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October 10, 2021

AI Uncovered Outlaw's Crypto Mining Operation

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10
Oct 2021
Discover how Darktrace AI technology exposed a hidden cryptocurrency mining scheme. Learn about the power of Darktrace AI in cybersecurity.

Infamy is a paradoxical calling for cyber-criminals. While for some, bragging rights are a motivation for cyber-crime in and of themselves, notoriety is usually not a sensible goal for those hoping to avoid detection. This is what threat actors behind the prolific Emotet botnet learned earlier in 2021, for instance, when a coordinated effort was launched by eight national law enforcement agencies to take down their operation. There are, however, certain names which appear again and again in cyber security media and consistently avoid detection – names like Outlaw.

How Outlaw plans an ambush

Despite being active since 2018, very little is known about the hacking group Outlaw, which has staged numerous botnet and crypto-jacking attacks in China and internationally. The group is recognized by a variety of calling cards, be they repeated filenames or a tendency to illicitly mine Monero cryptocurrency, but its success ultimately lies in its tendency to adapt and evolve during months of dormancy between attacks.

Outlaw’s attacks are marked by constant changes and updates, which they work on in relative silence, before targeting security systems which are too-often defeated by the unfamiliarity of the threat.

In 2020, Outlaw gained attention when they updated their botnet toolset to find and eradicate other criminals’ crypto-jacking software, maximizing their own payout from infected devices. While it might come as no surprise that there’s no honor among cyber-thieves, this update also implemented more troubling changes which allowed Outlaw’s malware to evade traditional security defenses.

By switching disguises between each big robbery, and laying low with the loot, Outlaw ensures that traditional security systems which rely on historical attack data will never be ready for them, no matter how much notoriety is attached to their name. When organizations move beyond these systems’ rules-based approaches, however, adopting Self-Learning AI to protect their digital estates, they can begin to turn the tables on groups like Outlaw.

This blog explores how two pre-infected zombie devices in two very different parts of the world were activated by Outlaw’s botnet in the summer of 2021, and how Darktrace was able to detect the activity despite the devices being pre-infected.

Bounty hunting: First signs of attack

Figure 1: Timeline of the attack.

When a new device was added to the network of a Central American telecomms company in July, Darktrace detected a series of regular connections to two suspicious endpoints which it identified as beaconing behavior. The same behavior was noticed independently, but almost simultaneously, at a financial company in the APAC region, which was implementing Darktrace for the first time. Darktrace’s Self-Learning AI was able to identify the pre-infected devices by clustering similarly-behaving devices into peer groups within the local digital estates and therefore recognize that both were acting unusually based on a range of behaviors.

The first sign that the zombie devices had been activated by Outlaw was the initiation of cryptocurrency mining. Both devices, despite their geographical distance, were discovered to be connected to a single crypto-account, exemplifying the indiscriminate and exponential nature by which a botnet grows.

Outlaw has in the past restricted its activities to devices within China in what was assumed to be a show of caution, but recent activities like this one speak to a growing confidence.

The botnet recruitment process

The subsequent initiation of Internet Relay Chat (IRC) connections across port 443, a port more often associated with HTTPS activity, was perfectly characteristic of the Outlaw botnet’s earlier activity in 2020. IRC is a tool regularly used for communication between botmasters and zombie devices, but by using port 443 the attacker was attempting to blend into normal Internet traffic.

Soon after this exchange, the devices downloaded a shell script. Darktrace’s Cyber AI Analyst was able to intercept and recreate this shell script as it passed through the network, revealing its full function. Intriguingly, the script identified and excluded devices utilizing ARM architecture from the botnet. Due to its notably low battery consumption, ARM architecture is used primarily by portable mobile devices.

This selectivity is evidence that malicious crypto-mining remains Outlaw’s primary objective. By circumventing smaller devices which offer limited crypto-mining capabilities, this shell script focuses the botnet on the most high-powered, and therefore profitable, devices, such as desktop computers and servers. In this way, it reduces the Indicators of Compromise (IOCs) left behind by the wider botnet without greatly affecting the scale of its crypto-mining operation.

The two devices in question did not employ ARM architecture, and minutes later received a secondary payload containing a file named dota3[.]tar[.]gz, a sequel of sorts to the previous incarnation of the Outlaw botnet, ‘dota2’, which itself referenced a popular video game of the same name. With the arrival of this file, the devices appear to have been updated with the latest version of Outlaw’s world-spanning botnet.

