability are different standards. I came back asking the deployment engineer's questions about every paper I write. This led to exploring quantum security primitives like Continuous-Variable Quantum Key Distribution (CV-QKD) to secure resource-constrained edge nodes, moving beyond computational assumptions to physical-law security guarantees. The unlearning verification problem and quantum-secured telemetry streams are where that thinking has landed for now. Seven papers in eighteen months, and the questions are still getting harder.

Publication Timeline

2025 #1
Springer CML 2025

Explainable AI and Quantum Security for Smart Homes Network Attack Classification

2025 #2
IEEE ICSC 2025

Quantum-Assisted XAI-Driven DL Framework for FDI Detection in Connected Autonomous Electric Vehicles Underlying 6G

2025 #3
IEEE VTC Spring 2025

Q-ShielD: Quantum-Enhanced Secure Framework for Autonomous Vehicles Communication

2025 #4
IEEE HealthCom 2025

Quantum-based Edge Intelligence Framework for Wearable Health IoT Device Networks

2025 #5
2025 IEEE Globecom Workshops (GC Wkshps)

Q-PhishNet: Quantum-Secured Explainable Machine Unlearning for Phishing Detection in IoT Networks

2026 #6
AI2M4RI 2026

Machine Unlearning-based Privacy-First Medical Imaging Framework for TB Detection

2026 #7
CITS 2026

FIDES: Federated Intelligence and Detection with Quantum Security for Financial Institutions