Honeypots
Discover the strategic use of Honeypots in modern cybersecurity. Learn how they bolster Intrusion Detection Systems and stay ahead of potential threats.
Introduction to HoneyPots
A honeypot is a cybersecurity trap companies use to lure hackers into identifying and researching the tactics and types of attacks they utilize. On the internet, it serves as a potential target and alerts the defenders to any unauthorized attempts to access the information system.
The main objective of deploying a honeypot is not to protect the organization but to study the cyberattack techniques and behaviors to enhance the existing security system to be more resilient when facing such attacks. A good analogy would be a football team watching footage of their opponent’s previous games. By studying their game strategies and plays, they can be better equipped to face them come game time.
Difference between Honeypot and Sandbox.
Honeypot is not to be confused with sandboxing. Sandboxing and honeypot security are two cybersecurity tactics constantly evolving but can be confused with one another. These two technologies are quite different, offering valuable solutions to various cybersecurity issues. It is worth understanding the differences between these two technologies.
Types of Honeypots
Types of Honeypots Based on Purpose
Honeypots can serve two primary purposes: Research or Production
Production honeypots
A production honeypot, the most common type, is used to gather cybersecurity-related data inside a company's or organization's production network. The production honeypot will be set up and wait for an attack. Information such as originating Internet Protocol (IP) addresses, traffic volume and frequency, directory accessories, and more may be collected if an attack occurs.
Research Honeypots
On the other hand, a research honeypot is a kind of honeypot that's utilized to gather data regarding the precise techniques and strategies hackers employ. They include phony data that appears sensitive and valuable to hackers, much like production honeypots. Research honeypots also gather data on assaults and weaknesses.
Businesses don't often use research honeypots. Government and research institutions employ them instead. This is the key difference between these two honeypots. Research honeypots are generally deployed on several networks or locations instead of production honeypots, which are utilized inside a company's network.
Research honeypots offer more details regarding attacks and vulnerabilities than production honeypots, which means they are more complicated and demand more significant effort to deploy.
Types of Honeypots Based on Activity Type
Email Honeypots are dormant email addresses disguised as accurate, active emails. Hackers may obtain these emails illegally; if they attempt to use this email, they must be a hacker. This is because no natural person actually uses this e-mail, and there is only one way to get the e-mail.
Database Honeypots
A database honeypot is a phony database deployed to draw database-specific attacks like SQL injection. Firewalls frequently let such attacks through. Businesses utilize firewalls that support honeypot systems to keep an attacker away from the real database.
Malware Honeypots are specifically designed to attract malware attacks, as the name implies. For example, an emulated USB flash drive can be a malware honeypot. If the machine with the USB drive is attacked, the honeypot will lure the attacker to the emulated USB drive.
A Spider Honeypot aims to trap web crawlers, hence the term “spider.” These honeypots are web pages and links that crawlers can access.
Types of Honeypots Based on Complexity
Honeypots can be classified by different complexities, which permit threat actors to perform different levels of malicious activity.
Low-interaction Honeypot
An attacker will only have limited access to the operating system using a low-interaction honeypot. Low interaction simply indicates that the opponent won't be able to engage in any substantial engagement with your decoy system because it is a more static environment. A low-interaction honeypot will often only simulate a few network services and internet protocols, just enough to fool the attacker and nothing more. Most businesses typically emulate protocols like TCP and IP, giving an attacker the impression that they are connected to a genuine system rather than a honeypot environment.
Mid-interaction Honeypot
The major drawback of a high-interaction honeypot is the resources needed to develop the high-interaction decoy system from scratch and keep track of it over time to reduce the risk for your business. For many, a medium-interaction honeypot method strikes the optimal mix between risk and functionality by diverting attackers without constructing a full physical or virtualized system. These may target attackers searching for specific vulnerabilities but would still not be appropriate for complex threats like zero-day exploits. A medium interaction honeypot, for instance, might imitate a Microsoft IIS web server and offer sufficient capability to draw a specific attack that researchers are interested in learning more about.
High-interaction Honeypot
The other end of the deception technology spectrum is a high-interaction honeypot. The attacker is given real and complete systems to attack rather than just emulating specific protocols or services, making it much less likely that they will suspect they are being diverted or watched. It is simple to identify threats and track and trace an attacker's activities because the systems are only used as a ruse, and any discovered traffic is harmful by virtue of its very presence. Researchers can discover an attacker's methods for escalating privileges or their lateral moves in quest of sensitive material by using a honeypot with a high interaction rate.
Advantages and Limitations
Advantages:
First, using honeypots enables an administrator to discover hacker techniques. This is due to the ability to see and assess attacks made against honeypots. One can create parallel systems with meaningless information using honeypots. Due to the active monitoring of these systems, information security is increased. This monitoring allows for the documentation of all hacker activity for later use.
