ブログ
/
/
April 29, 2020

How Email Attackers Are Buying Domain Names to Get Inboxes

Explore how mass domain purchasing allows cyber-criminals to stay ahead of legacy email tools — and how cyber AI stops the threats that slip through.
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.
Written by
Dan Fein
VP, Product
Default blog image
29
Apr 2020

It is by now common knowledge that the vast majority of cyber-threats start with an email. In the current working conditions, this is more true than ever – with a recent study reporting a 30,000% increase in phishing, websites, and malware targeting remote users.

Many email security tools struggle to detect threats they encounter for the first time. Attackers know this and are leveraging many techniques to take advantage of this fundamental flaw. This includes automation to mutate common threat variants, resulting in a massive increase in unknown threats. Another technique, which will be the focus of this blog post, is the rapid and widespread creation of new domains in order to evade reputation checks and signature-based detection.

The recent surge in domain creation

While traditional tools have to rely on identifying campaigns and patterns across multiple emails to establish whether or not an email is malicious, Cyber AI technology doesn’t require classifying emails into buckets in order to know they don’t belong. There is no need, therefore, to actively track campaigns. But as security researchers, it’s hard to miss some trends.

Since the coronavirus outbreak, we have seen the number of domains registered related to COVID-19 increase by 130,000. In this time, 60% of all spear phishing threats neutralized by Antigena Email were related to COVID-19 or remote work. Another recent study determined that 10,000 coronavirus-related domains are created every day, with roughly nine out of ten of these either malicious or attempting to generate sales of fake products.

With attackers also taking advantage of changing online behaviors arising from the pandemic, another trend we’ve seen is the proliferation of the keyword ‘Zoom’ in some of the unpopular domains that bypassed traditional tools, as attackers leverage the video conferencing platform’s recent rise in usage.

“I believe that hackers identified coronavirus as something users are desperate to find information on. Panic leads to irrational thinking and people forget the basics of cyber security.”

— COO, Atlas VPN

I recently wrote a blog post on the idea of ‘fearware’ and why it’s so successful. Right now, people are desperate for information, and attackers know this. Cyber-criminals play into fear, uncertainty, and doubt (FUD) through a number of mechanisms, and we have since seen a variety of imaginative attempts to engage recipients. These emails range from fake ‘virus trackers’, to sending emails purporting to be from Amazon, claiming an unmanageable rise in newly registered accounts, and demanding “re-registration” of the recipient’s credit card details should they wish to keep their account.

Domain name purchasing: A vicious cycle

Purchasing thousands of new domains and sending malicious emails en masse is a tried and tested technique that cyber-criminals have been leveraging for decades. Now with automation, they’re doing it faster than ever before.

Here’s why it works.

Traditional security tools work by analyzing emails in isolation, measuring them against static blacklists of ‘known bads’. By way of analogy, the gateway tool here is acting like a security guard standing at the perimeter of an organization’s physical premises, asking every individual who enters: “are you malicious?”

The binary answer to this sole question is extracted by looking at some metadata around the email, including the sender’s IP, their email address domain, and any embedded links or attachments. They analyze this data in a vacuum, and at face value, with no consideration towards the relationship between that data, the recipient, and the rest of the business. They run reputation checks, asking “have I seen this IP or domain before?” Crucially, if the answer is no, they let them straight through.

To spell that out, if the domain is brand new, it won’t have a reputation, and as these traditional tools have a limited ability to identify potential harmful elements via any other means, they have no choice but to let them in by default.

These methods barely scratch the surface of a much wider range of characteristics that a malicious email might contain. And as email threats get ever more sophisticated, the ‘innocent until proven guilty approach’ is not enough. For a comprehensive check, we would want to ask: does the domain have any previous relationship with the recipient? The organization as a whole? Does it look suspiciously visually similar to other domains? Is this the first time we’ve seen an inbound email from this user? Has anybody in the organization ever shared a link with this domain? Has any user ever visited this link?

Legacy tools are blatantly asking the wrong questions, to which attackers know the answers. And usually, they can skirt by these inattentive security guards by paying just a few pennies for new domains.

