Darktrace AI Detects Anomalous Behavior in Empty Office
AI uncovered hackers mining cryptocurrency on a biometric server in a deserted office during COVID-19. Learn how Darktrace detected this attack in real-time.
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
Share
09
Aug 2020
Darktrace recently detected a crypto-mining campaign that used the processing power of a corporate server to mine cryptocurrency at a manufacturing firm based in APAC. This server was in control of biometric door access within the client’s office and first downloaded a suspicious executable before beginning to mine for cryptocurrency. This occurred while the firm’s physical office was closed, with all employees working remotely due to COVID-19.
External-facing servers often face increased risk of compromise due to frequent touchpoints with the internet. It is vital that security teams are made aware of malicious activity on these devices as quickly as possible given their role in managing various business operations. Crypto-mining is difficult for many security tools to detect, particularly due to encrypted communications, and can go undetected on servers for long periods of time, slowing or damaging business operations.
Armed with an understanding of ‘normal’ for this manufacturing firm Darktrace’s AI was able to recognize the anomalous behavior, and the Cyber AI Analyst launched a fully autonomous investigation into the incident.
Figure 1: A timeline of the attack
The first signs of compromise
Darktrace identified an internet-facing server downloading a suspicious executable file, Securitcy.111, from a new external IP that had never been seen on the network before. The server had RDP, SMB, and SQL ports open externally – a successful incoming SQL connection from the external IP was seen shortly before the file download, suggesting a likely source of compromise. Successful RDP and SMB version 1 connections were also seen around the time of this activity.
Following this file download, the server began to repeatedly connect to external endpoints using self-signed TLS certificates. These endpoints are associated with mining pools for the digital currency Monero.
Darktrace’s detection
Despite a lack of threat intelligence on the external source of the file download, Darktrace’s AI easily detected that this behavior was highly unusual, alerting the firm to the serious emerging incident and enabling the customer to quickly take action. Instead of relying on known IoCs, the crypto-mining connections were immediately identified by Darktrace as suspicious due to their use of self-signed TLS certificates, alongside the statistical rarity of the endpoints for the business.
The new user agent was generic, and commonly associated with legitimate and malicious processes alike. This use of user agents means that C2 communication is less likely to be detected by the traditional security stack, however its unusual use was immediately flagged by Darktrace as suspicious.
AI Analyst coverage
Darktrace’s Cyber AI Analyst investigated this crypto-mining incident, providing an immediate indication that the device had been compromised.
Figure 2: Screenshot of AI Analyst detection of the crypto-mining
The below image shows the infected device over the same five-day period, with model breaches represented by dots and color indicating severity. The clear increase in model breaches on the device during this activity is a clear indication of compromise.
Figure 3: A graph showing a large increase in models breached by the device on 3 June
Figure 4: A sample of models breached by the server at the time of this compromise
As thousands of organizations moved to remote working this year – with the questions of when, how, and whether to return still unanswered – it is critical to ensure that physical IT infrastructure within offices remains secure. Internet-facing servers in particular must be able to withstand a multitude of external threats. This incident demonstrates the importance of security tools that can not only detect known IoCs, but emerging and unknown incidents.
Darktrace’s AI-powered approach was able to immediately detect the suspicious behavior and identify the compromise. Thanks to Darktrace’s quick detection, and the investigation of the Cyber AI Analyst, the client could remediate the crypto-mining infection.
Thanks to Darktrace analyst Emma Foulger for her insights on the above threat find.
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.
From One Alert to a Wider Campaign: Uncovering Novel Threats Through Behavioral Analysis
Key Findings
Darktrace’s Threat Research team is exploring a new method to identify malicious campaigns by correlating similar anomalous activity across its customer fleet.
The method uses unsupervised clustering to identify shared behavioral patterns across multiple customer deployments without relying on prior indicators of compromise (IoCs).
The approach identified shared infrastructure across dozens of customers, with activity linked to the likely POPA botnet.
A second cluster identified anomalous activity associated with Monero cryptocurrency mining.
How does Darktrace conduct threat research?
The Darktrace Threat Research team aims to identify malicious campaign activity affecting multiple customers. By their nature, these campaigns will lead to shared behavioral patterns across multiple victims.
In this piece, we describe a method for identifying malicious campaign activity that uses this simple observation as a guiding principle. We begin by outlining a few key facts about the modern threat landscape and why behavioral detection is necessary to meet the challenges it presents. We then explore how fleet-wide behavioral detections can be leveraged using the principle above to systematically identify multi-deployment campaigns impacting the Darktrace customer fleet. Finally, we demonstrate that this research is not purely theoretical, using real examples of campaign activity detected through this novel approach.
