Machine vs machine: instant domain fluxing identification with Darktrace
The algorithms made famous by Conficker almost a decade ago are continuing to frustrate the security community.
The algorithms made famous by Conficker almost a decade ago are continuing to frustrate the security community.
A function of some advanced malware, Domain Generating Algorithms (DGA) rapidly generate new domains as a means of evading security personnel. This process is known as ‘domain fluxing’ and provides an alternative means of communication with the attacker’s command-and-control servers. They are very difficult to detect using a traditional security approach.
Darktrace’s AI and machine learning are designed to detect threats without any pre-existing knowledge of attacker targets, tools, or capabilities. While traditional security tools depend on specific Indicators of Compromise to identify malicious activity, Darktrace instead focuses on behavioral changes that may point to an active compromise. Detection of Domain Generating Algorithms is just one example of Darktrace’s ability to pinpoint attacker C2 communications through the identification of behavioral anomalies.
DGA identification in action
Darktrace recently saw an employee of a healthcare company connecting a personal laptop to the corporate network. The employee was asked by another member of the organization to troubleshoot the laptop as a favor. Unbeknown to the employee, and despite the fact that anti-virus was installed, the laptop was compromised by an unknown strain of malware.
On joining the network, the compromised laptop made DNS queries for domains that Darktrace classified as 100% rare for the environment. These domains appeared to be dynamically generated as they were all between 25 to 30 characters long and used multiple top-level domains. Darktrace immediately triggered a high-scoring domain fluxing alert due to the sudden increase in failed DNS requests for abnormal domains.
Additional domain fluxing alerts were triggered within 30 minutes of the device joining the network. The only reason that this highly suspicious activity was allowed to persist for that period of time was that the security administrator was at lunch.
A sample of Darktrace network logs showing failed DNS requests for DGA domains.
Event logs showing domain fluxing identification within 14 seconds of the device joining the network.
The security administrator was informed of the situation on his return and immediately performed incident triage with Darktrace. The administrator was able to rapidly assess the device’s connection history using Darktrace’s native filters that are designed to detect scanning, lateral movement, C2 communications, and egress. He then identified the location of the compromised laptop, disabled its internet access, and physically removed the device from the network.
The total time from initial connection to identification, containment, and removal of the compromised, rogue device was approximately 67 minutes. The administrator left the following comment for the Darktrace analyst team:
Darktrace Threat Visualizer shows multiple domain fluxing model breaches as a result of a high volume of failed DNS requests for suspicious, dynamically-generated domains.
Darktrace’s AI approach focuses on identifying anomalies in evolving patterns of behavior. Although the laptop was new to the network, no other device was seen making a high volume of failed DNS requests for similar DGA domains. Darktrace immediately identified this activity as anomalous and generated an alert within 14 seconds of the device joining the network.
By employing Darktrace, a single analyst was able to discover and assess a compromised, rogue device operating within the network environment in just over an hour. What’s more, with Antigena (Darktrace’s autonomous response solution) in place, all suspicious behaviors would have been temporarily suspended, in real time. Alternatively, the administrator could have manually authorized Antigena’s proposed actions via the Darktrace Mobile App.
No extensive analysis of distributed log files, PCAPs, or other security tools. No prior knowledge of the attacker’s infrastructure or the malware. Darktrace AI identified DGA domains that were being produced on the fly, without help. That’s the level our security technology must perform at if we are to keep on top of all the new tech in the modern attacker’s toolkit.
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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.
ABOUT ThE AUTHOR
Chief Product Officer
Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.
Attack Trends: VIP Impersonation Across the Business Hierarchy
What is VIP impersonation?
VIP impersonation involves a threat actor impersonating a trusted, prominent figure at an organization in an attempt to solicit sensitive information from an employee.
VIP impersonation is a high-priority issue for security teams, but it can be difficult to assess the exact risks, and whether those are more critical than other types of compromise. Looking across a range of Darktrace/Email™ customer deployments, this blog explores the patterns of individuals targeted for impersonation and evaluates if these target priorities correspond with security teams' focus on protecting attack pathways to critical assets.
How do security teams stop VIP Impersonation?
Protecting VIP entities within an organization has long been a traditional focus for security teams. The assumption is that VIPs, due to their prominence, possess the greatest access to critical assets, making them prime targets for cyber threats.
Email remains the predominant vector for attacks, with over 90% of breaches originating from malicious emails. However, the dynamics of email-based attacks are shifting, as the widespread use of generative AI is lowering the barrier to entry by allowing adversaries to create hyper-realistic emails with minimal errors.
Given these developments, it's worth asking the question – which entities (VIP/non-VIP) are most targeted by threat actors via email? And, more importantly – which entities (VIP/non-VIP) are more valuable if they are successfully compromised?
There are two types of VIPs:
1. When referring to emails and phishing, VIPs are the users in an organization who are well known publicly.
2. When referring to attack paths, VIPs are users in an organization that are known publicly and have access to highly privileged assets.
Not every prominent user has access to critical assets, and not every user that has access to critical assets is prominent.