How to buy your way in

Let’s look at the situation from an attacker’s perspective. They just need one email to land and it could be keys to the kingdom, so an upfront purchase of a few thousand new domains will almost inevitably pay off. And they’d pay the price as long as it’s working and they’re profiting.

This is exactly what attackers are doing. Newly-registered domains consistently get through gateways until these traditional tools are armed with enough information to determine that the domains are bad, by which point thousands or even millions of emails could have been successfully delivered. As soon as the attack infrastructure is worn out, the attackers will abandon it, and very easily just purchase and deploy a new set of domains.

And so, the vicious cycle continues. Like a game of ‘whack-a-mole’, these legacy ‘solutions’ will continue to hammer down on recognized ‘bad’ emails – all the while more malicious domains are being created in the thousands in preparation for the next campaign. This is the ‘Domain Game’, and it’s a hard game for defenders to win.

Asking the right questions

Thankfully, the solution to this problem is as simple as the problem itself. It requires a movement away from the legacy approach and towards deploying technology that is up to par with the speed and scale of today’s attackers.

In the last two years, new technologies have emerged that leverage AI, seeking to understand the human behind the email address. Rather than inspecting incoming traffic at the surface-level and asking binary questions, this paradigm shift away from this insufficient legacy approach asks the right questions: not simply “are you malicious?”, but crucially: “do you belong?”

Informed by a nuanced understanding of the recipient, their peers, and the organization at large, every inbound, outbound, and internal email is analyzed in context, and is then re-analyzed over and over again in light of evolving evidence. Asking the right questions and understanding the human invariably sets a far higher standard for acceptable catch rates with unknown threats on first encounter. This approach far outpaces traditional email defenses which have proven to fail and leave companies and their employees vulnerable to malicious emails sitting in their inboxes.

Rather than desperately bashing away at blacklisted domains and IP addresses in an ill-fated attempt to beat the attackers, we can change the game altogether, tilting the scales in favor of the defenders – securing our inboxes and our organizations at large.

Learn more about Antigena Email.

‍

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.
Written by
Dan Fein
VP, Product

More in this series

No items found.

Blog

/

OT

/

October 21, 2026

Beyond the Fridge: Exposed Industrial Control Systems

Default blog imageDefault blog image

Introduction

In October 2026, hundreds of supermarkets across France were reportedly affected by a cyberattack targeting their refrigeration systems. According to public reports, the attackers gained access to internet-connected controllers and modified their settings, forcing refrigeration units into high and extremely low temperatures and causing significant disruption. Total losses have been reported to reach several million euros.

The incident drew attention to the security of industrial refrigeration systems, particularly older devices with exposed management interfaces and known vulnerabilities.

The exact details of how the attackers gained access have not been confirmed, however it is speculated that the public-facing web server could be accessed with the username “admin” and password being the current date. Researchers from Claroty’s Team82 identified a vulnerability in Danfoss AK-SM 8xxA Series in 2025, “CVE-2025-41450", allowing for administrative access using a time-based “code-of-the-day" for authentication [1].

Default credentials, outdated firmware and publicly accessible administrative interfaces have been an issue for years. And yet these systems continue to appear in internet scans and require little sophistication to gain admin access. A simple search of internet-connected devices shows a range of exposed devices from Programmable Logic Controllers (PLCs) and remote gateways, to, yes, more fridges.

Darktrace malware researchers identified hundreds of IoT and OT devices publicly exposed on the internet, many of which are configured with no authentication, weak credentials, or default credentials. As shown in Figure 1, an exposed Copeland XWEB web interface, used to manage Dixell equipment, including refrigerators, can provide the ability to remotely control connected devices.

‍

Figure 1: Copeland XWEB interface used to control systems including refrigerators and air conditioning.

However, it’s not only fridges; web management interfaces for industrial controls are also publicly accessible, often protected only by weak credentials. Figure 2 shows an example of a publicly exposed WAGO interface. The WAGO management interface is a configuration and administration panel for controllers. Through it, a logged-in user can view device details and diagnostics, manage user accounts and passwords, update firmware, back up or restore the full configuration, set the clock, and manage SD card storage. While it typically doesn’t manage switch outputs or read live process values, someone with access could still disrupt operation by changing network or Modbus settings, loading bad firmware or configurations, or by resetting passwords and enabling insecure services.

