|Vinay J. Ribeiro is the Shridhar Shukla Chair Associate Professor in Digital Trust in
the Department of Computer Science and
at the Indian Institute
of Technology Bombay and is affiliated with the Trust Lab at IIT Bombay.
Research Interests: Computer and Network Security (blockchain, IoT security, Ransomware), Wireless Networks, Indoor positioning and navigation
Youtube Playlists for Lectures on:
CS765: Introduction to Blockchains, Smart Contracts and Cryptocurrencies (2020)
CS762: Advanced Blockchain Technology (2022)
CS224: Computer Networks (2021)
Off-chain solution for scaling up smart contract computation: We have come up with a novel method to implement computationally intensive smart contracts on blockchains such as those of Ethereum, Bitcoin, Hyperledger etc. NDSS is a top-tier Computer Security Conference.
Paper Details: Sourav Das, Vinay Joseph Ribeiro, Abhijeet Anand, YODA: Enabling computationally intensive contracts on blockchains with Byzantine and Selfish nodes, NDSS 2019 [arXiv link]
(Further work has resulted in a paper at AISTAT 2022)
Speeding up Block validation in Blockchains Our measurements of the Ethereum blocks and transactions show that miners can speed up block validation significantly using cached state.
Paper Details: Nitin Awathare, Sourav Das, Vinay J. Ribeiro, Umesh Bellur, RENOIR: Accelerating Blockchain Validation using State Caching , ICPE 2021
Proactive rebalancing and reactive re-routing for Lightning Network: We present a method to improve transaction success ratio in payment channel networks such as Bitcoin's Lightning Network, by proactively rebalancing bidirectional channels and reactively rerouting payments around channels depleted of currency.
Paper Details: Nitin Awathare, Suraj, Akash, Vinay J. Ribeiro, Umesh Bellur, REBAL : Channel Balancing for Payment Channel Networks , MASCOTS 2021
Regulating cryptocurrencies such as Bitcoin Aditya Ahuja, Vinay J. Ribeiro, Ranjan Pal, How Should We Regulate Cryptocurrencies via Consensus?: A Strategic Framework for Optimal Legal Transaction Throughput , accepted for publication at journal ACM DLT 2022
Reducing Confirmation time by 50% in Bitcoin-like blockchains We introduce "Links" and "Anchors" which add PoW weight to blockchains more frequently than blocks. This helps reduce confirmation time by 50 percent.
Paper Details: Ovia Seshadri, Vinay J. Ribeiro, Aditya Kumar, Securely Boosting Chain Growth and Confirmation Speed in PoW Blockchains , IEEE Blockchain 2021
Paper Details: Ovia Seshadri, Vinay J. Ribeiro, Shadab Zafar, Securely Improving Stability and Performance of PoW Blockchains Using Anchors , COMSNETS 2022
Improving Ethereum computation by 100x without Sharding We present a new method to improve maximum computation of smart contracts in Ethereum-like blockchains by up to 100x.
Paper Details: Sourav Das, Nitin Awathare, Ling Ren, Vinay J. Ribeiro, Umesh Bellur, Tuxedo: Maximizing Smart Contract computation in PoW Blockchains , SIGMETRICS 2022 (paper)
2. IoT Security
Decentralized Attestation for IoT Swarms We have invented a decentralized attestation scheme for device swarms.
Paper Details: Samuel Wedaj, Kolin Paul, Vinay J. Ribeiro, DADS: Decentralised attestation of Device Swarms , ACM Transactions on Privacy and Security 2019.
3. Machine learning for Malware detection
RANSOMWARE Solution: My student, Saiyed Kashif Shaukat, has developed a multi-layered solution, implemented for Microsoft Windows, for a dreaded type of malware called Ransomware. Ransomware encrypts a user's files and asks for a ransom in order to decrypt files. The solution, called RansomWall, uses machine learning in one of its layers. The paper was presented at COMSNETS 2018 in January 2018.
Paper Details: Saiyed Kashif Shaukat, Vinay J. Ribeiro, RansomWall: A Layered Defense System against Cryptographic Ransomware Attacks using Machine Learning, COMSNETS 2018.Download Paper: IEEEXplore OR preprint
How to choose Data and Feature sets well for IoT malware detection using ML:
Paper Details: Misha Mehra, Jay N. Paranjape, Vinay J. Ribeiro Improving ML Detection of IoT Botnets using Comprehensive Data and Feature Sets, COMSNETS 2021 Download Paper: IEEEXplore
Frugal Deep Learning analysis of network traffic
BOND is designed considering the constraints of IoT gateways and betters the F1 score of standard benchmark ML algorithms and State-of-The-Art method - Kitsune, by at least 10 percent. Paper Details:Himanshu Gandhi, Misha Mehra, Vinay J. Ribeiro,BOND: Efficient and Frugal DL Model Co-design for Botnet detection on IoT Gateways, AIML Systems 2021
Personal Blog: Orthomolecular Medicine: the best kept secret of the medical profession