What Is a Honeypot?

A cybersecurity honeypot is a decoy security mechanism designed to attract cyber attackers so that security researchers can see how they operate and what they might be after. The honeypot, typically isolated from the organization’s primary production environment, serves as bait to lure attackers into engaging with the system without endangering the organization’s data.

Honeypots are intentionally established to appear vulnerable and enticing to attackers, mimicking a legitimate target such as a network, server, or application. When the honeypot lures in attackers, security analysts can gather information about their identities, methods of attack, and the tools they use. An organization can then use this information to improve its cybersecurity strategy, identify potential blind spots in the existing architecture, and prioritize and focus security efforts based on the techniques used or the most commonly targeted assets.

Honeypots leverage a manufactured attack target to lure cybercriminals away from legitimate targets, enabling cybersecurity teams to monitor them and misdirect adversaries from actual targets.

By mimicking real-world systems—financial databases, IoT devices, or even broader network configurations—honeypots are seemingly vulnerable targets that are isolated and monitored closely. Any engagement with a honeypot is typically considered suspect since there’s no real operational purpose for legitimate users to interact with it.

The magic of honeypots lies in their ability to deceive hackers. When attackers engage with these decoys, they unknowingly reveal their strategies, tools, and intentions. Security teams get a firsthand look at potential threats, allowing them to study the methods of attackers in a controlled environment.

In essence, honeypots act as digital tripwires and distractions. They divert malicious entities away from real assets while providing invaluable insights into potential vulnerabilities and emerging threats. By understanding and analyzing interactions with honeypots, organizations can bolster their cybersecurity defenses in a more informed and proactive manner.

The concept of honeypots has existed since the late 1980s and early 1990s. The idea was first documented in two publications in 1991: “The Cuckoo’s Egg” by Clifford Stoll and “An Evening with Berferd” by Bill Cheswick. But it wasn’t until 1997 that Fred Cohen’s Deception Toolkit was released, which was one of the first honeypot solutions available to the security community. A year later, in 1998, development began on CyberCop Sting, one of the first commercial honeypots sold to the public.

Honeypots have evolved over time, and modern deception technology involves traps and decoys strategically placed around critical systems. Once an attacker has penetrated a honeypot, these decoy systems observe, track, and sometimes counterattack to attack them.^[1] Gartner Research identified deception technology as an “emerging technology” in 2016 that is becoming “market-viable.”^[2]

Different types of honeypots can be used in a cybersecurity strategy. Some of the most common types include:

• Production honeypots: These honeypots are positioned alongside genuine production servers and run the same kinds of services. Production honeypots pinpoint compromises in internal networks while deceiving malicious actors.
• Research honeypots: Research honeypots provide valuable information about a cybercriminal’s latest attack techniques and tools. They can be used to improve security measures and develop new defense strategies.
• Low-interaction honeypots: This type of honeypot allows partial interaction with systems since they run limited emulated services with restricted functionality. Low-interaction honeypots are an early detection mechanism organizations commonly use in production environments.
• High-interaction honeypots: High-interaction honeypots are more complex and allow attackers to interact with a real operating system. They’re more resource-intensive and require more maintenance than low-interaction honeypots.
• Pure honeypots: Pure honeypots refer to a full-scale system running on various servers. It completely mimics the production system. User information and data are manipulated to appear confidential and sensitive, and various sensors track and observe threat actor activity.
• Client honeypots: This type of honeypot is established to simulate vulnerable client systems, such as web browsers or email clients. Client honeypots can be used to detect and analyze client-side attacks.
• Virtual honeypots: These honeypots are virtual machines that simulate a real system. They can be used to detect and analyze attacks on virtualized environments.

Each type of honeypot has specific use case applications and subsequent strengths and weaknesses. Therefore, organizations should carefully evaluate their objectives and resources when designing a honeypot strategy.

By following these best practices, organizations can effectively deploy honeypots and deception technologies to improve their cybersecurity strategy and identify potential blind spots in the existing architecture.

While honeypots can be invaluable cybersecurity tools, they can pose several challenges and limitations.