‍

Figure 2: WAGO Management interface.

‍

The issue extends beyond management interfaces. During our research, we identified publicly accessible web interfaces belonging to PLCs, including Rockwell Automation Allen-Bradley devices. In each case, the risk arises from how the devices have been deployed and configured, not from a flaw in the vendors’ products.

‍

Figure 3: Web interface for Allen-Bradley CompactLogix controller.

‍

Figure 3 shows the web interface of an Allen-Bradley CompactLogix controller. Unlike the previous examples, this interface provides a view into the controller's operation, including running tasks, network connections, memory usage and diagnostic information. Rockwell Automation's CompactLogix controllers are commonly used to automate industrial processes, from manufacturing and packaging lines to water treatment and material handling systems. Depending on their configuration, these controllers can operate motors, pumps, valves and other physical equipment.

Although in this example the interface shown does not provide direct control over the PLC's logic, it exposes information about the model, firmware, network layout, and connections, which is useful reconnaissance for a bigger attack.

The concern is not necessarily that every exposed interface allows an attacker to immediately manipulate industrial processes. However, these devices are accessible from the public internet in the first place, providing information about systems that may be responsible for controlling physical equipment.

The attacks on refrigeration systems in France highlight the consequences of poorly secured industrial devices. As demonstrated throughout this research, publicly accessible management interfaces are not limited to refrigeration controllers but extend to PLCs and other systems used to manage industrial equipment.

Finding these devices requires little effort. Internet scanners make it possible to identify publicly exposed interfaces quickly, and in some cases these interfaces rely on publicly documented default credentials or have no authentication at all. As attackers increasingly use AI tools, such attacks may be identified and carried out faster and at a greater scale.

The consequences of this access depend on the device and the functionality exposed. While some interfaces provide little more than diagnostic information, others allow users to modify configurations, disrupt communications, or change settings that affect physical equipment. In industrial environments, this can result in operational downtime, damaged equipment and financial losses.

Weak passwords, default credentials and exposed administrative interfaces are not new security issues. Yet their continued presence on internet-connected industrial systems demonstrates how easily preventable weaknesses can leave physical operations vulnerable to attack.

Exposed devices often come from third-party suppliers and contractors, who frequently install refrigeration controllers, gateways and PLCs without the organization ever being aware they are connected to the internet. Asset visibility remains an important foundation, but organizations should assume that some unknown assets and services will exist. Effective security therefore also depends on continuous monitoring, anomaly detection, network segmentation and rapid response, so that risky exposure and unusual activity can be identified and addressed before it becomes disruptive.

Darktrace helps security teams gain visibility across IT and OT and respond to risks: https://www.darktrace.com/products/operational-technology.

Copeland, Dixell, WAGO, Rockwell Automation, Allen-Bradley and CompactLogix are trademarks of their respective owners. Their use here is for identification only and does not imply any affiliation with or endorsement by those owners.

References

1.        https://claroty.com/team82/research/freeze-the-controller-defrost-the-food-uncovering-vulnerabilities-in-danfoss-refrigeration-controllers

‍

Continue reading
About the author
Tara Gould
Malware Research Lead

Blog

/

Network

/

October 20, 2026

Why Kubernetes Security Needs A Behavioral Approach

Default blog imageDefault blog image

Kubernetes has become a foundational technology for modern cloud-native infrastructure. It can orchestrate everything from customer-facing applications to data pipelines and, increasingly, AI workloads. This is due to the highly scalable nature of Kubernetes, making it cost efficient and reliable.

Gartner projects that by 2028, 95% of new AI deployments will run on Kubernetes, up from less than 30% today1. However, the same qualities that make Kubernetes powerful also make it uniquely challenging to secure, leaving many organizations with limited visibility into their clusters. This lack of visibility increases risk - it’s difficult to secure what you can’t see.

However, this risk is often looked at as purely a posture management challenge. While Cloud Security Posture Management (CSPM) solutions can tell you how a cluster should be configured, they don’t tell you what it is doing in real time. This requires runtime behavioral monitoring of clusters to detect active threats.

