Ethereum's high attention,rich business,certain anonymity,and untraceability have attracted a group of attackers.Cybercrime on it has become increasingly rampant,among which scam behavior is convenient,cryptic,ant...Ethereum's high attention,rich business,certain anonymity,and untraceability have attracted a group of attackers.Cybercrime on it has become increasingly rampant,among which scam behavior is convenient,cryptic,antagonistic and resulting in large economic losses.So we consider the scam behavior on Ethereum and investigate it at the node interaction level.Based on the life cycle and risk identification points we found,we propose an automatic detection model named Aparecium.First,a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors.Second,the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors.Conducting experiments in the wild Ethereum datasets,we prove Aparecium is effective which the precision,recall and F1-score achieve at 0.977,0.957 and 0.967 respectively.展开更多
The recent surge of Ethereum in prominence has made it an attractive target for various kinds of crypto crimes.Phishing scams,for example,are an increasingly prevalent cybercrime in which malicious users attempt to st...The recent surge of Ethereum in prominence has made it an attractive target for various kinds of crypto crimes.Phishing scams,for example,are an increasingly prevalent cybercrime in which malicious users attempt to steal funds from a user’s crypto wallet.This research investigates the effects of network architectural features as well as the temporal aspects of user activities on the performance of detecting phishing users on the Ethereum transaction network.We employ traditional machine learning algorithms to evaluate our model on real-world Ethereum transaction data.The experimental results demonstrate that our proposed features identify phishing accounts efficiently and outperform the baseline models by 4%in Recall and 5%in F1-score.展开更多
The“Bitcoin Generator Scam”(BGS)is a cyberattack in which scammers promise to provide victims with free cryptocurrencies in exchange for a small mining fee.In this paper,we present a data-driven system to detect,tra...The“Bitcoin Generator Scam”(BGS)is a cyberattack in which scammers promise to provide victims with free cryptocurrencies in exchange for a small mining fee.In this paper,we present a data-driven system to detect,track,and analyze the BGS.It works as follows:we first formulate search queries related to BGS and use search engines to find potential instances of the scam.We then use a crawler to access these pages and a classifier to differentiate actual scam instances from benign pages.Last,we automatically monitor the BGS instances to extract the cryptocurrency addresses used in the scam.A unique feature of our system is that it proactively searches for and detects the scam pages.Thus,we can find addresses that have not yet received any transactions.Our data collection project spanned 16 months,from November 2019 to February 2021.We uncovered more than 8,000 cryptocurrency addresses directly associated with the scam,hosted on over 1,000 domains.Overall,these addresses have received around 8.7 million USD,with an average of 49.24 USD per transaction.Over 70%of the active addresses that we are capturing are detected before they receive any transactions,that is,before anyone is victimized.We also present some post-processing analysis of the dataset that we have captured to aggregate attacks that can be reasonably confidently linked to the same attacker or group.Our system is one of the first academic feeds to the APWG eCrime Exchange database.It has been actively and automatically feeding the database since November 2020.展开更多
On June 19,2017,the Shenzhen Intermediate People's Court opened its second hearing on Liu Qjanzhen,an unemployed 57-year-old villager from Jiangsu province--or,as his victim Zheng Xueju knew him,the "Qjanlong...On June 19,2017,the Shenzhen Intermediate People's Court opened its second hearing on Liu Qjanzhen,an unemployed 57-year-old villager from Jiangsu province--or,as his victim Zheng Xueju knew him,the "Qjanlong Emperor," the still-surviving inheritor of multiple Qjng family fortunes and sound investment opportunity.展开更多
The binary vectors which respectively contain chimeric genes (SCaM1-GFP, SCaM4-GFP) were constructed and used to transform tobacco (Nicotiana tabacumL.), pGTV-GFP was used as control. The plasmolyzed cells of transgen...The binary vectors which respectively contain chimeric genes (SCaM1-GFP, SCaM4-GFP) were constructed and used to transform tobacco (Nicotiana tabacumL.), pGTV-GFP was used as control. The plasmolyzed cells of transgenic callus treated with CBW were investigated under the laser scanning confocal microscope. Green fluorescence was found in the cell wall of transgenic SCaM1-GFP callus. However there was no green fluorescence in the cell wall of SCaM4-GFP or GFP transgenic callus. These results indicate that SCaM1 can be secreted into the apoplast of plant cells, while SCaM4 does not exist in the apoplast of plant cells.展开更多
基金This research is supported by National Key Research and Development Program of China(No.2021YFF0307203,No.2019QY1300)Youth Innovation Promotion Association CAS(No.2021156)+1 种基金the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDC02040100)National Natural Science Foundation of China(No.61802404)。
文摘Ethereum's high attention,rich business,certain anonymity,and untraceability have attracted a group of attackers.Cybercrime on it has become increasingly rampant,among which scam behavior is convenient,cryptic,antagonistic and resulting in large economic losses.So we consider the scam behavior on Ethereum and investigate it at the node interaction level.Based on the life cycle and risk identification points we found,we propose an automatic detection model named Aparecium.First,a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors.Second,the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors.Conducting experiments in the wild Ethereum datasets,we prove Aparecium is effective which the precision,recall and F1-score achieve at 0.977,0.957 and 0.967 respectively.