• Limited Scope: Honeypots only capture threats that interact with them. If attackers target other parts of the network and avoid the honeypot, the threat may go undetected.
• Maintenance: Honeypots require continuous updates to mimic real systems convincingly. Experienced attackers might easily recognize an outdated honeypot.
• Potential misuse: If not properly isolated or secured, attackers can exploit honeypots as a launch point for further network attacks.
• False sense of security: Relying solely on honeypots might lead organizations to overlook other essential security measures, leading to potential vulnerabilities.
• Resource intensive: Setting up, managing, and analyzing data from honeypots can be resource-intensive, requiring both time and expertise.
• Risk of detection: Sophisticated attackers might recognize and avoid honeypots, making them ineffective against advanced threats.
• Data overload: Honeypots can generate vast amounts of data, which can be challenging to analyze effectively, especially if there are numerous false positives.
• Skill requirement: Deploying and managing honeypots requires expertise to ensure they are effective and do not introduce additional vulnerabilities.
• Potential for escalation: Engaging with certain attackers might lead them to escalate their efforts, potentially leading to more aggressive attacks on the organization.

Understanding these limitations and challenges is essential for organizations considering deploying honeypots, ensuring they are used effectively within a broader cybersecurity strategy.

Honeypots have been used in various scenarios to study and counteract malicious activities. Here are some use cases and corresponding examples based on popular types and applications:

• Research honeypots analyze how a new strain of malware spreads or study botnet behavior. Example: Universities or cybersecurity research organizations may deploy honeypots to gather data on malware propagation, attacker techniques, or emerging threats.
• Production honeypots protect sensitive customer data by diverting attackers to the decoy server. Example: A financial institution might set up a honeypot mimicking a transaction server to attract attackers and monitor their strategies.
• IoT honeypots understand threats specific to IoT devices, such as particular malware strains or exploitation techniques. Example: A company deploying multiple IoT devices creates a mock IoT device network as honeypots.
• Database honeypots attract and detect attackers targeting sensitive or proprietary data. Example: A decoy database filled with fabricated data but mimicking the structure of a genuine one.
• Web application honeypots identify web-based attack techniques like SQL injection or cross-site scripting attempts. Example: A fake e-commerce website or web portal that appears vulnerable.
• Spam honeypots (Spampots) study spam campaigns, phishing attempts, or malicious attachments. Example: An email server explicitly designed to attract and capture spam emails and malware.
• Client honeypots collect and analyze new malware variants or understand the infrastructure of cybercriminal networks. Example: A system set up to actively engage with malicious servers to gather malware samples or study exploit kits.
• Honeytokens detect unauthorized access or data breaches when these fake credentials are used. Example: A fabricated user credential or API key sprinkled within a system.

By deploying honeypots in these diverse scenarios, organizations can gain insights into potential threats, improve their security posture, and better protect their genuine assets.

While several companies have developed products to build deception technology, including honeypots, researchers at the University of Texas at Dallas have been researching where deception technology is going next. UT Dallas has developed the DeepDig (DEcEPtion DIGging) technique that plants traps and decoys onto real systems before applying machine learning techniques to better understand a malware attacker’s behavior. The technique is designed to use cyber-attacks as free sources of live training data for machine learning-based intrusion detection systems (IDS). These decoy systems act as a honeypot so that once an attacker has penetrated a network, security teams won’t just be notified but can fight back.^[4]

Additionally, the future of honeypot technologies may also involve more intricate distributed deception platforms (DDPs). These platforms involve traps and decoys strategically placed around key systems, allowing organizations to gather information across the production environment. This strategy enables organizations to identify threats with greater precision and allocate their security resources by concentrating on the methods employed or the assets most frequently attacked.

Proofpoint offers a range of cybersecurity solutions to use with honeypots to improve an organization’s cybersecurity posture. These solutions include email security, cloud security, threat intelligence, and security awareness training.

By using Proofpoint’s solutions, organizations can improve their ability to detect and respond to cyber threats, including those detected by honeypots. Proofpoint’s solutions can help organizations identify potential blind spots in their existing architecture and prioritize and focus security efforts based on the techniques being used or the most commonly targeted assets. For more information, contact Proofpoint.

[1] Varun Haran, BankInfoSecurity.com “Deception Technology in 2020”
[2] Lawrence Pingree, Gartner “Deception-related technology – it’s not just ‘nice to have’, it’s a new strategy of defense”
[3] Augusto Barros Gartner Research “New Research: Deception Technologies”
[4] John Leyden, The Daily Swig “AI-powered honeypots: Machine learning may help improve intrusion detection”