April 18, 2008 – 5:33 AM
A bot is a computer program installed on a compromised machine which offers an attacker a remote control mechanism. Botnets, i.e., networks of such bots under a common control infrastructure, pose a severe threat to today’s Internet: Botnets are commonly used for Distributed Denial-of-Service (DDoS) attacks, sending of spam, or other nefarious purposes [5, 24, 15].
The common control infrastructure of botnets in the past was based on Internet Relay Chat (IRC): The attacker sets up an IRC server and opens a specific channel in which he posts his commands. Bots connect to this channel and act upon the commands they observe. Today, the standard technique to mitigate IRCbased botnets is called botnet tracking [11, 15, 14] and includes three steps. The first step consists of acquiring and analyzing a copy of a bot. This can be achieved for example using honeypots [1] and special analysis software [4, 32]. In the second step, the botnet is infiltrated by connecting to the IRC channel with a specially crafted IRC client. Using the collected information, it is possible to analyze the means and techniques used within the botnet. More specifically, it is possible to identify the central IRC server which, in the third and final step, can be taken offline by law enforcement or other means [9]. An attacker can also use an HTTP server for distributing commands: in this setup, the bots periodically poll this server for new commands and act upon them. The botnet tracking methodology outlined above can also be applied in this scenario. Today we are encountering a new generation of botnets that use P2P style communication. These botnets do not have a central server that distributes commands and are therefore not directly affected by botnet tracking. Probably the most prominent P2P bot currently spreading in the wild is known as Peacomm, Nuwar, or Zhelatin. Because of its devastating success, this worm received major press coverage [13, 17, 22] in which — due to the circumstances of its spreading — it was given the name Storm Worm (or Storm for short) [30]. This malware is currently the most wide-spread P2P bot observed in the wild.
In this paper we study the question, whether the technique of botnet tracking can be extended to analyze and mitigate P2P based botnets. Roughly speaking, we adapt the three steps of botnet tracking in the following way using Storm Worm as a case study: In the first step, we must get hold of a copy of the bot binary. In the case of this botnet, we use spam traps to collect Storm Worm generated spam and client side honeypots to simulate the infection process. The second step, the infiltration of the botnet, is adopted since we need to use a P2P protocol instead of IRC, HTTP, or other client/server protocols. The third step, the actual mitigation, is the most difficult: In the case of Storm Worm we exploit weaknesses in the protocol used by the bot to inject our own content into the botnet, in an effort to disrupt the communication between the bots. We argue later that this method is effective against P2P botnets using content-based publish/subscribe-style communication.
Our measurements show that our strategy can be used as a way to disable the communication within the Storm botnet to a large extent. As a side effect, we are able to estimate the size of the Storm botnet, in general a hard task [25]. Our measurements are much more precise than previous measurements [12, 17]. This is because measurements previously were based on passive techniques, e.g., by observing visible network events like the number of spam mails supposedly sent via the bots. We are the first to introduce an active measurement technique to actually enumerate the number of infected machines: We crawl the P2P network, keep track of all peers, and distinguish an infected peer from a regular one based on characteristic behavior of the bots.
Read the full case study here…
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