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August 18, 2020

Evil Corp's WastedLocker Ransomware Attacks Observation

Darktrace detects Evil Corp intrusions with WastedLocker ransomware. Learn how AI spotted malicious activity, from initial intrusion to data exfiltration.
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
Max Heinemeyer
Global Field CISO
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18
Aug 2020

Darktrace has recently observed several targeted intrusions associated with Evil Corp, an advanced cyber-criminal group recently in the headlines after a surge in WastedLocker ransomware cases. The group is believed to have targeted hundreds of organizations in over 40 countries, demanding ransoms of $500,000 to $1m to unlock computer files it seizes. US authorities are now offering a $5m reward for information leading to the arrest of the group’s leaders — understood to be the largest sum of money ever offered for a cyber-criminal.

Thanks to its self-learning nature, Darktrace's AI detected these intrusions without the use of any threat intelligence or static Indicators of Compromise (IoCs). This blog describes the techniques, tools and procedures used in multiple intrusions by Evil Corp – also known as TA505 or SectorJ04.

Key takeaways

  • The threat actor was reusing TTPs as well as infrastructure across multiple intrusions
  • Some infrastructure was only observed in individual intrusions
  • While most WastedLocker reports focus on the ransomware, Darktrace has observed Evil Corp conducting data exfiltration
  • The attacker used various ‘Living off the Land’ techniques for lateral movement
  • Data exfiltration and ransomware activity took place on weekends, likely to reduce response capabilities of IT teams
  • Although clearly an advanced actor, Evil Corp can be detected and stopped before encryption ensues

Evil Corp ransomware attack

Figure 1: The standard attack lifecycle observed in Evil Corp campaigns

Initial intrusion

While Evil Corp is technically sophisticated enough to choose from an array of initial intrusion methods, fake browser updates were the weapon of choice in the observed campaign. These were delivered from legitimate websites and used social engineering to convince users to download these malicious ‘updates’. Evil Corp has actually built a framework around this capability, referred to as SocGholish.

Establishing foothold / Command & Control Traffic

Darktrace detected different C2 domains being contacted after the initial infection. These domains overlap across various victims, showing that the attacker is reusing infrastructure within the same campaign. The C2 communication – comprised of thousands of connections over several days – took place over encrypted channels with valid SSL certificates. No single infected device ever beaconed to more than one C2 domain at a time.

Two example C2 domains are listed below with more details:

techgreeninc[.]com

SSL beacon details:

  • Median beacon period: 3 seconds
  • Range of periods: 1 seconds - 2.58 minutes
  • Data volume sent per connection on average: 921 Bytes

investimentosefinancas[.]com

SSL beacon details:

  • Median beacon period: 1.7 minutes
  • Range of periods: 1 seconds - 6.68 minutes
  • Data volume sent per connection on average: 935 Bytes

Certificate information:

  • Subject: CN=investimentosefinancas.com
  • Issuer: CN=Thawte RSA CA 2018,OU=www.digicert.com,O=DigiCert Inc,C=US
  • Validation status: OK

Note in particular the median beacon period, which indicates that some C2 channels were much more hands-on, whilst others possibly acted as backup channels in case the main C2 was burned or detected. It’s also interesting to see the low amount of data being transferred to the hands-on C2 domains. The actual data exfiltration took place to yet another C2 destination, intentionally separated from the hands-on intrusion C2s. All observed C2 websites were recently registered with Russian providers and are not responsive (see below).

Figure 2: The unresponsive C2 domain

Registrar: reg.ru

Created: 2020-06-29 (6 weeks ago) | Updated: 2020-07-07 (5 weeks ago)

Figure 3: Some key information relating to the C2 domain

Darktrace’s Cyber AI Platform detected this Command & Control activity via various behavioral indicators, including unusual beaconing and unusual usage of TLS (JA3).