This download was made possible in part by the attacker’s use of ‘Living off the Land’ tactics. By using only common Linux programs already present on the devices (‘curl’ and ‘Wget’ respectively), Outlaw had avoided having its activity flagged by traditional security systems. Wget, for instance, is ostensibly a reputable program used for retrieving content from web servers, and was never previously recorded as part of Outlaw’s TTPs (Tactics, Techniques, and Procedures).

By evolving and adapting its approach, Outlaw is continually able to outsmart and outrun rules-based security. Darktrace’s Self-Learning AI, however, kept pace, immediately identifying this Wget connection as suspicious and advising further investigation.

Figure 2: Cyber AI Analyst identifies Wget use on the morning of July 15 as suspicious and begins investigating potentially related HTTP connections made on the morning of July 14. In this way, it builds a complete picture of the attack.

The botnet unchained

In the following 36 hours, Darktrace detected over 6 million TCP and SSH connections directed to rare external IP addresses using ports often associated with SSH, such as 22, 2222, and 2022.

Exactly what the botnet was undertaking with these connections can only be speculated on. The devices may have been made part of a DDoS (Distributed Denial of Service) attack, bruteforce attempts on targeted SSH accounts, or simply have taken up the task of seeking and infecting new targets, further expanding the botnet. Darktrace recognized that neither device had made SSH connections prior to this event and, had Antigena been in active mode, would have enacted measures to stop them.

Figure 3: The behavior on the device before and after the bot was activated on July 14, 2021. The large spike in model breaches shows clear deviation from the established ‘pattern of life’.

Thankfully, the owners of both devices responded to Darktrace’s detection alerts soon enough to prevent any serious damage to their own digital estates. Had these devices remained under the influence of the botnet, the ramifications may have been far graver.

The use of SSH protocol would have allowed Outlaw to pivot into any number of activities, potentially compromising each device’s network further and causing data or monetary loss to their respective organizations.

Call the sheriff: Self-Learning AI

Rules-based security solutions operate much like the ‘wanted’ posters of the old west, looking out for the criminals who came through town last week without preparing for those riding over the hill today. When black hats and outlaws are adopting new looks and employing new techniques with every attack, a new way of responding to threats is needed.

Darktrace doesn’t need to know the name ‘Outlaw’, or the group’s history of evolving attacks, in order to stop them. With its fundamental self-learning approach, Darktrace learns its surroundings from the ground up, and identifies subtle deviations indicative of a cyber-threat. And with Autonomous Response, it will even take targeted action to neutralize the threat at machine speed, without the need for human intervention.

Thanks to Darktrace analyst Jun Qi Wong for his insights on the above threat find.

Learn more about how Cyber AI Analyst sheds light on complex attacks

Technical details

Darktrace model detections

  • Compliance / Crypto Currency Mining Activity
  • Compromise / High Priority Crypto Currency Mining [Enhanced Monitoring]
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / Zip or Gzip from Rare External Location
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Device / Increased External Connectivity
  • Unusual Activity / Unusual External Activity
  • Compromise / SSH Beacon
  • Compromise / High Frequency SSH Beacon
  • Anomalous Connection / Multiple Connections to New External TCP Port

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
Oakley Cox
Director of Product

Oakley is a Product Manager within the Darktrace R&D team. He collaborates with global customers, including all critical infrastructure sectors and Government agencies, to ensure Darktrace/OT remains the first in class solution for OT Cyber Security. He draws on 7 years’ experience as a Cyber Security Consultant to organizations across EMEA, APAC and ANZ. His research into cyber-physical security has been published by Cyber Security journals and by CISA. Oakley has a Doctorate (PhD) from the University of Oxford.

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February 19, 2025

Darktrace Releases Annual 2024 Threat Insights

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Introduction: Darktrace’s threat research

Defenders must understand the threat landscape in order to protect against it. They can do that with threat intelligence.

Darktrace approaches threat intelligence with a unique perspective. Unlike traditional security vendors that rely on established patterns from past incidents, it uses a strategy that is rooted in the belief that identifying behavioral anomalies is crucial for identifying both known and novel threats.

For Darktrace analysts and researchers, the incidents detected by the AI solution mark the beginning of a deeper investigation, aiming to connect mitigated threats to wider trends from across the threat landscape. Through hindsight analysis, the Darktrace Threat Research team has highlighted numerous threats, including zero-day, n-day, and other novel attacks, showcasing their evolving nature and Darktrace’s ability to identify them.

