Darktrace explains how cyber criminals hacked into an exposed Internet-facing server and mined cryptocurrency, showing the capabilities of open port threats.
The rise of crypto-currencies has fuelled cyber-crime in various ways. Bitcoin has facilitated a range of criminal activities from money laundering to ransomware payments since its release in 2009, leading to a spike in ransomware attacks which has been growing ever since.
More directly, cyber-criminals often hack into company servers and exploit their processing power to mine cryptocurrency, without the organization’s knowledge. This blog will explore a real-life attack where an Internet-facing server was compromised and started mining Monero coins.
Mining cryptocurrency on an Internet-facing server
At a small private company in the APAC region, an Internet-facing DNS server began to receive multiple incoming RDP connections. They all came from rare destinations which had never been seen on the network. Many were sent from external sources with the RDP cookie, ‘hello’, indicating a brute-force attack.
Figure 1: Timeline of the attack
Hours later, the device was seen connecting out to a known endpoint associated with crypto-mining.
As both RDP and SMB ports on the device were open to the Internet, anomalous SMB connections were seen as well shortly following the crypto-mining connections.
Open ports, open sesame
Internet-facing servers are subject to many external threats, especially if sensitive ports are exposed. In this case, the attacker was able to gain a foothold through the DNS server because both the RDP and SMB ports could receive connections. It is important therefore to close communication to all external points which do not strictly need to be open.
Crypto-jacking continues to be a viable way for attackers to expend company resources in order to speed up their mining operations. Especially with the popularity of cryptocurrencies at the moment, we have observed a significant uptick in these types of threat.
The connections to the mining pool were identified by Darktrace’s AI without relying on any known IoCs. Instead, Cyber AI recognized the anomalous nature of the external endpoints, which were statistically rare for the server’s ‘pattern of life’.
If the threat had not been detected, the attacker would have continued to abuse the server resources, resulting in latency issues for important processes. The server could also have been subject to further malicious activity such as DDoS or ransomware.
Figure 2: A similar incident showing an increase in model breaches around the time of compromise on June 8
Protecting a company’s gems
Crypto-mining is notoriously difficult to detect and can go on for months unnoticed. And it can form just one phase of an attacker’s full plan to infiltrate a network — alongside moving laterally and compromising additional devices. Open ports and siloed defenses pave the way for an attacker to break into a system with little resistance.
Organizations need a mechanism for detecting unusual and sinister behavior once the threat is inside. To this end, Darktrace’s evolving understanding of ‘normal’ across users, devices, and peer groups enables it to detect the subtle signs of latent threats. And with Autonomous Response, it responds at machine speed, neutralizing the threat before it has had the chance to spread.
In this case, with Darktrace’s SOC team, the client was immediately made aware of the activity and promptly took the device offline. A Proactive Threat Notification was sent as soon as the attacker had commenced mining. Darktrace analysts then worked through the issue with the customer until the crisis had been resolved.
Darktrace’s AI detects and responds to threats no matter where they come from – RDP account compromise, misconfigured Internet-facing server, or sophisticated Hafnium-style zero day. Furthermore, it provides much-needed visibility over the enterprise, identifying and highlighting Internet-facing devices and any issues they may pose.
Thanks to Darktrace analyst Taylor Breland for his insights on the above threat find.
Device / Anomalous RDP Followed By Multiple Model Breaches
Compromise / Monero Mining
Compromise / High Priority Crypto Currency Mining (Enhanced Model Breach/PTN)
Device / Anomalous SMB Followed By Multiple Model Breaches
Compliance / Crypto Currency Mining Activity
Anomalous Server Activity / Anomalous External Activity from Critical Network Device
Compliance / Incoming Remote Desktop
Compliance / Internet Facing RDP Server
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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.
