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In computer terminology, a honeypot is a computer security mechanism set to detect, deflect, or, in some manner, counteract attempts at unauthorized use of information systems. Generally, a honeypot consists of data (for example, in a network site) that appears to be a legitimate part of the site, but is actually isolated and monitored, and that seems to contain information or a resource of value to attackers, who are then blocked. This is similar to the police baiting a criminal.
Honeypots can be classified based on their deployment (use/action) and based on their level of involvement. Based on deployment, honeypots may be classified as
- production honeypots
- research honeypots
Production honeypots are easy to use, capture only limited information, and are used primarily by corporations. Production honeypots are placed inside the production network with other production servers by an organization to improve their overall state of security. Normally, production honeypots are low-interaction honeypots, which are easier to deploy. They give less information about the attacks or attackers than research honeypots.
Research honeypots are run to gather information about the motives and tactics of the black hat community targeting different networks. These honeypots do not add direct value to a specific organization; instead, they are used to research the threats that organizations face and to learn how to better protect against those threats. Research honeypots are complex to deploy and maintain, capture extensive information, and are used primarily by research, military, or government organizations.
Based on design criteria, honeypots can be classified as:
- pure honeypots
- high-interaction honeypots
- low-interaction honeypots
Pure honeypots are full-fledged production systems. The activities of the attacker are monitored by using a casual tap that has been installed on the honeypot's link to the network. No other software needs to be installed. Even though a pure honeypot is useful, stealthiness of the defense mechanisms can be ensured by a more controlled mechanism.
High-interaction honeypots imitate the activities of the production systems that host a variety of services and, therefore, an attacker may be allowed a lot of services to waste his time. By employing virtual machines, multiple honeypots can be hosted on a single physical machine. Therefore, even if the honeypot is compromised, it can be restored more quickly. In general, high-interaction honeypots provide more security by being difficult to detect, but they are expensive to maintain. If virtual machines are not available, one physical computer must be maintained for each honeypot, which can be exorbitantly expensive. Example: Honeynet.
Low-interaction honeypots simulate only the services frequently requested by attackers. Since they consume relatively few resources, multiple virtual machines can easily be hosted on one physical system, the virtual systems have a short response time, and less code is required, reducing the complexity of the virtual system's security. Example: Honeyd.
Recently, a new market segment called deception technology has emerged using basic honeypot technology with the addition of advanced automation for scale. Deception Technology addresses the automated deployment of honeypot resources over a large commercial enterprise or government institution.
Malware honeypots are used to detect malware by exploiting the known replication and attack vectors of malware. Replication vectors such as USB flash drives can easily be verified for evidence of modifications, either through manual means or utilizing special-purpose honeypots that emulate drives. Malware increasingly is used to search for and steal cryptocurrencies, which provides opportunities for services such as Bitcoin Vigil to create and monitor honeypots by using small amount of money to provide early warning alerts of malware infection.
Spammers abuse vulnerable resources such as open mail relays and open proxies. Some system administrators have created honeypot programs that masquerade as these abusable resources to discover spammer activity. There are several capabilities such honeypots provide to these administrators and the existence of such fake abusable systems makes abuse more difficult or risky. Honeypots can be a powerful countermeasure to abuse from those who rely on very high volume abuse (e.g., spammers).
These honeypots can reveal the apparent IP address of the abuse and provide bulk spam capture (which enables operators to determine spammers' URLs and response mechanisms). For open relay honeypots, it is possible to determine the e-mail addresses ("dropboxes") spammers use as targets for their test messages, which are the tool they use to detect open relays. It is then simple to deceive the spammer: transmit any illicit relay e-mail received addressed to that dropbox e-mail address. That tells the spammer the honeypot is a genuine abusable open relay, and they often respond by sending large quantities of relay spam to that honeypot, which stops it. The apparent source may be another abused system—spammers and other abusers may use a chain of abused systems to make detection of the original starting point of the abuse traffic difficult.
This in itself is indicative of the power of honeypots as anti-spam tools. In the early days of anti-spam honeypots, spammers, with little concern for hiding their location, felt safe testing for vulnerabilities and sending spam directly from their own systems. Honeypots made the abuse riskier and more difficult.
Spam still flows through open relays, but the volume is much smaller than in 2001 to 2002. While most spam originates in the U.S., spammers hop through open relays across political boundaries to mask their origin. Honeypot operators may use intercepted relay tests to recognize and thwart attempts to relay spam through their honeypots. "Thwart" may mean "accept the relay spam but decline to deliver it." Honeypot operators may discover other details concerning the spam and the spammer by examining the captured spam messages.
Open relay honeypots include Jackpot, written in Java by Jack Cleaver; smtpot.py, written in Python by Karl A. Krueger; and spamhole (honeypot)|spamhole, written in C. The Bubblegum Proxypot is an open source honeypot (or "proxypot").