In 2024, the Threat Research team observed major trends around vulnerabilities in internet-facing systems, new and re-emerging ransomware strains, and sophisticated email attacks. Read on to discover some of our key insights into the current cybersecurity threat landscape.

Multiple campaigns target vulnerabilities in internet-facing systems

It is increasingly common for threat actors to identify and exploit newly discovered vulnerabilities in widely used services and applications, and in some cases, these vulnerability exploitations occur within hours of disclosure.

In 2024, the most significant campaigns observed involved the ongoing exploitation of zero-day and n-day vulnerabilities in edge and perimeter network technologies. In fact, in the first half of the year, 40% of all identified campaign activity came from the exploitation of internet-facing devices. Some of the most common exploitations involved Ivanti Connect Secure (CS) and Ivanti Policy Secure (PS) appliances, Palo Alto Network (PAN-OS) firewall devices, and Fortinet appliances.

Darktrace helps security teams identify suspicious behavior quickly, as demonstrated with the critical vulnerability in PAN-OS firewall devices. The vulnerability was publicly disclosed on April 11, 2024, yet with anomaly-based detection, Darktrace’s Threat Research team was able to identify a range of suspicious behavior related to exploitation of this vulnerability, including command-and-control (C2) connectivity, data exfiltration, and brute-forcing activity, as early as March 26.

That means that Darktrace and our Threat Research team detected this Common Vulnerabilities and Exposure (CVE) exploitation 16 days before the vulnerability was disclosed. Addressing critical vulnerabilities quickly massively benefits security, as teams can reduce their effectiveness by slowing malicious operations and forcing attackers to pursue more costly and time-consuming methods.

Persistent ransomware threats continue to evolve

The continued adoption of the Ransomware-as-a-Service (RaaS) model provides even less experienced threat actors with the tools needed to carry out disruptive attacks, significantly lowering the barrier to entry.

The Threat Research team tracked both novel and re-emerging strains of ransomware across the customer fleet, including Akira, LockBit, and Lynx. Within these ransomware attempts and incidents, there were notable trends in attackers’ techniques: using phishing emails as an attack vector, exploiting legitimate tools to mask C2 communication, and exfiltrating data to cloud storage services.

Read the Annual 2024 Threat Report for the complete list of prominent ransomware actors and their commonly used techniques.

Onslaught of email threats continues

With a majority of attacks originating from email, it is crucial that organizations secure the inboxes and beyond.

Between December 21, 2023, and December 18, 2024, Darktrace / EMAIL detected over 30.4 million phishing emails across the fleet. Of these, 70% successfully bypassed Domain-based Message Authentication, Reporting, and Conformance (DMARC) verification checks and 55% passed through all other existing layers of customer email security.

The abuse of legitimate services and senders continued to be a significant method for threat actors throughout 2024. By leveraging trusted platforms and domains, malicious actors can bypass traditional security measures and increase the likelihood of their phishing attempts being successful.

This past year, there was a substantial use of legitimately authenticated senders and previously established domains, with 96% of phishing emails detected by Darktrace / EMAIL utilizing existing domains rather than registering new ones.

These are not the only types of email attacks we observed. Darktrace detected over 2.7 million emails with multistage payloads.

While most traditional cybersecurity solutions struggle to cover multiple vectors and recognize each stage of complex attacks as part of wider malicious activity, Darktrace can detect and respond across email, identities, network, and cloud.

Conclusion

The Darktrace Threat Research team continues to monitor the ever-evolving threat landscape. Major patterns over the last year have revealed the importance of fast-acting, anomaly-based detection like Darktrace provides.

For example, response speed is essential when campaigns target vulnerabilities in internet-facing systems, and these vulnerabilities can be exploited by attackers within hours of their disclosure if not even before that.

Similarly, anomaly-based detection can identify hard to find threats like ransomware attacks that increasingly use living-off-the-land techniques and legitimate tools to hide malicious activity. A similar pattern can be found in the realm of email security, where attacks are also getting harder to spot, especially as they frequently exploit trusted senders, use redirects via legitimate services, and craft attacks that bypass DMARC and other layers of email security.

As attacks appear with greater complexity, speed, and camouflage, defenders must have timely detection and containment capabilities to handle all emerging threats. These hard-to-spot attacks can be identified and stopped by Darktrace.

Download the full report

Discover the latest threat landscape trends and recommendations from the Darktrace Threat Research team.

