Biblio

We investigate what we call the "Bitcoin Generator Scam" (BGS), a simple system in which the scammers promise to "generate" new bitcoins using the ones that were sent to them. A typical offer will suggest that, for a small fee, one could receive within minutes twice the amount of bitcoins submitted. BGS is clearly not a very sophisticated attack. The modus operandi is simply to put up some web page on which to find the address to send the money and wait for the payback. The pages are then indexed by search engines, and ready to find for victims looking for free bitcoins. We describe here a generic system to find and analyze scams such as BGS. We have trained a classifier to detect these pages, and we have a crawler searching for instances using a series of search engines. We then monitor the instances that we find to trace payments and bitcoin addresses that are being used over time. Unlike most bitcoin-based scam monitoring systems, we do not rely on analyzing transactions on the blockchain to find scam instances. Instead, we proactively find these instances through the web pages advertising the scam. Thus our system is able to find addresses with very few transactions, or even none at all. Indeed, over half of the addresses that have eventually received funds were detected before receiving any transactions. The data for this paper was collected over four months, from November 2019 to February 2020. We have found more than 1,300 addresses directly associated with the scam, hosted on over 500 domains. Overall, these addresses have received (at least) over 5 million USD to the scam, with an average of 47.3 USD per transaction.

Bitcoin mixing services improve anonymity by breaking the connection between Bitcoin addresses. In the darkweb environment, many illegal trades, such as in drugs or child pornography, avoid their transactions being traced by exploiting mixing services. Therefore, de-mixing algorithms are needed to identify illegal financial flows and to reduce criminal activity. Unfortunately, to the best of our knowledge, few studies on analyzing mixing services and de-anonymizing transactions have been proposed. In this paper, we conduct an in-depth analysis of real-world mixing services, and propose a de-mixing algorithm for Helix, one of the most widely used Bitcoin mixing services. The proposed algorithm de-anonymizes the relationship between the input and output addresses of mixing services by exploiting the static and dynamic parameters of mixing services. Our experiment showed that, we could identify the relationships between the input and output addresses of the Helix mixing service with a 99.14% accuracy rate.

Bitcoin exchanges rely heavily on traditional intrusion detection system to secure their system. However, this reliance has proven to be high risk, since Bitcoin and other blockchain-based transactions are not easily reversible. Many of the attacks have shown that the traditional intrusion detection system is not enough to safeguard against all possible attacks, and most importantly, in some cases, it takes a long time to assess the damage. In this paper, we first describe three types of intrusion models in Bitcoin exchanges and propose a detection and mitigation system using blockchain analysis for each. The proposed detection and mitigation system exploit the decentralized and public nature of Bitcoin blockchain to complement the existing traditional intrusion detection system as a fail-safe. The proposed method provides real-time intrusion detection capability that the existing work cannot provide. Although the proposed method is specifically for Bitcoin blockchain, similar ideas can be extended to other proof-of-work based blockchain cryptocurrencies.