Internal reconnaissance

In some cases, Darktrace witnessed several days of inactivity between establishing C2 and internal reconnaissance. The attackers used Advanced Port Scanner, a common IT tool, in a clear attempt to blend in with regular network activity. Several hundred IPs and dozens of popular ports were scanned at once, with tens of thousands of connections made in a short period of time.

Some key ports scanned were: 21, 22, 23, 80, 135, 139, 389, 443, 445, 1433, 3128, 3306, 3389, 4444, 4899, 5985, 5986, 8080. Darktrace detected this anomalous behavior easily as the infected devices don’t usually scan the network.

Lateral movement

Different methods of lateral movement were observed across intrusions, but also within the same intrusion, with WMI used to move between devices. Darktrace detected this by identifying when WMI usage was unusual or new for a device. An example of the lateral movement is shown below, with Darktrace detecting this as ‘New Activity’.

Figure 4: The model breach event log

PsExec was used where it already existed in the environment and Darktrace also witnessed SMB drive writes to hidden shares to copy malware, e.g.

C$ file=Programdata\[REDACTED]4rgsfdbf[REDACTED]

A malicious Powershell file was downloaded – partly shown in the screenshot below.

Figure 5: The malicious Powershell file

Accomplish mission – Data exfiltration or ransomware deployment

Evil Corp is currently best known for its WastedLocker ransomware. Whilst some of its recent intrusions have seen ransomware deployments, others have been classic cases of data exfiltration. Darktrace has not yet observed a double-threat – a case of exfiltration followed by ransomware.

The data exfiltration took place over HTTP to generic .php endpoints under the attacker’s control.

How Cyber AI Analyst reported on WastedLocker

When the first signs of anomalous activity were picked up by Darktrace’s Enterprise Immune System, Cyber AI Analyst automatically launched a full investigation and quickly provided a full overview of the overall incident. The AI Analyst continued to add more details to the ongoing incident as it evolved. There were a total of six AI Analyst incidents for the week spanning an example Evil Corp intrusion – and two of them directly covered the Evil Corp attack. In stitching together disparate security events and presenting a single narrative, Cyber AI Analyst did all the heavy lifting for human security staff, who could look at just a handful of fully-investigated incidents, instead of having to triage countless individual model breaches.

Figure 6: Cyber AI Analyst’s overview of the incident

Note how AI Analyst covers five phases of the attack lifecycle in a single incident report:

  1. Unusual Repeated Connections – Initial C2
  2. Possible HTTP Command & Control Traffic – Further C2
  3. Possible SSL Command & Control Traffic – Further C2
  4. Scanning of Multiple Devices – Internal reconnaissance with Advanced IP Scanner
  5. SMB Writes of Suspicious Files – Lateral Movement

Evil Corp rising

Every indicator suggests that this was not a case of indiscriminate ransomware, but rather highly sophisticated and targeted attacks by an advanced threat actor. With the ultimate goal of ransoming operations, the attacker moved towards the crown jewels of the organization: file servers and databases.

The organizations involved in the above analysis did not have Darktrace Antigena – Darktrace’s Autonomous Response technology – in active mode, and the threat was therefore allowed to escalate beyond its initial stages. With Antigena in full operation, the activity would have been contained at its early stages with a precise and surgical response which would have stopped the malicious behavior whilst allowing the business to operate as normal.

Despite the targeted and advanced nature of the threat, security teams are perfectly capable of detecting, investigating, and stopping the threat with Cyber AI. Darktrace was able to not only detect WastedLocker ransomware based on a series of anomalies in network traffic, but also stitch together those anomalies and investigate the incident in real time, presenting an actionable summary of the different attack stages without flooding the security team with meaningless alerts.

