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June 21, 2018

Unsupervised Machine Learning and JA3 for Enhanced Security

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21
Jun 2018
Unlock the true power of Darktrace's algorithms. Learn how JA3 enhances cybersecurity defenses with unique TLS/SSL fingerprints & unsupervised machine learning.

Introducing JA3

JA3 is a methodology for fingerprinting Transport Layer Security applications. It was first posted on GitHub in June 2017 and is the work of Salesforce researchers John Althouse, Jeff Atkinson, and Josh Atkins. The JA3 TLS/SSL fingerprints created can overlap between applications but are still a great Indicator of Compromise (IoC). Fingerprinting is achieved by creating a hash of 5 decimal fields of the Client Hello message that is sent in the initial stages of an TLS/SSL session.

JA3 is an interesting approach to the increasing usage of encryption in networks. There is also a clear uptick in cyber-attacks using encrypted command and control (C2) channels – such as HTTPS – for malware communication.

The benefits of JA3 for enhancing rules-and-signatures security

These near-unique fingerprints can be used to enhance traditional cyber security approaches such as whitelisting, deny-listing, and searching for IoCs.

Let’s take the following JA3 hash for example: 3e860202fc555b939e83e7a7ab518c38. According to one of the public lists that maps JA3s to applications, this JA3 hash is associated with the ‘hola_svc’ application. This is the infamous Hola VPN solution that is non-compliant in most enterprise networks. On the other hand, the following hash is associated with the popular messenger software Slack: a5aa6e939e4770e3b8ac38ce414fd0d5. Traditional cyber security tools can use these hashes like traditional signatures to search for instances of them in data sets or trying to deny-list malicious ones.

While there is some merit to this approach, it comes with all the known limitations of rules-and-signatures defenses, such as the overlaps in signatures, the inability to detect unknown threats, as well as the added complexity of having to maintain a database of known signatures.

JA3 in Darktrace

Darktrace creates JA3 hashes for every TLS/SSL connection it encounters. This is incredibly powerful in a number of ways. First, the JA3 can add invaluable context to a threat hunt. Second, Darktrace can also be queried to see if a particular JA3 was encountered in the network, thus providing actionable intelligence during incident response if JA3 IoCs are known to the incident responders.

Things become much more interesting once we apply our unsupervised machine learning to JA3: Darktrace’s AI algorithms autonomously detect which JA3s are anomalous for the network as a whole and which JA3s are unusual for specific devices.

It basically tells a cyber security expert: This JA3 (3e860202fc555b939e83e7a7ab518c38) has never been seen in the network before and it is only used by one device. It indicates that an application, which is used by nobody else on the network, is initiating TLS/SSL connections. In our experience, this is most often the case for malware or non-compliant software. At this stage, we are observing anomalous behavior.

Darktrace’s AI combines these IoCs (Unusual Network JA3, Unusual Device JA3, …) with many other weak indicators to detect the earliest signs of an emerging threat, including previously unknown threats, without using rules or hard-coded thresholds.

Catching Red-Teams and domain fronting with JA3

The following is an example where Darktrace detected a Red-Team’s C2 communication by observing anomalous JA3 behavior.

The unsupervised machine learning algorithms identified a desktop device using a JA3 that was 100% unusual for the network connecting to an external domain using a Let’s Encrypt certificate, which, along with self-signed certificates, is often abused by malicious actors. As well as the JA3, the domain was also 100% rare for the network – nobody else visited it:

It turned out that a Red-Team had registered a domain that was very similar to the victim’s legitimate domain: www.companyname[.]com (legitimate domain) vs. www.companyname[.]online (malicious domain). This was intentionally done to avoid suspicion and human analysis. Over a 7-day period in a 2,000-device environment, this was the only time that Darktrace flagged unusual behavior of this kind.

As the C2 traffic was encrypted (therefore no intrusion detection was possible on the payload) and the domain was non-suspicious (no reputation-based deny-listing worked), this C2 had remained undetected by the rest of the security stack.

Combining unsupervised machine learning with JA3 is incredibly powerful for the detection of domain fronting. Domain fronting is a popular technique to circumvent censorship and to hide C2 traffic. While some infrastructure providers take action to prevent domain fronting on their end, it is still prevalent and actively used by attackers.

The only agreed-upon method within wide parts of the cyber-security community to detect domain fronting appears to be TLS/SSL inspection. This usually involved breaking up encrypted communication to inspect the clear-text payloads. While this works, it commonly involves additional infrastructure, network restructuring and comes with privacy issues – especially in the context of GDPR.

Unsupervised machine learning makes the detection of domain fronting without having to break up encrypted traffic possible by combining unusual JA3 detection with other anomalies such as beaconing. A good start for a domain fronting threat hunt? A device beaconing to an anomalous CDN with an unusual JA3 hash.