The second advantage comes because of the first advantage. Since companies can document hackers’ behaviors, they can detect an attack before it even happens by monitoring for similar hacker techniques in their network.
Disadvantages:
Utilizing honeypots has the drawback of luring attackers. This is because once an attacker has gained access to a honeypot, they can be inspired to launch other attacks. Attackers are persistent, and being duped can fuel their motivation to try gaining access to the whole system.
Another drawback of utilizing honeypots is that they merely increase network design complexity. This implies that the additional resources will result in higher maintenance expenses. For honeypots to function well, they must also be kept operational.
The upcoming section covers various IP trends for patents concerning Honeypot-related inventions.
IP Trends and Insights
Filing trend in this domain for the last ten years
Top Jurisdiction
Top CPCs
Top assignees in this domain
According to public databases, there are over 2800 patents around Honeypots in the network security domain. Figure A depicts the publication trend for this Patent Set. As one can see, there has been a constant rise since 2006.
Publication Trends
Figure A depicts the publication trend for this Patent Set. As one can see, there has been a constant rise since 2013. According to public databases, over 2800 patents around Honeypots exist in the network security domain.
Top Countries
Figure B depicts the top Honeypot - Network Security domain jurisdictions with the most patents. China tops the list with 1655 patents, followed by the United States with 650 patents.
CPC Distribution
Figure C depicts the CPC (Cooperative Patent Classification) distribution of the Patent Set, which is dominated by the sub-class H04L 63/1491.
Below are the top 5 CPC classes with definitions:
H04L63/1491 - Countermeasures against malicious traffic using deception as countermeasures, e.g., honeypots, honeynets, decoys, or entrapment.
H04L63/1416 - Real-time detection of attacks or intrusion attempts (e.g., "misuse detection").
H04L63/1425 - Traffic logging for security purposes (e.g., detecting normal or anomalous behavior; comparing behavior; offline analysis using data mining, network security audit); non-real detection for deferred analysis.
H04L63/1408 - Detection of attacks involves monitoring the traffic on the network. Detection can be performed by different means: anomaly detection (comparing monitored traffic against regular traffic); and misuse detection (detecting specific traces that imply an attack).
H04L63/1441 - Detection and mitigation of particular types of attacks.
Top Assignees
Figure C depicts the patent set's assignee distribution. This distribution is spread out, and only the top 10 assignees are shown in the chart. IBM leads the list with 81 patents.
Honeypots and their future with AI/ML
Because honeypots are a deception technique that allows attacker behavior patterns to be understood, modern ML algorithms are particularly useful in comprehending complex data and making data-driven decisions. AI’s ability to constantly adapt and learn is advantageous for network security improvement. AI employs machine learning and deep learning to identify patterns on the network and group them; it then uses this information to identify deviations or security incidents before an attack occurs. Future security can be enhanced with the use of these patterns. Similar potential risks can be found and stopped before they can do any damage. It's challenging for hackers to surpass their intelligence because of continuous learning.
213 of 2800 patents correspond to Honeypot intrusion systems integrated with AI/ ML.
AI/ML-based Honeypot patent trend
This graph depicts the patent publication trend for the Honeypot system integrated with machine learning models. The publication of such patents around Honeypot systems have increased drastically since 2018.
As an illustration, consider using machine learning to automatically create honeypots based on discovering cyber-attacks to a network. Another illustration would be to create honeypot files that would serve as a decoy for the attacker by utilizing machine learning models to map the search terms of a cyber-attacker to sensitive documents.
AI/ML-based Honeypot patents – Top Assignee
Below graph shows assignees having patented innovations centered around Honeypots integrated with Artificial Intelligence. Clearly, Microsoft tops the list.
Few inventions disclose intelligent Honeypot involving AI/ ML.
US11237884B2
US10462181B2
US11032319B1
US10594729B2
Summary
The use of machine learning in honeypots is likely to become increasingly prevalent in the coming years. Potential areas of expansion include (a) Reinforcement learning to customize honeypot settings in response to changing threat scenarios dynamically, and (b) Generative Adversarial Networks (GANs) in honeypots can further improve the creation of high-impact and satisfying decoy information for attackers. In a nutshell, combining machine learning with honeypots will result in more effective and efficient security systems that can adapt and grow to keep up with the always-evolving threat landscape.
Disclaimer: This report is based on information that is publicly available and reliable. However, Lumenci cannot be held responsible for the accuracy or reliability of this data.
Disclaimer: This report is based on information that is publicly available and reliable. However, Lumenci cannot be held responsible for the accuracy or reliability of this data.