5 papers
TabSHAP
Aryan Chaudhary, Prateek Agarwal, Tejasvi Alladi
Large Language Models (LLMs) fine-tuned on serialized tabular data are emerging as powerful alternatives to traditional tree-based models, particularly for heterogeneous or context…
neuralFOMO: Can LLMs Handle Being Second Best? Measuring Envy-Like Preferences in Multi-Agent Settings
Arnav Ramamoorthy, Shrey Dhorajiya, Ojas Pungalia +6
Envy shapes competitiveness and cooperation in human groups, yet its role in large language model interactions remains largely unexplored. As LLMs increasingly operate in multi-age…
FAST-IDS: A Fast Two-Stage Intrusion Detection System with Hybrid Compression for Real-Time Threat Detection in Connected and Autonomous Vehicles
Devika S, Vishnu Hari, Pratik Narang +2
We have implemented a multi-stage IDS for CAVs that can be deployed to resourec-constrained environments after hybrid model compression.
FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles
Devika S, Vishnu Hari, Pratik Narang +2
This works presents an encoder-only transformer built with minimum layers for intrusion detection in the domain of Connected and Autonomous Vehicles using Federated Learning.
FedLiTeCAN : A Federated Lightweight Transformer for Fast and Robust CAN Bus Intrusion Detection
Devika S, Pratik Narang, Tejasvi Alladi
This work implements a lightweight Transformer model for IDS in the domain of Connected and Autonomous Vehicles