Conclusion

JA3 is not a silver bullet to pre-empt malware compromise. As a signature-based solution, it shares the same limitations of all other defenses that rely on pre-identified threats or deny-lists: having to play a constant game of catch-up with innovative attackers. However, as a novel means of identifying TLS/SSL applications, JA3 hashing can be leveraged as a powerful network behavioral indicator, an additional metric that can flag the use of unauthorized or risky software, or as a means of identifying emerging malware compromises in the initial stages of C2 communication. This is made possible through the power of unsupervised machine learning.

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Author
Max Heinemeyer
Global Field CISO

Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.

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April 4, 2025

Darktrace Named as Market Leader in the 2025 Omdia Market Radar for OT Cybersecurity Platforms

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We are pleased to announce that Darktrace / OT has been named a Market Leader in Omdia’s  2025 Market Radar for OT Cybersecurity Platforms. We believe this highlights our unique capabilities in the OT security market and follows similar recognition from Gartner who recently named Darktrace / OT as the sole Visionary in in the Magic Quadrant for Cyber Physical Systems (CPS) Protection Platforms market.

Historically, IT and OT systems have been managed separately, creating challenges due to the differences of priorities between the two domains. While both value availability, IT emphasizes confidentiality and integrity whereas OT focuses on safety and reliability. Organizations are increasingly converging these systems to reap the benefits of automation, efficiency, and productivity (1).

Omdia’s research highlights that decision makers are increasingly prioritizing comprehensive security coverage, centralized management, and advanced cybersecurity capabilities when selecting OT security solutions (1).

Rising productivity demands have driven the convergence of OT, IT, and cloud-connected systems, expanding attack surfaces and exposing vulnerabilities. Darktrace / OT provides a comprehensive OT security solution, purpose-built for critical infrastructure, offering visibility across OT, IoT, and IT assets, bespoke risk management, and industry-leading threat detection and response powered by Self-Learning AITM.

Figure 1: Omdia vendor overview for OT cybersecurity platforms
Figure 1: Omdia vendor overview for OT cybersecurity platforms

An AI-first approach to OT security  

Many OT security vendors have integrated AI into their offerings, often leveraging machine learning for anomaly detection and threat response. However, only a few have a deep-rooted history in AI, with longstanding expertise shaping their approach beyond surface-level adoption.

The Omdia Market Radar recognizes that Darktrace has extensive background in the AI space:

“Darktrace has invested extensively in AI research to fuel its capabilities since 2013 with 200-plus patent applications, providing anomaly detection with a significant level of customization, helping with SOC productivity and efficiency, streamlining to show what matters for OT.” (1)

Unlike other security approaches that rely on existing threat data, Darktrace / OT achieves this through Self-Learning AI that understands normal business operations, detecting and containing known and unknown threats autonomously, thereby reducing Sec Ops workload and ensuring minimal downtime

This approach extends to incident investigations where an industry-first Cyber AI AnalystTM automatically investigates all relevant threats across IT and OT, prioritizes critical incidents, and then summarizes findings in an easily understandable view—bringing production engineers and security analysts together to communicate and quickly take appropriate action.

Balancing autonomous response with human oversight

In OT environments where uptime is essential, autonomous response technology can be approached with apprehension. However, Darktrace offers customizable response actions that can be set to “human confirmation mode.”

Omdia recognizes that our approach provides customizable options for autonomous response:

“Darktrace’s autonomous response functionality enforces normal, expected behavior. This can be automated but does not need to be from the beginning, and it can be fine-tuned. Alternative step-by-step mitigations are clearly laid out step-by-step and updated based on organizational risk posture and current level of progress.” (1)

This approach allows security and production to keep humans-in-the-loop with pre-defined actions for potential attacks, enforcing normal to contain a threat, and allowing production to continue without disruption.  

Bespoke vulnerability and risk management

In the realm of OT security, asset management takes precedent as one of the key focus points for organizations. With a large quantity of assets to manage, practitioners are overwhelmed with information with no real way to prioritize or apply them to their unique environment.

Darktrace / OT is recognized by Omdia as having:

“Advanced risk management capabilities that showcase metrics on impact, exploit difficulty, and estimated cost of an attack […] Given the nascency of this capability (April 2024), it is remarkably granular in depth and insight.” (1)

Enabling this is Darktrace’s unique approach to AI extends to risk management capabilities for OT. Darktrace / OT understands customers’ unique risks by building a comprehensive and contextualized picture that goes beyond isolated CVE scoring. It combines attack path modeling with MITRE ATT&CK  techniques to provide hardening recommendations regardless of patching availability and gives you a clearer view of the potential impact of an attack from APT groups.

