Vulnerabilities / Threats

12/1/2017
10:30 AM
Connect Directly
Twitter
LinkedIn
RSS
E-Mail vvv
50%
50%

Deception: Why It's Not Just Another Honeypot

The technology has made huge strides in evolving from limited, static capabilities to adaptive, machine learning deception.

Deception — isn't that a honeypot? That's a frequently asked question when the topic of deception technology arises. This two-part post will trace the origins of honeypots, the rationale behind them, and what factors ultimately hampered their wide-scale adoption. The second post focuses on what makes up modern-day deception technology, how the application of deception technology has evolved, and which features and functions are driving its adoption and global deployment.

Almost 15 years ago, Honeyd was introduced as the first commercially available honeypot and offered simple network emulation tools designed to detect attackers. The concept was intriguing but never gained much traction outside of organizations with highly skilled staff and for research. The idea was to place a honeypot outside the network, wait for inbound network connections, and see if an attacker would engage with the decoy.

Today's attackers are more sophisticated, well-funded, and increasingly more aggressive in their attacks. Human error will continually result in mistakes for attackers to exploit. With breaches getting more severe, the population getting less patient, and the emergence of regulations and fines, in-network threat detection has become critical for every organization's security infrastructure. So much so that FBR Capital Markets forecasts that the deception technology market as a detection security control will grow to $3 billion by 2019, three times its size in 2016.

The systemic problem is that organizations are overly dependent on their prevention infrastructure, leading to a detection gap once that attacker is inside the network. For the connected world we live in, it's widely believed in the industry that it no longer works to focus only on keeping attackers out. The structure is also flawed when applied to insiders, contractors, and suppliers who have forms of privileged access. Alternatively, solutions that rely on monitoring, pattern matching, and behavioral analysis are being used as a detection control but can be prone to false positives, making them complex and resource intensive.

The concept of setting traps for attackers is re-emerging given its efficiency and the advancements in deception technology that have removed scalability, deployment, and operational functionality issues that previously had hampered the wide-scale adoption of honeypots. Consequently, companies across the financial, healthcare, technology, retail, energy, and government sectors are starting to turn to deception technology as part of their defense strategies.

Deception is still a fairly new technology, so it is not surprising that seasoned security professionals will ask, "Isn't deception just a honeypot or honeynet?" In fairness, if you consider that they are both built on trapping technology, they are similar. Both technologies were designed to confuse, mislead, and delay attackers by incorporating ambiguity and misdirecting their operations. But that is where the similarity ends.  

Deception's Evolution
Gene Spafford, a leading security industry expert and professor of computer science at Purdue University, originally introduced the concept of cyber deception in 1989 when he employed "active defenses" to identify attacks that were underway, designed to slow down attackers, learn their techniques, and feed them fake data.

The next generation of advancements included low-interaction honeypots, such as Honeyd, built on limited service emulations. The ability to detect mass network scanning or automated attacks (malware, scripts, bots, scanners), track worms, and low purchase costs were the principal appeal of low-interaction honeypots. However, honeypot adoption was limited, given a number of limitations and associated management complexity, such as the following:

  • Honeypots were designed for detecting threats that are outside the network and were predominately focused on general research vs. responding to the more critical need for in-network detection.  
  • Human attackers can easily figure out if a system is emulated, fingerprint it, and avoid detection from honeypots
  • These systems are not high interaction, limiting the attack information that could be collected and any value in improving incident response.
  • Attackers could abuse a compromised system and use it as a pivot point to continue their attack.
  • Honeypots are not designed for scalability, are operationally intensive, and require skilled security professionals to operate

Deception technology has made monumental strides in evolving from limited, static capabilities to adaptive, machine learning deception that is designed for easy operationalization and scalability. Today's deception platforms are built on the pillars of authenticity/attractiveness, scalability, ease of operations, and integrations that accelerate incident response. Based on our own internal testing and from others in the emerging deception market, deception is now so authentic that highly skilled red team penetration testers continually fall prey to deception decoys and planted credentials, further validating the technology's ability to successfully detect and confuse highly skilled cyberattackers into revealing themselves. 

Related Content:

Carolyn Crandall is a technology executive with over 25 years of experience in building emerging technology markets in security, networking, and storage industries. She has a demonstrated track record of successfully taking companies from pre-IPO through to ... View Full Bio
Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
Crowdsourced vs. Traditional Pen Testing
Alex Haynes, Chief Information Security Officer, CDL,  3/19/2019
New Mirai Version Targets Business IoT Devices
Dark Reading Staff 3/19/2019
Register for Dark Reading Newsletters
White Papers
Video
Cartoon Contest
Write a Caption, Win a Starbucks Card! Click Here
Latest Comment: Reading Schneier's Friday Squid Blog again?
Current Issue
5 Emerging Cyber Threats to Watch for in 2019
Online attackers are constantly developing new, innovative ways to break into the enterprise. This Dark Reading Tech Digest gives an in-depth look at five emerging attack trends and exploits your security team should look out for, along with helpful recommendations on how you can prevent your organization from falling victim.
Flash Poll
The State of Cyber Security Incident Response
The State of Cyber Security Incident Response
Organizations are responding to new threats with new processes for detecting and mitigating them. Here's a look at how the discipline of incident response is evolving.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2019-6149
PUBLISHED: 2019-03-18
An unquoted search path vulnerability was identified in Lenovo Dynamic Power Reduction Utility prior to version 2.2.2.0 that could allow a malicious user with local access to execute code with administrative privileges.
CVE-2018-15509
PUBLISHED: 2019-03-18
Five9 Agent Desktop Plus 10.0.70 has Incorrect Access Control (issue 2 of 2).
CVE-2018-20806
PUBLISHED: 2019-03-17
Phamm (aka PHP LDAP Virtual Hosting Manager) 0.6.8 allows XSS via the login page (the /public/main.php action parameter).
CVE-2019-5616
PUBLISHED: 2019-03-15
CircuitWerkes Sicon-8, a hardware device used for managing electrical devices, ships with a web-based front-end controller and implements an authentication mechanism in JavaScript that is run in the context of a user's web browser.
CVE-2018-17882
PUBLISHED: 2019-03-15
An Integer overflow vulnerability exists in the batchTransfer function of a smart contract implementation for CryptoBotsBattle (CBTB), an Ethereum token. This vulnerability could be used by an attacker to create an arbitrary amount of tokens for any user.