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February 12, 2018

The Rise of Cryptocurrency Attacks & Cyber Defense Solutions

Darktrace can detect cryptocurrency-related attacks with machine learning. Identify nefarious use of resources and protect against Coinhive drive-by mining.
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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12
Feb 2018

Prelude

The last 12 months have shown tremendous volatility in the value of cryptocurrencies, of which Bitcoin is the most prominent example. At the start of 2017, Bitcoin lingered around the $2,000 mark before suddenly taking off, climbing to historic highs of close to $20,000 in December 2017. Demand has since subsided, and at the time of writing, the price of Bitcoin is near to $10,772.

While Bitcoin is the most popular cryptocurrency, numerous alternatives, often called ‘altcoins’ have emerged and grown in value in the last 12 months. For example, Dogecoin, originally created to be a spoof cryptocurrency after a widespread internet meme, reached a notable market capitalization milestone of $2bn in January 2018.

Nowadays it is almost impossible to profitably mine Bitcoin on commodity hardware such as laptops, smartphones or desktop computers. At this late state, it just takes too long to perform the relevant calculations, and the cost of electricity is higher than the anticipated revenue in most cases. Other altcoins such as Monero use different algorithms, making them viable alternatives for aspiring crypto miners. It is often still feasible to mine altcoins on commodity hardware and see a return on investment.

The value of most altcoins is closely tied to the value of Bitcoin and, in many cases, the relationship is broadly proportional – a rise in Bitcoin prompting a similar lift in the altcoins. Monero, which has been rapidly adopted by Darknet markets, has profited from this effect. While Monero was valued at around $10 in January 2017, its price has been pumped up to $419 a year later.

There is much that is still not clear about the cryptocurrency phenomenon. Debate as to its relative value and its status as a currency rages, and will not be resolved any time soon. However, from a cyber security perspective there can be no doubt that the combination of altcoins being mineable on commodity hardware, the fact that mining is now becoming profitable as a side-effect of Bitcoin’s rise, and a maturity in cryptocurrency-related tech has led to a surge in cryptocurrency-related attacks.

Attack vectors

Darktrace has observed an abrupt increase of cryptocurrency-related attacks over the last 12 months. Both the frequency and the diversity of these attacks has grown significantly and largely mirrors the remarkable rise in the value of Bitcoin over that period.

Previously, cyber-criminals monetized their operations via banking Trojans/credit card fraud, selling stolen data and ransomware on the Darknet. However, criminals are notoriously adaptable and will follow the money wherever it leads, leading to an increase in cryptojacking’s popularity.

Cryptocurrency mining might not be as profitable as ransomware is upfront, but it can be secretly pursued for months without creating the havoc that characterizes ransomware attacks. Most users and security products might not notice a cryptocurrency miner being installed on a corporate device as it does not show obvious threats or messages to a user, except for an occasional increase in CPU or RAM usage.

Identifying these attacks can be very difficult for traditional security tools as they were not originally designed to catch this type of threat. Nor was Darktrace, but its approach – which relies on its evolving understanding of patterns of behavior – means that it can detect such attacks without having to know what to look for in advance.

Darktrace has detected a number of different attack vectors related to cryptocurrency attacks.

  1. Nefarious use of corporate resources
    Darktrace has detected a range of incidents where employees were intentionally installing cryptocurrency mining software on their corporate devices to mine for personal gain. These employees do not have to pay for the electricity used to run the corporate device in the office – they are basically turning their employer’s electricity into cash by commandeering it for mining operations.

    This is commonly seen as a compliance breach and increases the attack surface of a device that has mining software installed. It puts the corporate device at risk and also increases operational costs as the power consumption usually goes up for mining devices. The most popular cryptocurrency choices for this kind of mining in the last 12 months were Etherium and Monero – altcoins that can profitably be mined without the need for inordinate electricity.
  2. Coinhive drive-by mining
    Coinhive is a technology that allows website owners to use their visitors’ computing power to mine a tiny fraction of cryptocurrency for the website owner. Visitors will experience a small increase in computer resource consumption while browsing the website. Some websites experiment with this model to create new forms of revenue streams alternative to advertisement and banner placements.

    Coinhive usage is often not an opt-in process. Darktrace has observed various customer devices that regularly visit websites leveraging Coinhive technology. While the power consumption increase for a device browsing a website with Coinhive is ultimately negligible, the cumulative effect of a sizeable portion of the workforce unwittingly browsing websites using Coinhive results in increased power consumption cost for the organization as a whole.
  3. Malicious insider
    A malicious insider compromised his employer’s website to put a Coinhive script on there. This then mined Monero for every visitor on the employer’s website for the malicious insider’s personal gain.
  4. Traditional malware
    Cyber criminals are constantly looking to improve the return on investment of their operations. Reports suggest that criminals are starting to adjust their monetization methods based on the financial means of their targets. Suppose you can’t pay the fee extorted in a ransomware attack? They’ll just install a crypto miner on your device instead to ensure that the attack is not completely fruitless.