Modular, scalable security for industrial environments

Organizations need flexibility when it comes to OT security, some want a fully integrated IT-OT security stack, while others prefer a segregated approach due to compliance or operational concerns. The Darktrace ActiveAI Security Platform offers integrated security across multiple domains, allowing flexibility and unification across IT and OT security. The platform combines telemetry from all areas of your digital estate to detect and respond to threats, including OT, network, cloud, email, and user identities.

Omdia recognizes Darktrace’s expansive coverage across multiple domains as a key reason why organizations should consider Darktrace / OT:

“Darktrace’s modular and platform, approach offer’s integrated security across multiple domains. It offers the option of Darktrace / OT as a separate platform product for those that want to segregate IT and OT cybersecurity or are not yet in a position to secure both domains in tandem. The deployment of Darktrace’s platform is flexible—with nine different deployment options, including physical on-premises, virtual, cloud, and hybrid.” (1)

With flexible deployment options, Darktrace offers security teams the ability to choose a model that works best for their organization, ensuring that security doesn’t have to be a “one-size-fits-all” approach.

Conclusion: Why Darktrace / OT stands out in Omdia’s evaluation

Omdia’s 2025 Market Radar for OT Cybersecurity Platforms provides a technical-first, vendor-agnostic evaluation, offering critical insights for organizations looking to strengthen their OT security posture. Darktrace’s recognition as a Market Leader reinforces its unique AI-driven approach, flexible deployment options, and advanced risk management capabilities as key differentiators in an evolving threat landscape.

By leveraging Self-Learning AI, autonomous response, and real-world risk analysis, Darktrace / OT enables organizations to detect, investigate, and mitigate threats before they escalate, without compromising operational uptime.

Read the full report here!

References

  1. www.darktrace.com/resources/darktrace-named-a-market-leader-in-the-2025-omdia-market-radar-for-ot-cybersecurity-platforms
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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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Cloud

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April 2, 2025

Fusing Vulnerability and Threat Data: Enhancing the Depth of Attack Analysis

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Cado Security, recently acquired by Darktrace, is excited to announce a significant enhancement to its data collection capabilities, with the addition of a vulnerability discovery feature for Linux-based cloud resources. According to Darktrace’s Annual Threat Report 2024, the most significant campaigns observed in 2024 involved the ongoing exploitation of significant vulnerabilities in internet-facing systems. Cado’s new vulnerability discovery capability further deepens its ability to provide extensive context to security teams, enabling them to make informed decisions about threats, faster than ever.

Deep context to accelerate understanding and remediation

Context is critical when understanding the circumstances surrounding a threat. It can also take many forms – alert data, telemetry, file content, business context (for example asset criticality, core function of the resource), and risk context, such as open vulnerabilities.

When performing an investigation, it is common practice to understand the risk profile of the resource impacted, specifically determining open vulnerabilities and how they may relate to the threat. For example, if an analyst is triaging an alert related to an internet-facing Webserver running Apache, it would greatly benefit the analyst to understand open vulnerabilities in the Apache version that is running, if any of them are exploitable, whether a fix is available, etc. This dataset also serves as an invaluable source when developing a remediation plan, identifying specific vulnerabilities to be prioritised for patching.

Data acquisition in Cado

Cado is the only platform with the ability to perform full forensic captures as well as utilize instant triage collection methods, which is why fusing host-based artifact data with vulnerability data is such an exciting and compelling development.

The vulnerability discovery feature can be run as part of an acquisition – full or triage – as well as independently using a fast ‘Scan only’ mode.

Figure 1: A fast vulnerability scan being performed on the acquired evidence

Once the acquisition has completed, the user will have access to a ‘Vulnerabilities’ table within their investigation, where they are able to view and filter open vulnerabilities (by Severity, CVE ID, Resource, and other properties), as well as pivot to the full Event Timeline. In the Event Timeline, the user will be able to identify whether there is any malicious, suspicious or other interesting activity surrounding the vulnerable package, given the unified timeline presents a complete chronological dataset of all evidence and context collected.

Figure 2: Vulnerabilities discovered on the acquired evidence
Figure 3: Pivot from the Vulnerabilities table to the Event Timeline provides an in-depth view of file and process data associated with the vulnerable package selected. In this example, Apache2.

Future work

In the coming months, we’ll be releasing initial versions of highly anticipated integrations between Cado and Darktrace, including the ability to ingest Darktrace / CLOUD alerts which will automatically trigger a forensic capture (as well as a vulnerability discovery) of the impacted assets.

To learn more about how Cado and Darktrace will combine forces, request a demo today.

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About the author
Paul Bottomley
Director of Product Management, Cado
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