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14.5. Honeypot Systems

Honeypots are decoy systems that attract attackers to attempt to compromise them. Because a honeypot typically has low security inbound but higher security outbound, even novice attackers can compromise them easilynot to mention computer worms, which will be even more excited about them. As a result, the motives and the tactics of the attacker can be learned. I especially enjoy the works of Lance Spitzner, who has spent many years running honeypot systems. Lance was among the first people to recognize the value of honeypot systems against computer worms and other malicious threats, and he is dedicated to sharing his research results.

The concept of the honeypot was introduced in 1990 by Clifford Stoll's "The Cuckoo's Egg" and Bill Cheswick's "An Evening with Berferd." Not surprisingly, it was Fred Cohen who introduced the first publicly available honeypot solution, the Deception Toolkit in 19972.

Spitzner distinguishes between two basic kinds of honeypot systems: low and high interaction. A low-interaction honeypot simply emulates some network services. It might be able to capture some parts of the attack, but because the attack might not have a chance to complete, it might not be captured and understood. On the other hand, high-interaction honeypots might be vulnerable, real systems or a set of vulnerable systems among different operating systems. (In addition, some high-interaction honeypot solutions such as Collapsar8 are implemented with both real and virtual machines, and the attacks against individual honeypots in the system are correlated.) A high-interaction honeypot might get compromised completely, and the attacker might be able to download even more tools to the system, which can consequently be captured. Similarly, when computer worms penetrate a target, they can be captured and sent to an analysis center for automated processing. This will be discussed in more detail in Chapter 15, "Malicious Code Analysis Techniques."

A very simple example of a honeypot can be illustrated with the use of NetCat (NC), which has already been used in various chapters of this book. The following command can capture HTTP traffic on a dedicated system:

NC l p 80 >http.log

This command instructs NetCat to listen on port 80 (HTTP) and redirect the incoming traffic to a log file. Although this is a fairly low-interaction honeypot, it is good enough to capture the CodeRed worm because CodeRed simply sends a GET request to a random target. So if the previous command is executed on a system without a firewall to block incoming traffic, CodeRed will be captured in the http.log file as soon as CodeRed sends itself to the IP address where NC listens. In fact, this is exactly what Ryan Russel did to capture CodeRed quickly and successfully. This method also can be used to capture a worm like Slammer, which uses UDP to hit a vulnerable Microsoft SQL Server without any fingerprinting involved.

Note

Existing literature suggests that Slammer pings its target first, but this is not the case.


The NetCat command would be the following:

NC l p 1434 u >ms-sql.log

To take this one step further, some low-interaction honeypots, such as Back Officer Friendly, are listening on a few ports to capture attacks in a way very similar to the previous NetCat example. Figure 14.2 shows Roger Thomson's Worm Radar, which also uses the listening principle to capture interesting network traffic, match it against known signatures, and build statistics from all the deployed honeypot solutions. Roger captured several worms, including minor variants of CodeRed, which he noticed with the use of exact identification built into the matching engine of Worm Radar. Indeed, it is vital for all honeypot systems to identify already known attacks. Roger's program also tricks worms into revealing their body to Worm Radar. Thus the specific communication needed to capture new variants of the worms is in place.

Figure 14.2. Worm Radar showing the World view of captured attacks.


Another example of a low-interaction system is Honeyd (http://www.honeynet.org) by Niels Provos9. Honeyd can interact with attackers and computer worms a little better than the previously mentioned solutions because it can pretend to be many different systems. Honeyd can capture ARP (Address Resolution Protocol) requests10 that do not belong to any target system and act as if it were the system in question. As a result, a computer worm can have fun with Honeyd and communicate with it, but the services are emulated by Honeyd without any vulnerability (so not all worms can be captured by it completely without some special tricks in place).

Some computer worms, such as Linux/Slapper, are more difficult to capture because the target needs to interact more extensively with the attacker system. Not only does Linux/Slapper fingerprint the target (as explained in Chapter 9, "Strategies of Computer Worms"), it also exploits the target twice (as explained in Chapter 10, "Exploits, Vulnerabilities, and Buffer Overflow Attacks") before it uploads its source code to the target. Such worms need a high-interaction honeypot solution to be captured successfully. Such honeypots are often called research honeypots.

Another interesting solution is LaBrea, a so-called "sticky honeypot" developed by Tom Liston (http://labrea.sourceforge.net). LaBrea can capture ARP requests on your network, very effectively slowing down or stopping computer worms on a network. Unfortunately, LaBrea quickly became a target of the Digital Millennium Copyright Act (DMCA); as a result, Tom Liston pulled its sources in 2003 (see www.hackbusters.net).

Throughout this chapter, the worm captures will have many examples of ARP requests generated by computer worms as they scan for new targets on a network.

Not only can decoy systems be useful to study and protect against computer worms and exploitation, but they also can be a useful technique against all forms of spam. For example, the Brightmail spam detection system utilizes millions of decoy e-mail addresses that are populated to attract spammers using a variety of techniques. E-mail received on decoy accounts are likely to be originated from spammers especially if the same (or similar) e-mail is received on more than one or a very large number of decoy accounts. The system can collect the data from the decoy accounts and use it directly to generate spam filter rules, effectively preventing classified spam from being transmitted at major Internet service providers (ISPs) around the world.

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