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The Darktrace Threat Research Team

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February 18, 2025

Unifying IT & OT With AI-Led Investigations for Industrial Security

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As industrial environments modernize, IT and OT networks are converging to improve efficiency, but this connectivity also creates new attack paths. Previously isolated OT systems are now linked to IT and cloud assets, making them more accessible to attackers.

While organizations have traditionally relied on air gaps, firewalls, data diodes, and access controls to separate IT and OT, these measures alone aren’t enough. Threat actors often infiltrate IT/Enterprise networks first then exploit segmentation, compromising credentials, or shared IT/OT systems to move laterally, escalate privileges, and ultimately enter the OT network.

To defend against these threats, organizations must first ensure they have complete visibility across IT and OT environments.

Visibility: The first piece of the puzzle

Visibility is the foundation of effective industrial cybersecurity, but it’s only the first step. Without visibility across both IT and OT, security teams risk missing key alerts that indicate a threat targeting OT at their earliest stages.

For Attacks targeting OT, early stage exploits often originate in IT environments, adversaries perform internal reconnaissance among other tactics and procedures but then laterally move into OT first affecting IT devices, servers and workstations within the OT network. If visibility is limited, these threats go undetected. To stay ahead of attackers, organizations need full-spectrum visibility that connects IT and OT security, ensuring no early warning signs are missed.

However, visibility alone isn’t enough. More visibility also means more alerts, this doesn’t just make it harder to separate real threats from routine activity, but bogs down analysts who have to investigate all these alerts to determine their criticality.

Investigations: The real bottleneck

While visibility is essential, it also introduces a new challenge: Alert fatigue. Without the right tools, analysts are often occupied investigating alerts with little to no context, forcing them to manually piece together information and determine if an attack is unfolding. This slows response times and increases the risk of missing critical threats.

Figure 1: Example ICS attack scenario

With siloed visibility across IT and OT each of these events shown above would be individually alerted by a detection engine with little to no context nor correlation. Thus, an analyst would have to try to piece together these events manually. Traditional security tools struggle to keep pace with the sophistication of these threats, resulting in an alarming statistic: less than 10% of alerts are thoroughly vetted, leaving organizations vulnerable to undetected breaches. As a result, incidents inevitably follow.

Darktrace’s Cyber AI Analyst uses AI-led investigations to improve workflows for analysts by automatically correlating alerts wherever they occur across both IT and OT. The multi-layered AI engine identifies high-priority incidents, and provides analysts with clear, actionable insights, reducing noise and highlighting meaningful threats. The AI significantly alleviates workloads, enabling teams to respond faster and more effectively before an attack escalates.

Overcoming organizational challenges across IT and OT

Beyond technical challenges like visibility and alert management, organizational dynamics further complicate IT-OT security efforts. Fundamental differences in priorities, workflows, and risk perspectives create challenges that can lead to misalignment between teams:

Non-transferable practices: IT professionals might assume that cybersecurity practices from IT environments can be directly applied to OT environments. This can lead to issues, as OT systems and workflows may not handle IT security processes as expected. It's crucial to recognize and respect the unique requirements and constraints of OT environments.

Segmented responsibilities: IT and OT teams often operate under separate organizational structures, each with distinct priorities, goals, and workflows. While IT focuses on data security, network integrity, and enterprise applications, OT prioritizes uptime, reliability, and physical processes.

Different risk perspectives: While IT teams focus on preventing cyber threats and regulatory violations, OT teams prioritize uptime and operational reliability making them drawn towards asset inventory tools that provide no threat detection capability.

Result: A combination of disparate and ineffective tools and misaligned teams can make any progress toward risk reduction at an organization seem impossible. The right tools should be able to both free up time for collaboration and prompt better communication between IT and OT teams where it is needed. However, different size operations structure their IT and OT teams differently which impacts the priorities for each team.

In real-world scenarios, small IT teams struggle to manage security across both IT and OT, while larger organizations with OT security teams face alert fatigue and numerous false positives slowing down investigations and hindering effective communication with the IT security teams.

By unifying visibility and investigations, Darktrace / OT helps organizations of all sizes detect threats earlier, streamline workflows, and enhance security across both IT and OT environments. The following examples illustrate how AI-driven investigations can transform security operations, improving detection, investigation, and response.

