Attack and Defence of Ethereum Remote APIs

Wang, Xu; Zha, Xuan F.; Yu, Guangsheng; Ni, Wei; Liu, Ren Ping; Guo, Y. Jay; Niu, Xinxin; Zheng, Kan

doi:10.1109/glocomw.2018.8644498

Cited by 27 publications

(22 citation statements)

References 2 publications

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“…The public blockchain takes in charge of low-frequent functions, e.g., IoT blockchain initialization and IoT blockchain anchoring, by running smart contracts. As a public popular blockchain platform, Ethereum can be a good choice for the public blockchain, which has been widely adopted in researches [6,16].…”

Section: Overviewmentioning

confidence: 99%

“…All the nodes, including IoT blockchain miners and IoT devices, have their unique public/private keys and can be identified by their corresponding public keys (or the equivalent blockchain addresses). The private keys, with the assumption of absolute security, are the foundation of blockchain security and prevent identity forging attacks [16]. Note that, miners of an IoT blockchain use the same pairs of public/private keys across the public blockchain and IoT blockchain.…”

Section: Overviewmentioning

confidence: 99%

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A High-Performance Hybrid Blockchain System for Traceable IoT Applications

Wang

et al. 2019

Lecture Notes in Computer Science

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Blockchain, as an immutable distributed ledger, can be the key to realize secure and trustworthy IoT applications. However, existing blockchains can hardly achieve high-performance and high-security for large-scale IoT applications simultaneously. In this paper, we propose a hyper blockchain architecture combining the security of public blockchains with the efficiency of private blockchains. An IoT anchoring smart contract is proposed to anchor private IoT blockchains into a public blockchain. An IoT device management smart contract is also designed to trace sensory data. A comprehensive analysis reveals that the proposed hybrid blockchain system can achieve the performance of private blockchains and resist tampering.

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Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

A High-Performance Hybrid Blockchain System for Traceable IoT Applications

Wang

et al. 2019

Lecture Notes in Computer Science

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“…Compared with Bitcoin, Ethereum proposed a novel technology named smart contract. A smart contract is the core of Ethereum, which enables developers to program their own applications in an immune and low-cost manner on the basis of blockchain structure [4]. ey are automatically enforced by the consensus mechanism of the blockchain without relying on a trusted authority [4].…”

Section: Introductionmentioning

confidence: 99%

“…A smart contract is the core of Ethereum, which enables developers to program their own applications in an immune and low-cost manner on the basis of blockchain structure [4]. ey are automatically enforced by the consensus mechanism of the blockchain without relying on a trusted authority [4]. Once the smart contract was developed on Ethereum, it could not be changed.…”

Section: Introductionmentioning

confidence: 99%

A Novel Machine Learning-Based Analysis Model for Smart Contract Vulnerability

You

et al. 2021

Security and Communication Networks

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In recent years, a lot of vulnerabilities of smart contracts have been found. Hackers used these vulnerabilities to attack the corresponding contracts developed in the blockchain system such as Ethereum, and it has caused lots of economic losses. Therefore, it is very important to find out the potential problems of the smart contracts and develop more secure smart contracts. As blockchain security events have raised more important issues, more and more smart contract security analysis methods have been developed. Most of these methods are based on traditional static analysis or dynamic analysis methods. There are only a few methods that use emerging technologies, such as machine learning. Some models that use machine learning to detect smart contract vulnerabilities cost much time in extracting features manually. In this paper, we introduce a novel machine learning-based analysis model by introducing the shared child nodes for smart contract vulnerabilities. We build the Abstract-Syntax-Tree (AST) for smart contracts with some vulnerabilities from two data sets including SmartBugs and SolidiFI-benchmark. Then, we build the Abstract-Syntax-Tree (AST) of the labeled smart contract for data sets named Smartbugs-wilds. Next, we get the shared child nodes from both of the ASTs to obtain the structural similarity, and then, we construct a feature vector composed of the values that measure structural similarity automatically to build our machine learning model. Finally, we get a KNN model that can predict eight types of vulnerabilities including Re-entrancy, Arithmetic, Access Control, Denial of Service, Unchecked Low Level Calls, Bad Randomness, Front Running, and Denial of Service. The accuracy, recall, and precision of our KNN model are all higher than 90%. In addition, compared with some other analysis tools including Oyente and SmartCheck, our model has higher accuracy. In addition, we spent less time for training .

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“…Earlier epidemic models omit topologies by assuming a network topology of a complete graph [5]. Network topologies, playing a key role in cyber security [6], are later considered and proved to have a strong impact on the epidemic propagation process [7].…”

Section: Introductionmentioning

confidence: 99%

Group-Based Susceptible-Infectious-Susceptible Model in Large-Scale Directed Networks

Wang

Song

et al. 2019

Security and Communication Networks

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Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.

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Attack and Defence of Ethereum Remote APIs

Cited by 27 publications

References 2 publications

A High-Performance Hybrid Blockchain System for Traceable IoT Applications

A High-Performance Hybrid Blockchain System for Traceable IoT Applications

A Novel Machine Learning-Based Analysis Model for Smart Contract Vulnerability

Group-Based Susceptible-Infectious-Susceptible Model in Large-Scale Directed Networks

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