Yet existing approaches to securing Kubernetes by most Cloud Detection and Response (CDR) and Network Detection and Response (NDR) vendors often have limited coverage or require a third-party integration to forward traffic and metadata. This means that capabilities can be limited and detections can lack the context of the wider environment, which slows down triage, investigation and response.

Why Kubernetes is hard to secure

Traditional security tooling was built for persistent, long-term infrastructure such as servers and virtual machines with fixed identities that last months or years. Some elements of Kubernetes environments can have a much shorter lifespan, breaking many assumptions those tools rely on:

  1. Increased complexity
    1. Kubernetes environments can be complex in comparison to traditional infrastructure, with many different elements to consider such as nodes, clusters, containers, pods and the workloads themselves.
  2. Ephemeral by design
    1. Kubernetes pods can spin up and terminate in seconds. A container that was compromised may no longer exist by the time a deeper investigation is required, leaving no artifact to analyze. This poses a unique challenge for security operations.
  3. Weaker workload boundaries
    1. Kubernetes containers use lightweight, shared-kernel isolation rather than separate virtual machines, improving efficiency but reducing separation between workloads and the host. As a result, a compromised container may have a greater potential impact on the node and neighboring workloads than a compromised virtual machine (VM).2
    2. Recent vulnerabilities have shown that threat actors can escape a container and gain access to the underlying host.3, 4
  4. Everything is API-driven
    1. Dozens or hundreds of microservices communicate over internal APIs, and the Kubernetes control plane itself is an API. This creates a large and dynamic attack surface, making it difficult to distinguish legitimate activity from malicious behavior.
    2. Without identity-aware runtime visibility, threat actors can move laterally between pods and exploit misconfigured role-based access controls (RBAC) with little chance of detection.

Rising to the challenge of securing Kubernetes

With the release of Darktrace 7.2, Darktrace / HYBRID NETWORK can extend behavioral security into Kubernetes, delivering visibility, detection and response capabilities across runtime activity. Darktrace addresses the challenges of securing Kubernetes environments in several ways.

Behavioral detection, not static rules

Many Kubernetes runtime tools rely on predefined rules and signatures - a fixed list of “bad” behaviors to match against. That approach struggles with novel attacks and generates noise at scale. Darktrace takes a different approach: our Adaptive AI builds a Unique Behavioral Profile for each organization from the ground up, learning the normal behavior of every workload, node and identity in your cluster. It then uses this understanding to detect meaningful deviations from expected behavior, which serves as the basis for surfacing suspicious and malicious activity.  

A pod reaching out to an unfamiliar destination, an unexpected executable launching inside a workload, or an identity performing an action it has never performed, all surface as anomalies without anyone having to write a rule in advance.

Darktrace surfaces unexpected Kubernetes activity that does not align with an organization’s Unique Behavioral Profile

‍

Visibility across the network, the process, and the control plane

Darktrace monitors Kubernetes at three levels that together provide comprehensive runtime visibility:

  1. Network-layer
    1. Traffic between pods, across nodes and out to the internet. This helps to detect lateral movement and unusual connectivity, whether internal or external.
  2. Process-layer
    1. What is executing inside your containers. If a process reads a sensitive credential file or spawns an unexpected shell inside a production pod, Darktrace can identify that activity and use it as context for further detections.
  3. Control plane
    1. Activity against the Kubernetes API itself: someone executes a command in a running pod, RBAC changes, or unusual administrative actions. Many high-impact security events involve the control plane, an area that is often less visible to solutions that only analyze or scan network-layer telemetry.

Misconfiguration and vulnerability awareness

Runtime detection is strongest when combined with posture and exposure management. Darktrace surfaces risky pod configurations, such as privileged containers, host networking, and over-permissive service accounts as workloads are deployed, and links CVEs from images to the workloads running them. Security teams can go beyond simply knowing that a vulnerability exists and see exactly where it is in the cluster. When combined with behavior-based detections, this provides invaluable context that can help prioritize incidents and proactively identify areas that need further attention. For example, anomalous workload behavior on its own might not be very interesting to a security team at first, but if that same workload is known to host sensitive data, is known to have critical vulnerabilities associated with it, or is publicly accessible, then this narrative becomes much more suspicious and demanding of their attention.

