Editorial for the Special Issue on “Bayesian Networks: Inference Algorithms, Applications, and Software Tools”

Ethereum is one of the popular Blockchain platform. The key component in the Ethereum Blockchain is the smart contract. Smart contracts (SC) are like normal computer programs which are written mostly in solidity high-level object-oriented programming language. Smart contracts allow completing transactions directly between two parties in the network without any middle man or mediator. Modification of the smart contracts are not possible once deployed into the Blockchain. Thus smart contract has to be vulnerable free before deploying into the Blockchain. In this paper, Bayesian Network Model was designed and constructed based on Bayesian learning concept to detect smart contract security vulnerabilities which are Reentrancy, Tx.origin and DOS. The results showed that the proposed BNMC (Bayesian Network Model Construction) design is able to detect the severity of each vulnerability and also suggest the reasons for the vulnerability. The accuracy of the proposed BNMC results are improved (accuracy 8% increased for both Reentracy and Tx.origin, 6% increased for DOS), compared with traditional method LSTM. This proposed BNMS design and implementation is the first attempt to detect smart contract vulnerabilities using Bayesian Networks.

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Editorial for the Special Issue on “Bayesian Networks: Inference Algorithms, Applications, and Software Tools”

Abstract: In the field of Artificial Intelligence, Bayesian Networks (BN) [...]

Cited by 2 publications

References 7 publications

Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems

Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems

Bayesian network based vulnerability detection of blockchain smart contracts

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