Reimagining Your SOC: Overcoming Alert Fatigue with AI-Led Investigations
The efficiency of a Security Operations Center (SOC) hinges on its ability to detect, analyze and respond to threats effectively. With advancements in AI and automation, key early SOC team metrics such as Mean Time to Detect (MTTD) have seen significant improvements:
96% of defenders believing AI-powered solutions significantly boost the speed and efficiency of prevention, detection, response, and recovery.
Organizations leveraging AI and automation can shorten their breach lifecycle by an average of 108 days compared to those without these technologies.
While tool advances have improved performance and effectiveness in the detection phase, this has not been as beneficial to the next step of the process where initial alerts are investigated further to determine their relevance and how they relate to other activities. This is often measured with the metric Mean Time to Analysis (MTTA), although some SOC teams operate a two-level process with teams for initial triage to filter out more obviously uninteresting alerts and for more detailed analysis of the remainder. SOC teams continue to grapple with alert fatigue, overwhelmed analysts, and inefficient triage processes, preventing them from achieving the operational efficiency necessary for a high-performing SOC.
Addressing this core inefficiency requires extending AI's capabilities beyond detection to streamline and optimize the following investigative workflows that underpin effective analysis.
Challenges with SOC alert investigation
Detecting cyber threats is only the beginning of a much broader challenge of SOC efficiency. The real bottleneck often lies in the investigation process.
Detection tools and techniques have evolved significantly with the use of machine learning methods, improving early threat detection. However, after a detection pops up, human analysts still typically step in to evaluate the alert, gather context, and determine whether it’s a true threat or a false alarm and why. If it is a threat, further investigation must be performed to understand the full scope of what may be a much larger problem. This phase, measured by the mean time to analysis, is critical for swift incident response.
Challenges with manual alert investigation:
Too many alerts
Alerts lack context
Cognitive load sits with analysts
Insufficient talent in the industry
Fierce competition for experienced analysts
For many organizations, investigation is where the struggle of efficiency intensifies. Analysts face overwhelming volumes of alerts, a lack of consolidated context, and the mental strain of juggling multiple systems. With a worldwide shortage of 4 million experienced level two and three SOC analysts, the cognitive burden placed on teams is immense, often leading to alert fatigue and missed threats.
Even with advanced systems in place not all potential detections are investigated. In many cases, only a quarter of initial alerts are triaged (or analyzed). However, the issue runs deeper. Triaging occurs after detection engineering and alert tuning, which often disable many alerts that could potentially reveal true threats but are not accurate enough to justify the time and effort of the security team. This means some potential threats slip through unnoticed.
Understanding alerts in the SOC: Stopping cyber incidents is hard
Let’s take a look at the cyber-attack lifecycle and the steps involved in detecting and stopping an attack:
First we need a trace of an attack…
The attack will produce some sort of digital trace. Novel attacks, insider threats, and attacker techniques such as living-off-the-land can make attacker activities extremely hard to distinguish.
A detection is created…
Then we have to detect the trace, for example some beaconing to a rare domain. Initial detection alerts being raised underpin the MTTD (mean time to detection). Reducing this initial unseen duration is where we have seen significant improvement with modern threat detection tools.
When it comes to threat detection, the possibilities are vast. Your initial lead could come from anything: an alert about unusual network activity, a potential known malware detection, or an odd email. Once that lead comes in, it’s up to your security team to investigate further and determine if this is this a legitimate threat or a false alarm and what the context is behind the alert.
Investigation begins…
It doesn’t just stop at a detection. Typically, humans also need to look at the alert, investigate, understand, analyze, and conclude whether this is a genuine threat that needs a response. We normally measure this as MTTA (mean time to analyze).
Conducting the investigation effectively requires a high degree of skill and efficiency, as every second counts in mitigating potential damage. Security teams must analyze the available data, correlate it across multiple sources, and piece together the timeline of events to understand the full scope of the incident. This process involves navigating through vast amounts of information, identifying patterns, and discerning relevant details. All while managing the pressure of minimizing downtime and preventing further escalation.