Learn more about Autonomous Response

Network IoCs:

IoCCommenttechgreeninc[.]comC2 domaininvestimentosefinancas[.]comC2 domain

Selected associated Darktrace model breaches:

  • Compromise / Beaconing Activity To External Rare
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / Suspicious Beaconing Behaviour
  • Device / New or Unusual Remote Command Execution
  • Compromise / Beaconing Activity To External Rare
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Slow Beaconing Activity To External Rare
  • Device / New User Agent
  • Unusual Activity / Unusual Internal Connections
  • Device / Suspicious Network Scan Activity
  • Device / Network Scan
  • Device / Network Scan - Low Anomaly Score
  • Device / ICMP Address Scan
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Compromise / SSL Beaconing to Rare Destination
  • Anomalous Connection / SMB Enumeration
  • Compliance / SMB Drive Write
  • Anomalous File / Internal / Unusual SMB Script Write

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
Max Heinemeyer
Global Field CISO

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May 26, 2026

Journey of a Threat: How Multi-Layered AI Works in Darktrace / EMAIL

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Darktrace / EMAIL is an implementation of the Darktrace methodology – a multi-layered AI system built into a single product. As with other Darktrace products, Darktrace / EMAIL learns the expected behaviours of an organization and its employees to identify novel threats and anomalous activity.

The diagram below represents the architecture of Darktrace / EMAIL’s multi-layered AI: a structured visualization of how intelligence is built, step by step, from raw data to actionable insight. Each layer plays a distinct role, feeding into the next: collecting data, understanding behaviour, analysing intent, making decisions, and presenting clear outcomes.

It all starts with an email

In this blog, we’ll follow a malicious email as it passes through the Darktrace / EMAIL system, showing exactly what happens as it travels through each layer of the pyramid, from basic data extraction to AI-powered metric creation, and finally deciding on any autonomous actions.

Let’s take this example email. As an end-user, you can see that this is an obvious extortion attempt where an adversary is threatening legal action if money isn’t paid within 24 hours, but how does Darktrace figure that out?

Part 1: Data Gathering

Processing of an email begins on point-of-transit for all inbound, outbound, or lateral emails. The first step is to extract information directly. This includes taking information from the headers (such as sending and receiving addresses, sender IP address, routing, and authentication protocols), as well as extraction of raw HTML and CSS data from the email itself.

This directly extracted information only allows for immediate surface level analysis, such as identifying signature-based attacks (known malicious addresses / domains), but is insufficient for identifying novel threats, complex attacks, or potential email or vendor compromise. This is where Darktrace’s AI analysis shines.

In this example, the SPF, DKIM, and DMARC authentication all passed successfully, showing that even malicious emails can still bypass these signature-based checks. Even with this success, Darktrace will continue to analyse the email.

Diving deeper into the technical information, we can see further information extracted from the headers, including aggregations from the header information, historical calculations such as the frequency and volume of emails to and from a particular domain, and much more.

Part 2: Social Graphing

Social Graphing involves the analysis of sending and receiving behaviours of different mailboxes to create peer-groups. Mailboxes who often send and receive to and from the same mailboxes, or exhibit other correlated behaviours, will be clustered together using a collection of unsupervised AI clustering systems. These groups may represent uses in the same teams who perform similar activity, groups of external facing mailboxes which often receive unsolicited emails, or groups of VIP users (such as C-suite or executives).

Social graphing is an essential component of Darktrace’s pattern of life analysis. This clustering allows Darktrace to understand the responsibilities of individuals – for example, behaviours which are anomalous for one group of users may be completely expected of another group.

In our example, the email was sent to 3 different users within the organization. As part of the social graphing, an “Association Anomaly” is calculated which indicates the likelihood that these users would receive emails from this user or domain, based on historical patterns.

Part 3: Metric Calculation

Metrics are calculated for every email, representing more complex characteristics of an email which can’t be directly extracted. Darktrace / EMAIL features over 1000 unique metrics, calculated both algorithmically and using an ensemble of AI systems.

Algorithmically calculated (non-AI) metrics include further historical calculations, and counts of features such as code blocks, and hidden text, to name a few.

