A Market in Dream: the Rapid Development of Anonymous Cybercrime In this paper we have conducted a comprehensive measurement and analysis on the Dream market, an anonymous online market that uses cryptocurrency as transaction currency. We first collect data between October 30th 2018 and March 1st 2019. Then we use decision treebased approach to classify goods. Following we analyze the category of goods sold in the market, the shipping place of vendors. By analyzing more than 1,970,303 items, we find the goods sold in Dream Market are mainly drugs and digital goods. We estimate the total sales of all vendors, and find that an average monthly income is 14 million during the measurement period, which means that the market commission income is more than 560,000 per month. Based on these data, we use transaction cost theory to analyze the transaction attributes of illegal transactions, which shows that anonymous online market can reduce transaction cost of illegal transactions. We finally discuss the results analyzed and the intervention policy, as well as recent DDoS attacks and future trends of illegal transactions in anonymous online market.

Cryptocurrency Pump and Dump Schemes: Quantification and Detection In the past years, cryptocurrencies have received a lot of attention in popular media. Having attracted significant speculation, prices have soared in 2017, fell in 2018 and are generally known to be very volatile. However, some of the price changes have been due to organized manipulation. Traditionally known in the world of penny stocks and made illegal in most countries, pump and dump schemes are frequent in cryptocurrencies, and mostly unregulated. In this paper, we perform quantification and detection of pump and dump schemes that are coordinated through Telegram chats and executed on Binance xe2x80x93 one of the most popular cryptocurrency exchanges. We detail how pumps are organized on Telegram, and quantify the properties of 149 confirmed events with respect to market capitalization, trading volume, price impact and profitability. Based on this ground truth, and regular trading intervals obtained from twitter timestamps, we optimize a binary classifier in order to be able to detect additional suspicious trading activity. Our results indicate that pump and dump schemes occur frequently in cryptocurrencies with market capitalizations below 50 million, that scheme operators often organize their actions across multiple channels, that such activity tends to lead to inflated prices over longer time periods and machine learning can help to identify activity that is similar to known pump and dump schemes.