52 research outputs found
Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
With the popularity of cryptocurrencies and the remarkable development of
blockchain technology, decentralized applications emerged as a revolutionary
force for the Internet. Meanwhile, decentralized applications have also
attracted intense attention from the online gambling community, with more and
more decentralized gambling platforms created through the help of smart
contracts. Compared with conventional gambling platforms, decentralized
gambling have transparent rules and a low participation threshold, attracting a
substantial number of gamblers. In order to discover gambling behaviors and
identify the contracts and addresses involved in gambling, we propose a tool
termed ETHGamDet. The tool is able to automatically detect the smart contracts
and addresses involved in gambling by scrutinizing the smart contract code and
address transaction records. Interestingly, we present a novel LightGBM model
with memory components, which possesses the ability to learn from its own
misclassifications. As a side contribution, we construct and release a
large-scale gambling dataset at
https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset to facilitate future
research in this field. Empirically, ETHGamDet achieves a F1-score of 0.72 and
0.89 in address classification and contract classification respectively, and
offers novel and interesting insights
Empirical Review of Smart Contract and DeFi Security: Vulnerability Detection and Automated Repair
Decentralized Finance (DeFi) is emerging as a peer-to-peer financial
ecosystem, enabling participants to trade products on a permissionless
blockchain. Built on blockchain and smart contracts, the DeFi ecosystem has
experienced explosive growth in recent years. Unfortunately, smart contracts
hold a massive amount of value, making them an attractive target for attacks.
So far, attacks against smart contracts and DeFi protocols have resulted in
billions of dollars in financial losses, severely threatening the security of
the entire DeFi ecosystem. Researchers have proposed various security tools for
smart contracts and DeFi protocols as countermeasures. However, a comprehensive
investigation of these efforts is still lacking, leaving a crucial gap in our
understanding of how to enhance the security posture of the smart contract and
DeFi landscape.
To fill the gap, this paper reviews the progress made in the field of smart
contract and DeFi security from the perspective of both vulnerability detection
and automated repair. First, we analyze the DeFi smart contract security issues
and challenges. Specifically, we lucubrate various DeFi attack incidents and
summarize the attacks into six categories. Then, we present an empirical study
of 42 state-of-the-art techniques that can detect smart contract and DeFi
vulnerabilities. In particular, we evaluate the effectiveness of traditional
smart contract bug detection tools in analyzing complex DeFi protocols.
Additionally, we investigate 8 existing automated repair tools for smart
contracts and DeFi protocols, providing insight into their advantages and
disadvantages. To make this work useful for as wide of an audience as possible,
we also identify several open issues and challenges in the DeFi ecosystem that
should be addressed in the future.Comment: This paper is submitted to the journal of Expert Systems with
Applications (ESWA) for revie
A Novel Health Prognosis Method for a Power System Based on a High-Order Hidden Semi-Markov Model
Power system health prognosis is a key process of condition-based maintenance. For the problem of large error in the residual lifetime prognosis of a power system, a novel residual lifetime prognosis model based on a high-order hidden semi-Markov model (HOHSMM) is proposed. First, HOHSMM is developed based on the hidden semi-Markov model (HSMM). An order reduction method and a composite node mechanism of HOHSMM based on permutation are proposed. The health state transition matrix and observation matrix are improved accordingly. The high-order model is transformed into the corresponding first-order model, and more node dependency information is stored in the parameter group to be estimated. Secondly, in order to estimate the parameters and optimize the structure of the proposed model, an intelligent optimization algorithm group is used instead of the expectation–maximization (EM) algorithm. Thus, the simplification of the topology of the high-order model by the intelligent optimization algorithm can be realized. Then, the state duration variables in the high-order model are defined and deduced. The prognosis method based on polynomial fitting is used to predict the residual lifetime of the power system when the prior distribution is unknown. Finally, the intelligent optimization algorithm is used to solve the proposed model, and experiments are performed based on a set of power system data sets to evaluate the performance of the proposed model. Compared with HSMM, the proposed model has better performance on the power system health prognosis problem and can get a relatively good solution in a short computation time
Online Health Management for Complex Nonlinear Systems Based on Hidden Semi-Markov Model Using Sequential Monte Carlo Methods
Health management for a complex nonlinear system is becoming more important for condition-based maintenance and minimizing the related risks and costs over its entire life. However, a complex nonlinear system often operates under dynamically operational and environmental conditions, and it subjects to high levels of uncertainty and unpredictability so that effective methods for online health management are still few now. This paper combines hidden semi-Markov model (HSMM) with sequential Monte Carlo (SMC) methods. HSMM is used to obtain the transition probabilities among health states and health state durations of a complex nonlinear system, while the SMC method is adopted to decrease the computational and space complexity, and describe the probability relationships between multiple health states and monitored observations of a complex nonlinear system. This paper proposes a novel method of multisteps ahead health recognition based on joint probability distribution for health management of a complex nonlinear system. Moreover, a new online health prognostic method is developed. A real case study is used to demonstrate the implementation and potential applications of the proposed methods for online health management of complex nonlinear systems
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