My
previous post already contains some information on our recent work, but I think it makes sense to include some more details. We wanted to study an attack class we call
impersonation attacks, i.e., all attacks in which an attacker wants to steal a credential from a victim in order to impersonate as the victim at a provider:
This kind of attacks is quite common, for example also phishing attacks fall under this class: In such an attack, the attacker uses phishing e-mails as an
attack channel and lures the victim into revealing his credentials at a bogus site. These credentials are then sent to the attacker using the
harvesting channel, which can for example be e-mail. The attacker can then use the stolen credentials to impersonate as the victim, for example at an online bank.
We studied a specified type of impersonation attacks, namely the attacks in which keyloggers and banking trojans are used by the attacker. Example of such malware include
ZeuS/Wsnpoem and
Limbo/Nethell, which we studied in detail. Based on the information collected during dynamic analysis, we found many dropzones and got access to many logfiles. We performed a statistical analysis of this data and here are some highlights:
- We found a total of 175 different countries in which the 170,000 victims are located and almost one third of the infected machines are located in either Russia or the United States.
- We also found that the dropzones are located in many different Autonomous Systems (68 different AS in total), but several AS host a larger percentage of ZeuS dropzones: The three most common AS host 49% of all dropzones, indicating that there are some providers preferred by the attackers. Presumably those providers offer bullet-proof hosting, i.e., takedown requests are not handled properly by these providers.
- In total, we found 10,775 unique bank account credentials in all logfiles. This includes passwords and all bank account details as entered by a victim during a normal transaction. Furthermore, we found more than 5,600 full credit card details and tens of thousands of passwords for different sites.
- The distribution of victim IP addresses is highly non-uniform: The majority of victims are located in the IP address ranges between 58.* – 92.* and 189.* – 220.*.
- The results of analyzing the potential income of an attacker indicate that an attacker can earn several hundred dollars per day based on impersonation attacks with keyloggers – a seemingly lucrative business.
Full details are available in the
technical report. Note that the data we collected during this study is very sensitive. We thus handed over this data to
AusCERT, the national Computer Emergency Response Team (CERT) for Australia, since they are in a position to notify the victims.
Update: I received a few comments regarding how to protect against this threat. Best way for protection is patching and not clicking all links and attachments. Furthermore, you can protect yourself against keyloggers by using two-factor authentification when doing bank transactions. German banks offer services such as
mobile TAN/SMS-TAN in which a transaction number is sent to the mobile phone to authorize a transaction. A weaker system is
iTAN (
indexed TAN). The Postbank also published some guidance on
how to protect yourself. If you follow these guidelines, you should be relatively secure and not affected by banking trojans.
