Trade or Trick?

2021 ◽  
Vol 5 (3) ◽  
pp. 1-26
Author(s):  
Pengcheng Xia ◽  
Haoyu Wang ◽  
Bingyu Gao ◽  
Weihang Su ◽  
Zhou Yu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Security And Privacy ◽  
Smart Contracts ◽  
Early Stages ◽  
Asset Trading ◽  
Digital Asset ◽  
Guilt By Association ◽  
Privacy Issues ◽  
Digital Assets

The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem, and our approach can act as a whistleblower that identifies scam tokens at their early stages.

DistProv-Data Provenance in Distributed Cloud for Secure Transfer of Digital Assets with Ethereum Blockchain using ZKP

2019 ◽  
Vol 10 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Navya Gouru ◽  
NagaLakshmi Vadlamani
Keyword(s):  
Cloud Computing ◽  
Data Provenance ◽  
Smart Contracts ◽  
Provenance Data ◽  
Cryptographic Algorithms ◽  
Digital Asset ◽  
Contemporary Architecture ◽  
Distributed Cloud ◽  
Business Sectors ◽  
Digital Assets

The importance and usage of the distributed cloud is increasing rapidly over a traditionally centralized cloud for the storing and exchanging of digital assets between untrusted parties in many business sectors. Storing the digital assets in the distributed cloud is considered superior to traditional cloud computing in terms of environmentally friendly, cost, security and other technical dimensions. In this article, a contemporary architecture DistProv is proposed where an open source distributed cloud IPFS is used to store and transfer the digital assets between the consignor and consignee. These two are untrusted parties exchanging sensitive documents secured by cryptographic algorithms with permission-based access verified by ethereum smart contracts using zero-knowledge proof (ZKP) and simultaneously publishing the provenance data about the digital asset as a transaction on the blockchain. This article also discusses on verifying the integrity of the digital assets and authentication of the consignor and thus preserving a strong CIA triad.

scholarly journals Mapping the NFT revolution: market trends, trade networks, and visual features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matthieu Nadini ◽  
Laura Alessandretti ◽  
Flavio Di Giacinto ◽  
Mauro Martino ◽  
Luca Maria Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Visual Features ◽  
Smart Contracts ◽  
Public Attention ◽  
Trade Networks ◽  
Market Trends ◽  
Analyse Data ◽  
Digital Assets

AbstractNon Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.

A Comprehensive Review of the Security and Privacy Issues in Blockchain Technologies

2022 ◽  
pp. 180-199
Author(s):  
Mangesh Manikrao Ghonge ◽  
N. Pradeep ◽  
Renjith V. Ravi ◽  
Ramchandra Mangrulkar
Keyword(s):  
Economic Models ◽  
New Age ◽  
Security And Privacy ◽  
Smart Contracts ◽  
Comprehensive Review ◽  
Blockchain Technology ◽  
Application Potential ◽  
The Impact ◽  
Privacy Issues ◽  
Comprehensive Study

The development of blockchain technology relies on a variety of disciplines, including cryptography, mathematics, algorithms, and economic models. All cryptocurrency transactions are recorded on a digital and decentralized public ledger known as the blockchain. Customers may keep track of their crypto-transactions by looking at a chronological list rather than a centralized ledger. The blockchain's application potential is bright, and it has already produced results. In various fields, blockchain technology has been incorporated and deployed, from the earliest days of cryptocurrencies to the present day with new-age smart contracts. No comprehensive study on blockchain security and privacy has yet been done despite numerous studies in this area over the years. In this chapter, the authors talked about blockchain's security and privacy issues as well as the impact they've had on various trends and applications. This chapter covers both of these topics.

scholarly journals A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

2021 ◽  
Vol 7 ◽  
pp. e414
Author(s):  
Shilan S. Hameed ◽  
Wan Haslina Hassan ◽  
Liza Abdul Latiff ◽  
Fahad Ghabban
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Security Requirements ◽  
Security And Privacy ◽  
The Internet ◽  
Learning Approaches ◽  
Systems Quality ◽  
Internet Of Medical Things ◽  
Privacy Issues

Background The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device’s lifespan. Methodology This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

DistProv-Data Provenance in Distributed Cloud for Secure Transfer of Digital Assets with Ethereum Blockchain using ZKP

2020 ◽  
pp. 866-890
Author(s):  
Navya Gouru ◽  
NagaLakshmi Vadlamani
Keyword(s):  
Cloud Computing ◽  
Data Provenance ◽  
Smart Contracts ◽  
Provenance Data ◽  
Cryptographic Algorithms ◽  
Digital Asset ◽  
Contemporary Architecture ◽  
Distributed Cloud ◽  
Business Sectors ◽  
Digital Assets

The importance and usage of the distributed cloud is increasing rapidly over a traditionally centralized cloud for the storing and exchanging of digital assets between untrusted parties in many business sectors. Storing the digital assets in the distributed cloud is considered superior to traditional cloud computing in terms of environmentally friendly, cost, security and other technical dimensions. In this article, a contemporary architecture DistProv is proposed where an open source distributed cloud IPFS is used to store and transfer the digital assets between the consignor and consignee. These two are untrusted parties exchanging sensitive documents secured by cryptographic algorithms with permission-based access verified by ethereum smart contracts using zero-knowledge proof (ZKP) and simultaneously publishing the provenance data about the digital asset as a transaction on the blockchain. This article also discusses on verifying the integrity of the digital assets and authentication of the consignor and thus preserving a strong CIA triad.

scholarly journals Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Linda A. Antonucci ◽  
Alessandra Raio ◽  
Giulio Pergola ◽  
Barbara Gelao ◽  
Marco Papalino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Cognition ◽  
Attachment Style ◽  
Learning Algorithm ◽  
Early Recognition ◽  
Emotion Perception ◽  
First Episode ◽  
Support Vector ◽  
Significant Group ◽  
Early Stages

Abstract Background Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance. Methods Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities. Results The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample (p = 0.03). Conclusion Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

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