Shifting to a behavioral-focused approach
The modern era of cyber-defense necessitates detection capabilities beyond traditional signature-based methods. Technologies such as antivirus signatures, hash-based malware identification, and IP blocklists have historically been successful in defending against known threats. However, they are easily circumvented by attackers and inherently limited in defending against the novel attack techniques that today's threat landscape is producing.
Recent breakthroughs in AI represent a transformational risk multiplier that cannot be ignored. AI systems do not merely execute existing attacks at machine speed; there are signs they can also synthesize novel tradecraft [1], increasing the likelihood that defenders will encounter attack patterns that have never previously been observed. Meanwhile vulnerability disclosures continue to accelerate. More than 48,000 CVEs were published in 2025, and published statistics to date indicate that 2026 is already on track to significantly exceed that figure [2]. While multiple factors may contribute to vulnerability discovery, AI is seen as a key factor in this rapid growth. Organizations should expect a growing proportion of malicious activity to fall outside the scope of detections that rely solely on prior knowledge of known threats.
By design, Darktrace’s behavioral and anomaly-focused detection capabilities are well-suited to this testing landscape. With a focus on detecting unknown and unexpected activity that deviates from the baseline of “normal” behavior, detection of CVE exploitation without prior knowledge of the vulnerability and before public disclosure, for example, is achievable [3][4].
Darktrace’s diverse customer base benefits from a further layer of protection: working with only deidentified alert metadata, our threat researchers hunt proactively across the fleet. Beyond the level of the individual customer, there is a second, larger vantage to be leveraged.
Darktrace data provides unique insight into the unexpected behavioral patterns that occur across its customer fleet. What can we see if we look at this fleet-wide telemetry and hunt for systematic behavioral campaigns that impact our customer base?
Correlating across the customer base
At the deployment level, Darktrace focuses on whether activity is unusual in the context of an organization's normal behavior, but as we zoom out and look at these alerts at the fleet level, the problem fundamentally changes . We are no longer seeking anomalies in largely normal data; we are seeking systematic patterns within data already known to be anomalous. An isolated behavior anomaly may be notable for a single customer; the same fingerprint across dozens is a signal of a structured campaign. Hunting for such campaigns and sharing our findings are key areas of focus for Darktrace’s Threat Research team. In this spirit, Darktrace is actively exploring a a new research approach that leverages deidentified alert metadata to increase campaign detection ability.
This approach is based on the premise that an expert human analyst presented with a set of alerts will identify likely related activity by assessing whether the metadata fields indicate a relationship. For example, two connections to a rare endpoint that exhibits naming structure that is likely from a single algorithm are a strong indicator, as is the use of an unusual port carrying a given protocol (e.g. port 6000 for SSL). Darktrace’s approach to campaign detection encodes this expert human judgement process through deterministic embedding functions that obtain reproducible vector representations of alert data. Unsupervised clustering is then applied to these vector representations to obtain groups of alerts that describe semantically similar alert data across multiple customers. These clusters therefore represent similar anomalous activity that has occurred across multiple customers. In other words, these clusters are candidates for malicious campaign activity.
Figure 1: Layers of AI-based analysis provided through Darktrace products and cross-customer correlation.
Case study 1 – unusual connections to shared underlying infrastructure
In one recent case, the clustering produced several tight groups of alerts spanning dozens of customers. Each group centered on a single domain whose subdomains were algorithmically generated (e.g. s01699, s1736, s91). Examined side by side, these clusters shared an unmistakable behavioral shape: each involved a domain with a clear naming pattern (e.g. s016841786.net-echo[.]com, s1670.pixellog[.]io, s217.pixellog[.]io) , with connections over SSL on the unusual port 6000 to servers hosted by non-mainstream providers (e.g. OVH, Hetzner, GTHost, Leaseweb), rather than generally expected content-delivery networks.
Figure 2: An example model alert triggering on unusual outbound connections and data transfer to s1670.pixellog[.]io.
Closer inspection revealed that the same subdomain label appeared under different domains (e.g. s1786.net-echo[.]com and s1786.pixellog[.]io). Following this lead, Darktrace’s threat researchers pivoted across the customer base and found numerous distinct subdomain labels reused under more than one registered domain. In several cases, the same label resolved to the same IP across different domains (e.g. s1704 under both pixellog[.]io and net-echo[.]com → 148.113.222[.]244; s01684 → 15.235.67[.]95), suggesting the use of ephemeral subdomain infrastructure in an attempt to avoid signature-based defense techniques.