Before and after AI-led investigation

Before: Small manufacturing company

At a small manufacturing company, a 1-3 person IT team juggles everything from email security to network troubleshooting. An analyst might see unusual traffic through the firewall:

  • Unusual repeated outbound traffic from an IP within their OT network destined to an unidentifiable external IP.

With no dedicated OT security tools and limited visibility into the industrial network, they don’t know what the internal device in question is, if it is beaconing to a malicious external IP, and what it may be doing to other devices within the OT network. Without a centralized dashboard, they must manually check logs, ask operators about changes, and hunt for anomalies across different systems.

After a day of investigation, they concluded the traffic was not to be expected activity. They stop production within their smaller OT network, update their firewall rules and factory reset all OT devices and systems within the blast radius of the IP device in question.

After: Faster, automated response with Cyber AI Analyst

With Darktrace / OT and Cyber AI Analyst, the IT team moves from reactive, manual investigations to proactive, automated threat detection:

  • Cyber AI Analyst connects alerts across their IT and OT infrastructure temporally mapping them to attack frameworks and provides contextual analysis of how alerts are linked, revealing in real time attackers attempting lateral movement from IT to OT.
  • A human-readable incident report explains the full scope of the incident, eliminating hours of manual investigation.
  • The team is faster to triage as they are led directly to prioritized high criticality alerts, now capable of responding immediately instead of wasting valuable time hunting for answers.

By reducing noise, providing context, and automating investigations, Cyber AI Analyst transforms OT security, enabling small IT teams to detect, understand, and respond to threats—without deep OT cybersecurity expertise.

Before: Large critical infrastructure organization

In large critical infrastructure operations, OT and IT teams work in separate silos. The OT security team needs to quickly assess and prioritize alerts, but their system floods them with notifications:

  • Multiple new device connected to the ICS network alerts
  • Multiple failed logins to HMI detected
  • Multiple Unusual Modbus/TCP commands detected
  • Repeated outbound OT traffic to IT destinations

At first glance, these alerts seem important, but without context, it’s unclear whether they indicate a routine error, a misconfiguration, or an active cyber-attack. They might ask:

  • Are the failed logins just a mistake, or a brute-force attempt?
  • Is the outbound traffic part of a scheduled update, or data exfiltration?

Without correlation across events, the engineer must manually investigate each one—checking logs, cross-referencing network activity, and contacting operators—wasting valuable time. Meanwhile, if it’s a coordinated attack, the adversary may already be disrupting operations.

After: A new workflow with Cyber AI Analyst

With Cyber AI Analyst, the OT security team gets clear, automated correlation of security events, making investigations faster and more efficient:

  • Automated correlation of OT threats: Instead of isolated alerts, Cyber AI Analyst stitches together related events, providing a single, high-confidence incident report that highlights key details.
  • Faster time to meaning: The system connects anomalous behaviors (e.g., failed logins, unusual traffic from an HMI, and unauthorized PLC modifications) into a cohesive narrative, eliminating hours of manual log analysis.
  • Prioritized and actionable alerts: OT security receives clear, ranked incidents, immediately highlighting what matters most.
  • Rapid threat understanding: Security teams know within minutes whether an event is a misconfiguration or a cyber-attack, allowing for faster containment.

With Cyber AI Analyst, large organizations cut through alert noise, accelerate investigations, and detect threats faster—without disrupting OT operations.

An AI-led approach to industrial cybersecurity

Security vendors with a primary focus on IT may lack insight into OT threats. Even OT-focused vendors have limited visibility into IT device exploitation within OT networks, leading to failed ability to detect early indicators of compromise. A comprehensive solution must account for the unique characteristics of various OT environments.

In a world where industrial security is no longer just about protecting OT but securing the entire digital-physical ecosystem as it interacts with the OT network, Darktrace / OT is an AI-driven solution that unifies visibility across IT, IoT and OT, Cloud into one cohesive defense strategy.

Whether an attack originates from an external breach, an insider threat, a supply chain compromise, in the Cloud, OT, or IT domains Cyber AI Analyst ensures that security teams see the full picture - before disruption occurs.

Learn more about Darktrace / OT 

  • Unify IT and OT security under a single platform, ensuring seamless communication and protection for all interconnected devices.
  • Maintain uptime with AI-driven threat containment, stopping attacks without disrupting production.
  • Mitigate risks with or without patches, leveraging MITRE mitigations to reduce attack opportunities.

Download the solution brief to see how Darktrace secures critical infrastructure.

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About the author
Daniel Simonds
Director of Operational Technology
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