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- Chen, Weili, et al.
(författare)
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Detecting Ponzi Schemes on Ethereum : Towards Healthier Blockchain Technology
- 2018
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Ingår i: WWW '18. - New York, New York, USA : ACM Digital Library. - 9781450356398 ; , s. 1409-1418
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Konferensbidrag (refereegranskat)abstract
- Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help dealing with this issue, this paper proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the proposed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.
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| 2. |
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| 3. |
- Dai, Hong-Ning, et al.
(författare)
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Big Data Analytics for Large-scale Wireless Networks : Challenges and Opportunities
- 2019
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Ingår i: ACM Computing Surveys. - : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 52:5
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Tidskriftsartikel (refereegranskat)abstract
- The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.
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| 4. |
- Wang, Qiu, et al.
(författare)
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On Connectivity of Wireless Sensor Networks with Directional Antennas
- 2017
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna models have their pros and cons in the accuracy of reflecting realistic antennas and the computational complexity, we propose a new analytical directional antenna model called the iris model to balance the accuracy against the complexity. We conduct extensive simulations to evaluate the analytical framework. Our results show that our proposed analytical model on the network connectivity is accurate, and our iris antenna model can provide a better approximation to realistic directional antennas than other existing antenna models.
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