AI-driven metrics include Inducement Classification which uses Natural Language Processing to identify potential phishing, solicitation, or extortion attempts; Named Entity Recognition to identify PII and other sensitive data within an email to support Data Loss Prevention; and many more.

We can follow our example email through this process and view the outcome of these metric calculations. Looking at the language metrics for this email, we can see that our email has reported a high extortion inducement, along with identification of banking information and language indicating urgency.

Part 4: Evaluation and Combination Engine (models)

Once all metrics have been calculated for an email, it gets sent to an evaluation and combination engine where the metrics are compared against blocks of logic to determine if an email contains a threat. One key model which alerted for this example message was a model to tag and block extortion attempts.

Since our example email has a high inducement score for extortion, along the presence of a bitcoin wallet address in the message, this model alerts. When a model in the engine is activated, actions are taken – in this case adding a tag to the email to flag it as extortion in the console and hold the email to prevent it from reaching the end-user mailbox.

Part 5: Meta-Modelling and Actions

Once the models have been run, the actions are taken against the email. If the email hasn’t been blocked or held, this is the point where it will reach the end-user's mailbox.

In the Darktrace / EMAIL UI, all actions models which alerted for an email and actions taken as a result can be seen. At the top of this page, you can see the alert indicating an extortion attempt along with the action to hold the message.

Alongside this, a meta-classifier is used to calculate an overall anomaly score for each email, based on how much the email differs from the pattern of life for the user. The score of the email is boosted by any actions that have taken place.

Part 6: Campaign Clustering

All emails are passed through the Darktrace / EMAIL campaign clustering system. This system creates clusters based on related features within the emails to identify groups of emails with the same sender or intent.

In our case, the email was identified as part of a campaign, alongside other emails which were also identified as extortion attempts against a small group of recipients.

Email campaigns may have additional actions applied to them if the campaign is deemed malicious, and in this case, you can see that the autonomous response was to hold all emails in the campaign. This means that if an email manages to avoid being blocked in the evaluation and combination engine but gets identified as part of the campaign, the hold action will be applied to it retroactively.

Part 7: Cyber AI Analyst

Darktrace’s Cyber AI Analyst presents key information and anomaly indicators for each email, such as further information about authentication, specific metrics, or other identified anomalies and mismatches.

Cyber AI Analyst can also utilize data from Darktrace / EMAIL to enhance its investigation of incidents from other Darktrace products, correlating relevant information to build a fuller picture. More information about the Cyber AI Analyst is available in the Darktrace AI Arsenal.

Part 8: Data Presentation (UI)

Once all processing has taken place against the email, it is presented in the Darktrace / EMAIL UI. Here, members of the SOC team can investigate incidents and anomalies, interact with malicious emails to see why they were blocked, and much more.

Our email stands out here with its 100 anomaly score. Every email which passes through a Darktrace / EMAIL will undergo the same thorough and rigorous analysis to identify potential risks, apply autonomous actions where required, and will ultimately be assigned a score to be displayed here. By providing a single overall score in the UI, rather than presenting emails in full, Darktrace / EMAIL allows SOC teams to more easily identify which emails are most important to investigate, increasing efficiency and reducing alert fatigue.

Take the next step

Many email security tools on the market that claim to be AI-driven are in fact bolting AI onto attack-centric approaches, which rely on automating the identification of known threats. These approaches struggle, and will continue to struggle, with adapting to novel, AI-generated threats.

By analyzing every email within its deeply integrated, multi-layered AI system, Darktrace / EMAIL is able to identify the subtle threats that others miss. This depth not only improves detection accuracy, but enables confident, autonomous action, giving security teams clearer insight into AI outcomes and greater control while supporting users.

For a full deep dive into each stage of the AI system, check out the white paper: A Guide to the Multi-Layered AI in Darktrace / EMAIL

Learn more about securing AI in your enterprise.

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May 26, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

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Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

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Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

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Product Marketing Manager, Network Detection & Response
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