Containment begins…
Once we confirm something as a threat, and the human team determines a response is required and understand the scope, we need to contain the incident. That's normally the MTTC (mean time to containment) and can be further split into immediate and more permanent measures.
The challenge is not only in 1) detecting threats quickly, but also 2) triaging and investigating them rapidly and with precision, and 3) prioritizing the most critical findings to avoid missed opportunities. Effective investigation demands a combination of advanced tools, robust workflows, and the expertise to interpret and act on the insights they generate. Without these, organizations risk delaying critical containment and response efforts, leaving them vulnerable to greater impacts.
While there are further steps (remediation, and of course complete recovery) here we will focus on investigation.
Developing an AI analyst: How Darktrace replicates human investigation
Darktrace has been working on understanding the investigative process of a skilled analyst since 2017. By conducting internal research between Darktrace expert SOC analysts and machine learning engineers, we developed a formalized understanding of investigative processes. This understanding formed the basis of a multi-layered AI system that systematically investigates data, taking advantage of the speed and breadth afforded by machine systems.
With this research we found that the investigative process often revolves around iterating three key steps: hypothesis creation, data collection, and results evaluation.
All these details are crucial for an analyst to determine the nature of a potential threat. Similarly, they are integral components of our Cyber AI Analyst which is an integral component across our product suite. In doing so, Darktrace has been able to replicate the human-driven approach to investigating alerts using machine learning speed and scale.
Here’s how it works:
When an initial or third-party alert is triggered, the Cyber AI Analyst initiates a forensic investigation by building multiple hypotheses and gathering relevant data to confirm or refute the nature of suspicious activity, iterating as necessary, and continuously refining the original hypothesis as new data emerges throughout the investigation.
Using a combination of machine learning including supervised and unsupervised methods, NLP and graph theory to assess activity, this investigation engine conducts a deep analysis with incidents raised to the human team only when the behavior is deemed sufficiently concerning.
After classification, the incident information is organized and processed to generate the analysis summary, including the most important descriptive details, and priority classification, ensuring that critical alerts are prioritized for further action by the human-analyst team.
If the alert is deemed unimportant, the complete analysis process is made available to the human team so that they can see what investigation was performed and why this conclusion was drawn.
To illustrate this via example, if a laptop is beaconing to a rare domain, the Cyber AI Analyst would create hypotheses including whether this could be command and control traffic, data exfiltration, or something else. The AI analyst then collects data, analyzes it, makes decisions, iterates, and ultimately raises a new high-level incident alert describing and detailing its findings for human analysts to review and follow up.
Cost Savings Equivalent to adding 30 full-time Level 2 analysts without increasing headcount
Minimize Business Risk Takes on the busy work from human analysts and elevates a team’s overall decision making
Improve Security Outcomes Identifies subtle, sophisticated threats through holistic investigations
Unlocking an efficient SOC
To create a mature and proactive SOC, addressing the inefficiencies in the alert investigation process is essential. By extending AI's capabilities beyond detection, SOC teams can streamline and optimize investigative workflows, reducing alert fatigue and enhancing analyst efficiency.
This holistic approach not only improves Mean Time to Analysis (MTTA) but also ensures that SOCs are well-equipped to handle the evolving threat landscape. Embracing AI augmentation and automation in every phase of threat management will pave the way for a more resilient and proactive security posture, ultimately leading to a high-performing SOC that can effectively safeguard organizational assets.
Every relevant alert is investigated
The Cyber AI Analyst is not a generative AI system, or an XDR or SEIM aggregator that simply prompts you on what to do next. It uses a multi-layered combination of many different specialized AI methods to investigate every relevant alert from across your enterprise, native, 3rd party, and manual triggers, operating at machine speed and scale. This also positively affects detection engineering and alert tuning, because it does not suffer from fatigue when presented with low accuracy but potentially valuable alerts.
