Name
Abstract | ||
|---|---|---|
As of 2014, a fifth of EU citizens relied on cloud accounts to store their documents according to a Eurostat report. Although useful, there are downsides to the use of cloud documents. They often accumulate sensitive information over time, including financial information. This makes them attractive targets to cybercriminals. To understand what happens to compromised cloud documents that contain financial information, we set up 100 fake payroll sheets comprising 1000 fake records of fictional individuals. We populated the sheets with traditional bank payment information, cryptocurrency details, and payment URLs. To lure cybercriminals and other visitors into visiting the sheets, we leaked links pointing to the sheets via paste sites. We collected data from the sheets for a month, during which we observed 235 accesses across 98 sheets. Two sheets were not opened. We also recorded 38 modifications in 7 sheets. We present detailed measurements and analysis of accesses, modifications, edits, and devices that visited payment URLs in the sheets. Contrary to our expectations, bank payment URLs received many more clicks than cryptocurrency payment URLs despite the popularity of cryptocurrencies and emerging blockchain technologies. On the other hand, sheets that contained cryptocurrency details recorded more modifications than sheets that contained traditional banking information. In summary, we present a comprehensive picture of what happens to compromised cloud spreadsheets. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EuroSPW.2019.00053 | 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) |
Keywords | Field | DocType |
honeypot,information-theft,cloud-document,financial | Honeypot,World Wide Web,Computer science,Popularity,Computer network,Blockchain,Information sensitivity,Cryptocurrency,Payroll,Payment,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-7281-3027-9 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremiah Onaolapo | 1 | 44 | 5.41 |
| Martin Lazarov | 2 | 3 | 0.74 |
| Gianluca Stringhini | 3 | 701 | 61.87 |