Darktrace highlights Kubernetes misconfigurations such as privileged containers, host networking, and missing security controls, helping teams prioritize runtime alerts with valuable exposure and risk context

‍

Response, tailored to Kubernetes

Detection is most effective when paired with response. Darktrace can take precise actions on Kubernetes workloads, including blocking a malicious connection at the pod level, helping to safely contain threats while security teams investigate. Response actions can be taken fully autonomously or via a human-in-the-loop process, which is completely configurable depending on the desired level of autonomy and scope. Darktrace is expanding the number of Kubernetes-native response actions to give customers even more flexibility when containing threats.

Forensics that survive the workload

Because containers are so short-lived, evidence disappears fast and this can make it very difficult to answer questions related to the initial detections. Darktrace solves this with Forensic Acquisition & Investigation capabilities that extend to Kubernetes containers, allowing investigations to be triggered manually by an analyst, or automatically from an alert, preserving evidence before the workload is gone.

For incident responders, this addresses a key challenge in container security: being able to answer “what actually happened?” after an ephemeral workload has vanished.

One platform, not another silo

Kubernetes doesn't exist in isolation, and neither should its security. Darktrace correlates cluster activity with the surrounding cloud environment, including AWS, Azure and GCP identities, network activity and cloud assets, allowing analysts to pivot between a Kubernetes workload and its wider context. A single anomalous action can be traced from the control plane to the pod, and from the pod to the cloud identity behind it, all within a single investigation workflow. This can also include alerts from other areas of hybrid infrastructure or third-party sources where relevant, adding more context to investigations and speeding up workflows for human analysts.

Built to deploy easily

Operational complexity can be a barrier to security adoption. Darktrace / HYBRID NETWORK for Kubernetes installs with a single command, and for teams that manage their infrastructure as code, it supports raw manifests so the deployment can live in a Git repository and be managed through existing GitOps workflows. The same deployment approach works across Kubernetes environments in AWS, Azure, Google Cloud, and on-premises infrastructure. Early adopters of Darktrace’s Kubernetes capabilities have highlighted the simplicity of deployment and time to value.

Darktrace can deploy across your Kubernetes environment and wider hybrid infrastructure, while also providing visibility into Kubernetes deployment structure and status

Next steps

Kubernetes environments are dynamic, ephemeral and API-driven in ways that challenge traditional security tooling. With Kubernetes usage increasing due to widespread AI adoption, it is essential to secure Kubernetes environments as part of securing what AI can do. Darktrace / HYBRID NETWORK addresses these challenges by learning normal behavior in Kubernetes environments across network, process and control-plane layers, identifying meaningful threats and enabling targeted response. Combined with the context of wider hybrid infrastructure and forensic investigation capabilities, Darktrace provides visibility into both emerging threats and active incidents.

To see how Darktrace can help secure your Kubernetes environment and your wider hybrid infrastructure, get in touch to arrange your free trial. Existing customers can contact their Darktrace Account Team for more information about deployment options and licensing.

‍

Sources

[1] Gartner, Container Management research, 2025: "By 2028, 95% of new AI deployments will use Kubernetes, up from less than 30% today."  https://www.gartner.com/en/documents/7913109. (quote reproduced under Gartner reprint at https://www.kubermatic.com/magic-quadrant-and-critical-capabilities-for-container-management/)

[2] Palo Alto Networks (Unit 42) – Container Breakouts: Escape Techniques in Cloud Environments. https://unit42.paloaltonetworks.com/container-escape-techniques/

[3] runC container-escape vulnerabilities (CVE-2025-31133, CVE-2025-52565, CVE-2025-52881), disclosed by a SUSE researcher on November 5, 2025. https://www.securityweek.com/runc-vulnerabilities-can-be-exploited-to-escape-containers/

[4] NVIDIAScape (CVE-2025-23266), CVSS 9.0. https://nvidia.custhelp.com/app/answers/detail/a_id/5659

[related-resource]

‍

Continue reading
About the author
Elise Longhurst
Senior Product Executive
あなたのデータ × DarktraceのAI
唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