Retain and improve analyst skills
Transferring most analysis processes to AI systems can risk team skills if they don't maintain or build them and if the AI doesn't explain its process. This can reduce the ability to challenge or build on AI results and cause issues if the AI is unavailable. The Cyber AI Analyst, by revealing its investigation process, data gathering, and decisions, promotes and improves these skills. Its deep understanding of cyber incidents can be used for skill training and incident response practice by simulating incidents for security teams to handle.
Create time for cyber risk reduction
Human cybersecurity professionals excel in areas that require critical thinking, strategic planning, and nuanced decision-making. With alert fatigue minimized and investigations streamlined, your analysts can avoid the tedious data collection and analysis stages and instead focus on critical decision-making tasks such as implementing recovery actions and performing threat hunting.
Stay tuned for part 3/3
Part 3/3 in the Reimagine your SOC series explores the preventative security solutions market and effective risk management strategies.
Bytesize Security: Insider Threats in Google Workspace
What is an insider threat?
An insider threat is a cyber risk originating from within an organization. These threats can involve actions such as an employee inadvertently clicking on a malicious link (e.g., a phishing email) or an employee with malicious intent conducting data exfiltration for corporate sabotage.
Insiders often exploit their knowledge and access to legitimate corporate tools, presenting a continuous risk to organizations. Defenders must protect their digital estate against threats from both within and outside the organization.
For example, in the summer of 2024, Darktrace / IDENTITY successfully detected a user in a customer environment attempting to steal sensitive data from a trusted Google Workspace service. Despite the use of a legitimate and compliant corporate tool, Darktrace identified anomalies in the user’s behavior that indicated malicious intent.
Attack overview: Insider threat
In June 2024, Darktrace detected unusual activity involving the Software-as-a-Service (SaaS) account of a former employee from a customer organization. This individual, who had recently left the company, was observed downloading a significant amount of data in the form of a “.INDD” file (an Adobe InDesign document typically used to create page layouts [1]) from Google Drive.
While the use of Google Drive and other Google Workspace platforms was not unexpected for this employee, Darktrace identified that the user had logged in from an unfamiliar and suspicious IPv6 address before initiating the download. This anomaly triggered a model alert in Darktrace / IDENTITY, flagging the activity as potentially malicious.
Figure 1: A Model Alert in Darktrace / IDENTITY showing the unusual “.INDD” file being downloaded from Google Workspace.
Following this detection, the customer reached out to Darktrace’s Security Operations Center (SOC) team via the Security Operations Support service for assistance in triaging and investigating the incident further. Darktrace’s SOC team conducted an in-depth investigation, enabling the customer to identify the exact moment of the file download, as well as the contents of the stolen documents. The customer later confirmed that the downloaded files contained sensitive corporate data, including customer details and payment information, likely intended for reuse or sharing with a new employer.
In this particular instance, Darktrace’s Autonomous Response capability was not active, allowing the malicious insider to successfully exfiltrate the files. If Autonomous Response had been enabled, Darktrace would have immediately acted upon detecting the login from an unusual (in this case 100% rare) location by logging out and disabling the SaaS user. This would have provided the customer with the necessary time to review the activity and verify whether the user was authorized to access their SaaS environments.
Conclusion
Insider threats pose a significant challenge for traditional security tools as they involve internal users who are expected to access SaaS platforms. These insiders have preexisting knowledge of the environment, sensitive data, and how to make their activities appear normal, as seen in this case with the use of Google Workspace. This familiarity allows them to avoid having to use more easily detectable intrusion methods like phishing campaigns.
Darktrace’s anomaly detection capabilities, which focus on identifying unusual activity rather than relying on specific rules and signatures, enable it to effectively detect deviations from a user’s expected behavior. For instance, an unusual login from a new location, as in this example, can be flagged even if the subsequent malicious activity appears innocuous due to the use of a trusted application like Google Drive.
Credit to Vivek Rajan (Cyber Analyst) and Ryan Traill (Analyst Content Lead)
Appendices
Darktrace Model Detections
SaaS / Resource::Unusual Download Of Externally Shared Google Workspace File