    As malware authors become more sophisticated, they often deploy multi-staged malware that can swap weaponized payloads. Once malware has infected a system successfully, its authors can often decide what actions to take next. Encrypt the device and extort a ransom? Install a banking Trojan to harvest credit card details? Install more spyware modules to look for data exfiltration? Or, now, install a cryptocurrency miner.

    These pieces of malware operate stealthily and often go undetected for several weeks. An infection might start with a phishing email that contains a macro-enabled document. As soon as a user enabled the macro, the malware will download a file-less stager that lives in memory and cannot be detected by traditional antivirus. Command and control communication is usually maintained via IP addresses that change on a daily basis in order to outrun threat intelligence and blacklisting attempts. As no obvious damage is done straight away, these attacks often stay under the radar for prolonged times, so long as self-learning technology such as Darktrace is not employed.

    This becomes much more concerning as malware authors could swap one payload for another overnight if they deem it more profitable, switching from a furtive crypto mining Trojan to ransomware the next day. While we have not observed this kind of attack in the wild yet, it is plausible, and in cyberspace what can be done, will be done.

Conclusions

Revolutionary technologies like cryptocurrencies have both their dark and light aspects. For all of the creative energy released by the crypto-blockchain revolution, Bitcoin and its alternatives have quickly become the universal currency of the criminal underworld. Indeed, the former Chief Economist of the World Bank, Joseph Stiglitz – an adamant critic of cryptocurrencies – has said that the whole value of Bitcoin resides in its “potential for circumvention” and “lack of oversight”.

While Stiglitz’s case may be overstated, there can be no question that cyber criminals have sensed a new opportunity to make money. A lot of organizations still regard crypto mining as a compliance incident. This can lead to grave consequences as a cryptocurrency mining device might lead to more severe incidents that can have a serious effect on business operations.

This kind of threat is difficult to detect as no obvious damage is done. However, with Darktrace’s machine learning we can correlate even the weakest indicators of such an attack into a compelling picture of threat. While traditional tools may struggle to see these deviations, Darktrace can pinpoint the changes in behavior effected by cryptocurrency miners without having to rely on any blacklists or signatures.

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 27, 2026

How to Evaluate AI Vendors: 5 Key categories for AI Adoption

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Understanding the AI buyers’ market

AI adoption has become a central topic of discussion in boardrooms, drawing growing interest from business leaders. Ultimately, organizations hope that an investment in AI technology will have tremendous returns. However, the process of buying an AI solution is not as straight forward as it appears on the surface.  

While business leaders may be eager to improve productivity across their operations, practitioners responsible for evaluating and selecting AI solutions may not always have the visibility or technical understanding needed to make the right decisions for their business. What is typically marketed as a holistic solution to their most critical problems is usually followed by uncertainty when AI tools are finally operationalized in real environments.

This guide is intended to support security leaders who are under growing pressure to adopt AI tools while navigating complex terminology, vendor claims, and increasingly crowded buying cycles. Ultimately, the goal is to help organizations evaluate and adopt AI in a safe, effective, and well-governed way. To support this, we’ve structured the evaluation framework across five key categories:

  1. Governance, safety, and data controls
  1. Data gathering and training
  1. Model and technique choice
  1. Performance and accuracy validation    
  1. Interpretability, adjustability, and transparency    

What buying AI looks like in cybersecurity

While investing in AI can bring immense benefits to your security team, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.  

With acceleration in AI adoption, accompanied by the recent boom in agentic AI and autonomous agents, CISOs must look “beneath the hood" of these tools to understand how they work, how they are governed, and to ensure the system is secure and compliant with internal policies.

Challenges in the AI buyers’ marketplace  

The AI security software market is buzzing with hype and flashy promises, which, understandably, needs to be addressed with due diligence. Potential buyers, especially in the cybersecurity space, are hesitant when it comes to allowing AI autonomous capabilities across their workflows, and a lack of vendor transparency can exacerbate those feelings.  

Reinforcing this sentiment, research from this year's Darktrace’s State of AI Cybersecurity report shows where confidence and hesitancy emerge amongst potential buyers. On the one hand, security professionals agree that they have good visibility into the logic and reasoning processes their AI solutions use. However, they lack the explainability and trust to allow AI to take independent remedial action.

  • 89% say they have good visibility into the reasoning behind the outputs generated by AI solutions
  • 92% say they need to understand how a defensive AI tool makes decisions before they can trust it
  • Only 14% say they allow AI to act independently, performing autonomous actions without human approval
  • 74% say they are limiting the autonomy of AI taking action in their SOC until explainability improves

Given the desire for trust and explainability we are seeing from buyers, it's important for them to be equipped with the right questions to ask vendors during an assessment or POV of AI tools in order to demystify marketing hype from real operational outcomes.

Below is a list of categories in which buyers can assess AI vendors or AI Service Providers (AISPs) to help reach safe adoption and maximize their ROI.  

5 categories of AI vendor assessment

Darktrace groups these AI-related questions into 5 categories: governance, data and training, model and technique choice, performance validation, and interpretability and adjustability. By asking questions regarding each of these 5 categories, buyers can gain a deeper understanding of how an AISP’s systems work and whether they suit their business requirements.

