A Blockchain-Based Data-Sharing Framework for Cloud Based Internet of Things Systems with Efficient Smart Contracts

Ramana, Kadiyala; Mohana, R.Madana; Kumar Reddy, C. Kishor; Srivastava, Gautam; Gadekallu, Thippa Reddy

doi:10.1109/iccworkshops57953.2023.10283747

2023 IEEE International Conference on Communications Workshops (ICC Workshops) 2023

DOI: 10.1109/iccworkshops57953.2023.10283747

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A Blockchain-Based Data-Sharing Framework for Cloud Based Internet of Things Systems with Efficient Smart Contracts

Kadiyala Ramana,

R.Madana Mohana,

C. Kishor Kumar Reddy

et al.

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2024

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An Integrated Smart Contract Vulnerability Detection Tool Using Multi-Layer Perceptron on Real-Time Solidity Smart Contracts

Colin,

Mohan,

Pan

et al. 2024

IEEE Access

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Smart contract vulnerabilities have led to substantial disruptions, ranging from the DAO attack to the recent Poolz Finance. While initially, the smart contract vulnerability definition lacked standardization, even with the advancements in Solidity, the potential for deploying malicious contracts to exploit legitimate ones persists. The Abstract syntax tree (AST), opcodes, and control flow graph (CFG) are the intermediate representations for Solidity contracts. In this paper, we propose an integrated and efficient smart contract vulnerability detection algorithm based on Multi-layer perceptron (MLP). We use feature vectors from the Opcodes and CFG for the machine learning (ML) model training. The existing ML-based approaches for analyzing the smart contract code are constrained by the vulnerability detection space, significantly varying Solidity versions, and no unified approach to verify against the ground truth. The primary contributions in this paper are (i) a standardized pre-processing method for smart contract training data, (ii) introducing bugs to create a balanced dataset of flawed files across Solidity versions using AST, and (iii) standardizing vulnerability identification using the Smart Contract Weakness Classification (SWC) registry. The ML models employed for benchmarking the proposed MLP, and a multi-input model combining MLP and Long short-term memory (LSTM) in our study are Random forest (RF), XGBoost (XGB), Support vector machine (SVM). The performance evaluation on real-time smart contracts deployed on the Ethereum Blockchain show an accuracy of up to 91% using MLP with the lowest average False Positive Rate (FPR) among all tools and models, measuring at 0.0125.

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An Integrated Smart Contract Vulnerability Detection Tool Using Multi-Layer Perceptron on Real-Time Solidity Smart Contracts

Colin,

Mohan,

Pan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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A Blockchain-Based Data-Sharing Framework for Cloud Based Internet of Things Systems with Efficient Smart Contracts

Cited by 1 publication

References 21 publications

An Integrated Smart Contract Vulnerability Detection Tool Using Multi-Layer Perceptron on Real-Time Solidity Smart Contracts

An Integrated Smart Contract Vulnerability Detection Tool Using Multi-Layer Perceptron on Real-Time Solidity Smart Contracts

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