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An email address that is not used for any other purpose than to receive spam can also be considered a spam honeypot. Compared with the term "spamtrap", the term "honeypot" might be more suitable for systems and techniques that are used to detect or counterattacks and probes. With a spamtrap, spam arrives at its destination "legitimately"—exactly as non-spam email would arrive.
An amalgam of these techniques is Project Honey Pot, a distributed, open source project that uses honeypot pages installed on websites around the world. These honeypot pages disseminate uniquely tagged spamtrap email addresses and spammers can then be tracked—the corresponding spam mail is subsequently sent to these spamtrap e-mail addresses.
Databases often get attacked by intruders using SQL Injection. As such activities are not recognized by basic firewalls, companies often use database firewalls for protection. Some of the available SQL database firewalls provide/support honeypot architectures so that the intruder runs against a trap database while the web application remains functional.
Just as honeypots are weapons against spammers, honeypot detection systems are spammer-employed counter-weapons. As detection systems would likely use unique characteristics of specific honeypots to identify them, a great deal of honeypots in use makes the set of unique characteristics larger and more daunting to those seeking to detect and thereby identify them. This is an unusual circumstance in software: a situation in which "versionitis" (a large number of versions of the same software, all differing slightly from each other) can be beneficial. There's also an advantage in having some easy-to-detect honeypots deployed. Fred Cohen, the inventor of the Deception Toolkit, even argues that every system running his honeypot should have a deception port that adversaries can use to detect the honeypot. Cohen believes that this might deter adversaries.
Two or more honeypots on a network form a honey net. Typically, a honey net is used for monitoring a larger and/or more diverse network in which one honeypot may not be sufficient. Honey nets and honeypots are usually implemented as parts of larger network intrusion detection systems. A honey farm is a centralized collection of honeypots and analysis tools.
Updated on ClearOS' web page: ClearOS Pedigree
The metaphor of a bear being attracted to and stealing honey is common in many traditions, including Germanic and Slavic. A common Germanic kenning for the bear was "honey eater". The tradition of bears stealing honey has been passed down through stories and folklore, especially the well known Winnie the Pooh.
References and notes
- Naveen, Sharanya. "Honeypot". Retrieved 1 June 2016.
- Lance Spitzner (2002). Honeypots tracking hackers. Addison-Wesley. pp. 68–70. ISBN 0-321-10895-7.
- Litke, Pat. "Cryptocurrency-Stealing Malware Landscape". Secureworks.com. SecureWorks. Retrieved 9 March 2016.
- "Bitcoin Vigil: Detecting Malware Through Bitcoin". cryptocoins news. May 5, 2014.
- Edwards, M. "Antispam Honeypots Give Spammers Headaches". Windows IT Pro. Retrieved 11 March 2015.
- "Sophos reveals latest spam relaying countries". Help Net Security. Help Net Security. 24 July 2006. Retrieved 14 June 2013.
- "Honeypot Software, Honeypot Products, Deception Software". Intrusion Detection, Honeypots and Incident Handling Resources. Honeypots.net. 2013. Retrieved 14 June 2013.
- dustintrammell (27 February 2013). "spamhole – The Fake Open SMTP Relay Beta". SourceForge. Dice Holdings, Inc. Retrieved 14 June 2013.
- Ec-Council (5 July 2009). Certified Ethical Hacker: Securing Network Infrastructure in Certified Ethical Hacking. Cengage Learning. pp. 3–. ISBN 978-1-4354-8365-1. Retrieved 14 June 2013.
- Kaushik, Gaurav; Tyagi, Rashmi (2012). "Honeypot : Decoy Server or System Setup Together Information Regarding an Attacker" (PDF). VSRD International Journal of Computer Science & Information Technology. 2: 155–166.
- "Secure Your Database Using Honeypot Architecture". www.dbcoretech.com. August 13, 2010. Archived from the original on March 8, 2012.
- "Deception Toolkit". All.net. All.net. 2013. Retrieved 14 June 2013.
- "cisco router Customer support". Clarkconnect.com. Retrieved 2015-07-31.
- Honeynets a Honeynet Definition (PDF) by Ryan Talabis from PhilippineHoneynet.org
- "Know Your Enemy: GenII Honey Nets Easier to deploy, harder to detect, safer to maintain.". Honeynet Project. Honeynet Project. 12 May 2005. Retrieved 14 June 2013.
- "The word for "bear"". Pitt.edu. Retrieved 12 Sep 2014.
- Lance Spitzner (2002). Honeypots tracking hackers. Addison-Wesley. ISBN 0-321-10895-7.
- Sean Bodmer, CISSP, CEH, Dr Max Kilger, PhD, DrPH(c) Gregory Carpenter, CISM, Jade Jones, Esq., JD (2012). Reverse Deception: Organized Cyber Threat Counter-Exploitation. McGraw-Hill Education. ISBN 978-0071772495.