Governance, safety, and data controls

Governance of AI systems is critical for all AISPs. Whether their platform is based around a single model, or is a more complex, composite AI solution, strong governance is essential to ensure the system is safe, robust, and reliable.

A simple question you could ask is:

What AI governance policies and frameworks do you follow, and/or certifications do you currently maintain?

For more questions you can ask vendors, download the full guide here.

Darktrace is certified to the ISO/IEC 42001 standard, the world’s first AI Management System (AIMS) standard. ISO/IEC 42001 addresses the unique ethical and technical challenges AI poses by setting out a structured way to manage risks such as transparency, accuracy, and misuse. This includes a commitment to ethical AI development, and effective management and monitoring of AI systems both prior to and continually after release.

Data gathering and training

Accurate, meaningful, and unbiased data gathering is the first important step in producing any AI system. An AI model trained using inaccurate, unbalanced, or poor-quality training data will fail to perform optimally.

To alleviate concerns regarding training data quality, a question you could ask is:

What steps do you take to prevent bias in your AI models and training data?

For more questions, download the full guide here.

AISPs should be able to provide information about the steps taken, workflows followed, and auditing performed to reduce AI bias where appropriate. While it’s sometimes impossible to fully remove bias from an AI model, appropriate actions should be taken to mitigate or reduce bias where relevant.

Model and technique choice

Different AI techniques are optimal for different tasks. For example, research from Gartner suggests that relying on a single “one-size-fits-all" model can lead to data gaps, especially in highly specialized domains.

To achieve more accurate and robust AI solutions, AI leaders should move beyond using just one model or technique, embrace composite AI practices, and adopt a holistic AI system perspective.

A straightforward question you could ask is simply:

What type(s) of AI model(s) do you utilize in your solution?

For more questions, download the full guide here.

While specific detailed information about custom systems used by AISPs is likely proprietary, buyers should expect vendors to be able to provide an overview of the broad techniques used. This will allow you as a buyer to determine if the type of model is appropriate for your use case.

Performance and accuracy validation  

Testing and evaluation of performance is essential for all AI systems. Performance analysis should be performed both before release and continually after release to identify potential data or model drift.  

A question you could ask to understand an AISPs testing workflow is:

How do you audit, test, evaluate, verify, and validate your AI model outputs?

For more questions, download the full guide here.

Testing workflows will likely vary depending on the type of model – measurements relevant to one system may not always be relevant to others. Assessment of systems should also extend beyond these standard accuracy and robustness tests, and should also feature physical performance, such as latency and resource consumption.  

Interpretability, adjustability, and transparency  

AI systems are typically a black box, simply providing an output without an explanation of how that output was attained. Interpretability and transparency are critical to ensure that both SOC teams and end-users trust the outputs of a system to be accurate and meaningful.

A question you could ask is:

How do you promote a trust relationship between human analysts and AI outputs?

For more questions, download the full guide here.

In the context of cybersecurity, trust and interpretability are even more essential. This is particularly relevant for generative AI-based systems (including most AI Agents), where the risk of hallucination can reduce trust in responses.

Cybersecurity systems often need to perform autonomous actions to block incoming threats – an email filtering system may hold potentially dangerous emails; a firewall may block malicious inbound connections. If SOC teams can’t trust these systems to perform accurately, these systems may be limited or disabled, critically reducing their defensive power.

Darktrace as an AI-native cybersecurity vendor

Darktrace has been building and applying AI in cybersecurity for over a decade, developing its capabilities alongside an increasingly complex and fast‑moving threat landscape. This experience has resulted in a mature, multi-layered approach to AI, which continuously learns the normal patterns of each organization to understand behavior, interpret context, and identify meaningful deviations — without relying on predefined rules or known attack signatures. Over time, this has enabled a proven behavioral understanding that helps uncover subtle signals of risk that may otherwise be missed.

With the backing of our ISO/IEC 42001 certification, stakeholders, customers, and partners can be confident that Darktrace is responsibly, ethically, and safely developing its AI systems, and managing the use of AI in day-to-day operations in a compliant and secure manner.  

Explore the principles behind Darktrace’s responsible AI approach, informed by collaboration with global experts in academia and governments, detailing how accountability, explainability, and continuous validation are built into its cybersecurity technology.

How Darktrace secures AI systems

Darktrace now brings these capabilities to monitor and respond to risk generated from AI systems across organizations with Darktrace / SECURE AI. This solution analyzes how prompts, agents, and systems are used within the context of each organization, bringing every AI interaction into a single view. This unique approach helps teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI agent activity.

Stay up to date

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

Further Reading on AI in cybersecurity

When deciding to invest in an AI solution, it’s important to understand what this means for you and your organization. The questions presented here are only a starting point in understanding an AI solution and whether it is appropriate for your use case.  

Gain deeper knowledge on applications of AI in cybersecurity and Darktrace’s multi-layered AI in the AI Arsenal White Paper.

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Jamie Bali
Technical Author (AI) Developer

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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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Jamie Bali
Technical Author (AI) Developer
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