This example demonstrates how multi-deployment data can uncover shared infrastructure that may be difficult to identify at the individual deployment level alone. In particular, the presented method facilitated this discovery by producing coherent, tight clusters of geometrically similar vector embeddings of malicious activity from large-scale fleet data, which could then be presented to a researcher for further investigation. This activity highlighted across the Darktrace customer base was ultimately linked to likely POPA botnet activity through use of open-source intelligence (OSINT) [5].
Case Study 2 – anomalous user agent and txt URI
A second example shows the method surfacing a single, self-contained cluster. The clustering was not given any indicators to look for; instead, it grouped devices that shared an unusual combination of features. On inspection, that shared consisted of:
- unusual user agent (tes)
- specific request path (/md5_GIP.txt)
- uncommon destination port (83)
- hostname impersonating Google (matching regular expression ^www\.google\.com\.[a-zA-Z0-9]+\.fast-dns-host\.com$)
Treating these shared features as IoCs, Darktrace’s threat researchers then pivoted to an exploration across the customer base to investigate the full breadth of the campaign. This search identified further commonalities; additional domains which shared the same IP as the domain family highlighted in the cluster, as well as evidence of cryptocurrency mining on affected devices. Additional OSINT research also aligned with these findings, linking the discovered payloads and infrastructure to Monero cryptocurrency mining [6].
Conclusion
The method outlined in this blog is under active development, with ongoing technical refinement providing opportunities to further enhance its capabilities. While the capability continues to mature, this approach to threat hunting leverages the powerful vantage point of fleetwide telemetry across a diverse customer base to help protect Darktrace customers. This method allows campaigns that at the deployment level appear as isolated alerts to be seen at their true scale and spread. It enables proactive fleet-wide analysis without prior IoCs to identify campaigns impacting Darktrace customers.
This is possible because of what the method is built on: the atomic unit is the Darktrace alert, a signal that encodes years of cyber expertise, while the multi-deployment nature of the method allows organizations to indirectly benefit from findings from other networks through shared intelligence by Darktrace’s Threat Research team.
Credit to Daniel Levy (Threat Hunting Data Scientist), Paul Jennings (Principal Cyber Analyst), Emma Foulger (Global Head, Threat Operations)
“I wonder if running that tool influenced the connectivity in some way.”
“I might need to target down or reboot, but first, I'll investigate the network using ARP. I wonder if ARP is responding properly. If I see "ip neigh stale," that could indicate a problem. I think arping might help in this situation, but I’ll need to keep an eye on how the network is behaving. It's all about figuring out where the issue lies!”
図10：エージェントの異常なLDAPアクティビティに対してトリガーされたこのLinuxサーバーに対するモデル検知は、Cyber AI Analystによって自律的に調査され、その調査結果にはエージェントの異常なSMBセッションアクティビティに関する内容が含まれ、Cyber AIAnalystインシデントに記録されました。
DarktraceのCyber AI Analystは、エージェントのアクティビティによってトリガーされたモデル検知を自律的に調査し、その調査結果を1つの重要なインシデントレポートにまとめました。インシデント報告書の内容は、エージェントによるネットワークスキャンから始まりコーディング演習内容のSMBベースの上書きで終わる、エージェントの行動の各段階を詳しくカバーしています。
図20：Cyber AI Analystは、モデル検知に対する自律的な調査を通じて得られた所見をつなぎ合わせ、エージェントの進行状況のタイムラインを作成し、アクティビティを視覚的に表したグラフとともに提供しました。
Cyber Verification Program下のAnthropic Opus 4.6モデルなど、他のフロンティアモデルを使用したエージェントも、Darktraceのシミュレートされた環境で同じ設定とタスクを使ってテストされました。反復の度に、エージェントは演習の内容または採点プログラムを変更して不正を行おうとしました。基盤となるモデルに関係なく、エージェントは自分が置かれた環境を侵害することで、100％のスコアを達成する方法を見つけることに成功していました。これらのエージェントによるハッキングは、アクティビティの内容こそ違っていましたが、すべてその環境のベースラインの振る舞いから大きく逸脱した動作を引き起こしていました。これらの逸脱はDarktraceプラットフォームに十分な検知の機会を提供し、エージェントの逃げ場はありませんでした。
