Fig 3
![How smart contracts work [16].](publication/346184233/figure/fig3/AS:961488541978637@1606248140841/How-smart-contracts-work-16.jpg)
Source publication
![Fig. 1. A simplified scenario of DAO attack [11].](publication/346184233/figure/fig1/AS:961488541974528@1606248140524/A-simplified-scenario-of-DAO-attack-11_Q320.jpg)
![Fig. 2. Szabo's example [6].](publication/346184233/figure/fig2/AS:961488541978634@1606248140586/Szabos-example-6_Q320.jpg)
![Fig. 3. How smart contracts work [16].](publication/346184233/figure/fig3/AS:961488541978637@1606248140841/How-smart-contracts-work-16_Q320.jpg)
Contexts in source publication
Context 1
... platform for creating smart contracts [19]. A contract address also includes its own storage (i.e., state data) or an amount of "Ether" balance (i.e., Ethereum cryptocurrency). Moreover, Solidity supports a variety of APIs to implement specific business logic for developers, e.g., transfer money to some address or get the blockchain information. Fig. 3 illustrates a home buying between two people using Ethereum smart contracts. ...
Context 2
... of abstract interpretation. It thus arrives at expressions in terms of those symbols for expressions and variables in the program, and constraints in terms of those symbols for the possible outcomes of each conditional branch. To give a clear example, in SimpleContract, the CFG of "sendeth" function (similar to "sendtoken") corresponding to it in Fig. 3, reads in values and returns Fail if the amountE is greater than etherA. During symbolic execution, symbolic state maps variables to symbolic values (e.g., amountE assigned to α, etherA to β and etherB to θ). When reaching the if statement, α, β could take any value, and symbolic execution can, therefore, proceed along both branches, ...
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Citations
Smart contracts are programs that execute transactions involving independent parties and cryptocurrencies. As programs, smart contracts are susceptible to a wide range of errors and vulnerabilities. Such vulnerabilities can result in significant losses. Furthermore, by design, smart contract transactions are irreversible. This creates a need for methods to ensure the correctness and security of contracts pre-deployment. Recently there has been substantial research into such methods. The sheer volume of this research makes articulating state-of-the-art a substantial undertaking. To address this challenge, we present a systematic review of the literature. A key feature of our presentation is to factor out the relationship between vulnerabilities and methods through properties. Specifically, we enumerate and classify smart contract vulnerabilities and methods by the properties they address. The methods considered include static analysis as well as dynamic analysis methods and machine learning algorithms that analyze smart contracts before deployment. Several patterns about the strengths of different methods emerge through this classification process.
Cyber-attacks are getting more sophisticated and nuanced. Intrusion detection systems (IDSs) are commonly used in a variety of networks to assist in the timely detection of intrusions. In recent years, blockchain technology has got a lot of attention as a way to share data without the need for a trusted third party. In particular, data recorded in a single block cannot be modified without impacting all subsequent blocks. For an effective update, an attacker will need to monitor the majority of network nodes, which is not feasible given the current network size. This work aims to create a deep learning-based IDS model with the potential of integrating blockchain technology with intrusion detection, inspired by the ability to apply blockchain in all fields. The proposed model outperforms the conventional systems with respect to accuracy in detecting the security attacks.