基金the project(sanction order no.1/2021-22(GIA))funded by the National Informatics Centre,MeitY,Government of India.
文摘The recent surge of Ethereum in prominence has made it an attractive target for various kinds of crypto crimes.Phishing scams,for example,are an increasingly prevalent cybercrime in which malicious users attempt to steal funds from a user’s crypto wallet.This research investigates the effects of network architectural features as well as the temporal aspects of user activities on the performance of detecting phishing users on the Ethereum transaction network.We employ traditional machine learning algorithms to evaluate our model on real-world Ethereum transaction data.The experimental results demonstrate that our proposed features identify phishing accounts efficiently and outperform the baseline models by 4%in Recall and 5%in F1-score.
基金This work was supported in part by Canada's Natural Sciences and Engineering Research Council(grant number“CRDPJ 539938-19”)and IBM Centre for Advanced Studies(CAS)Canada(grant number“1059”).
文摘The“Bitcoin Generator Scam”(BGS)is a cyberattack in which scammers promise to provide victims with free cryptocurrencies in exchange for a small mining fee.In this paper,we present a data-driven system to detect,track,and analyze the BGS.It works as follows:we first formulate search queries related to BGS and use search engines to find potential instances of the scam.We then use a crawler to access these pages and a classifier to differentiate actual scam instances from benign pages.Last,we automatically monitor the BGS instances to extract the cryptocurrency addresses used in the scam.A unique feature of our system is that it proactively searches for and detects the scam pages.Thus,we can find addresses that have not yet received any transactions.Our data collection project spanned 16 months,from November 2019 to February 2021.We uncovered more than 8,000 cryptocurrency addresses directly associated with the scam,hosted on over 1,000 domains.Overall,these addresses have received around 8.7 million USD,with an average of 49.24 USD per transaction.Over 70%of the active addresses that we are capturing are detected before they receive any transactions,that is,before anyone is victimized.We also present some post-processing analysis of the dataset that we have captured to aggregate attacks that can be reasonably confidently linked to the same attacker or group.Our system is one of the first academic feeds to the APWG eCrime Exchange database.It has been actively and automatically feeding the database since November 2020.
文摘On June 19,2017,the Shenzhen Intermediate People's Court opened its second hearing on Liu Qjanzhen,an unemployed 57-year-old villager from Jiangsu province--or,as his victim Zheng Xueju knew him,the "Qjanlong Emperor," the still-surviving inheritor of multiple Qjng family fortunes and sound investment opportunity.
文摘The binary vectors which respectively contain chimeric genes (SCaM1-GFP, SCaM4-GFP) were constructed and used to transform tobacco (Nicotiana tabacumL.), pGTV-GFP was used as control. The plasmolyzed cells of transgenic callus treated with CBW were investigated under the laser scanning confocal microscope. Green fluorescence was found in the cell wall of transgenic SCaM1-GFP callus. However there was no green fluorescence in the cell wall of SCaM4-GFP or GFP transgenic callus. These results indicate that SCaM1 can be secreted into the apoplast of plant cells, while SCaM4 does not exist in the apoplast of plant cells.
