honeyblog

A common technique employed by spammers is to send spam mails with the help of botnets. In a typical setting, the spammer uses so called template-based spamming: the attacker sends the bots a spam template that describes the structure of the spam message to be sent. Furthermore, the attacker sends meta-data like recipient list, subject list, and a list of URLs that are used to fill in variables in the template. The bots then construct an email based on the template and the meta-data, and send this email to the targets. As a result, the actual work of handling the SMTP communication is moved from the control server to the bots. Nowadays this technique is used by most large spam botnets, like Waledac, Bobax, Rustock, Cutwail, and a lot of the other major spam botnets as Joe Stewart explained in detail.

Since spammers nowadays use such a tactic, we can also collect spam mails in a more efficient way: Instead of waiting at the end-user's mailboxes or spamtraps for mail messages to arrive and then decide whether or not this is spam, we directly interact with the servers that are used to send spam messages. The basic idea is that we execute spambots, i.e., malicious software dedicated to sending spam emails, in a controlled (honeypot) environment and collect all email messages sent by the bots. This enables us to directly interfere with botnet control servers to collect current spam messages sent by a specific botnet.

We describe this idea in more detail in a short paper that was published at DIMVA'09. The paper is also available on this blog.

Abstract: With increasing security measures in network services, remote exploitation is getting harder. As a result, attackers concentrate on more reliable attack vectors like email: victims are infected using either malicious attachments or links leading to malicious websites. Therefore efficient filtering and blocking methods for spam messages are needed. Unfortunately, most spam filtering solutions proposed so far are reactive, they require a large amount of both ham and spam messages to efficiently generate rules to differentiate between both. In this paper, we introduce a more proactive approach that allows us to directly collect spam message by interacting with the spam botnet controllers. We are able to observe current spam runs and obtain a copy of latest spam messages in a fast and efficient way. Based on the collected information we are able to generate templates that represent a concise summary of a spam run. The collected data can then be used to improve current spam filtering techniques and develop new venues to efficiently filter mails.