Darktrace analysis of VIP impersonation
We analyzed patterns of attack pathways and phishing attempts across 20 customer deployments from a large, randomized pool encompassing a diverse range of organizations.
Understanding Attack Pathways
Our observations revealed that 57% of low-difficulty attack paths originated from VIP entities, while 43% of observed low-difficulty attack paths towards critical assets or entities began through non-VIP users. This means that targeting VIPs is not the only way attackers can reach critical assets, and that non-VIP users must be considered as well.
While the sample size prevents us from establishing statistical significance across all customers, the randomized selection lends credence to the generalizability of these findings to other environments.
On average, 1.35% of total emails sent to these customers exhibited significantly malicious properties associated with phishing or some form of impersonation. Strikingly, nearly half of these malicious emails (49.6%) were directed towards VIPs, while the rest were sent to non-VIPs. This near-equal split is worth noting, as attack paths show that non-VIPs also serve as potential entry points for targeting critical assets.
For example, a recent phishing campaign targeted multiple customers across deployments, with five out of 13 emails specifically aimed at VIP users. Darktrace/Email actioned the malicious emails by double locking the links, holding the messages, and stripping the attachments.
Given that non-VIP users receive nearly half of the phishing or impersonation emails, it underscores the critical importance for security teams to recognize their blind spots in protecting critical assets. Overlooking the potential threat originating from non-VIP entities could lead to severe consequences. For instance, if a non-VIP user falls victim to a phishing attack or gets compromised, their credentials could be exploited to move laterally within the organization, potentially reaching critical assets.
This highlights the necessity for a sophisticated security tool that can identify targeted users, without the need for extensive customization and regardless of VIP status. By deploying a solution capable of promptly responding to email threats – including solicitation, phishing attempts, and impersonation – regardless of the status of the targeted user, security teams can significantly enhance their defense postures.
Darktrace vs Traditional Email Detection Methods
Traditional rules and signatures-based detection mechanisms fall short in identifying the evolving threats we’ve observed, due to their reliance on knowledge of past attacks to categorize emails.
Secure Email Gateway (SEG) or Integrated Cloud Email Security (ICES) tools categorize emails based on previous or known attacks, operating on a known-good or known-bad model. Even if tools use AI to automate this process, the approach is still fundamentally looking to the past and therefore vulnerable to unknown and zero-day threats.
Darktrace uses AI to understand each unique organization and how its email environment interoperates with each user and device on the network. Consequently, it is able to identify the subtle deviations from normal behavior that qualify as suspicious. This approach goes beyond simplistic categorizations, considering factors such as the sender’s history and recipient’s exposure score.
This nuanced analysis enables Darktrace to differentiate between genuine communications and malicious impersonation attempts. It automatically understands who is a VIP, without the need for manual input, and will action more strongly on incoming malicious emails based on a user’s status.
Email does determine who is a VIP, without a need of manual input, and will action more strongly on incoming malicious emails.
Darktrace/Email also feeds into Darktrace’s preventative security tools, giving the interconnected AI engines further context for assessing the high-value targets and pathways to vital internal systems and assets that start via the inbox.
Leveraging AI for Enhanced Protection Across the Enterprise
The efficacy of AI-driven security solutions lies in their ability to make informed decisions and recommendations based on real-time business data. By leveraging this data, AI driven solutions can identify exploitable attack pathways and an organizations most critical assets. Darktrace uniquely uses several forms of AI to equip security teams with the insights needed to make informed decisions about which pathways to secure, reducing human bias around the importance of protecting VIPs.
With the emergence of tools like AutoGPT, identifying potential targets for phishing attacks has become increasingly simplified. However, the real challenge lies in gaining a comprehensive understanding of all possible and low-difficulty attack paths leading to critical assets and identities within the organization.
At the same time, organizations need email tools that can leverage the understanding of users to prevent email threats from succeeding in the first instance. For every email and user, Darktrace/Email takes into consideration changes in behavior from the sender, recipient, content, and language, and many other factors.
Integrating Darktrace/Email with Darktrace’s attack path modeling capabilities enables comprehensive threat contextualization and facilitates a deeper understanding of attack pathways. This holistic approach ensures that all potential vulnerabilities, irrespective of the user's status, are addressed, strengthening the overall security posture.
Contrary to conventional wisdom, our analysis suggests that the distinction between VIPs and non-VIPs in terms of susceptibility to impersonation and low-difficulty attack paths is not as pronounced as presumed. Therefore, security teams must adopt a proactive stance in safeguarding all pathways, rather than solely focusing on VIPs.
Attack path modeling enhances Darktrace/Email's capabilities by providing crucial metrics on potential impact, damage, exposure, and weakness, enabling more targeted and effective threat mitigation strategies. For example, stronger email actions can be enforced for users who are known to have a high potential impact in case of compromise.
In an era where cyber threats continue to evolve in complexity, an adaptive and non-siloed approach to securing inboxes, high-priority individuals, and critical assets is indispensable.
Gootloader Malware: Detecting and Containing Multi-Functional Threats with Darktrace
What is multi-functional malware?
While traditional malware variants were designed with one specific objective in mind, the emergence of multi-functional malware, such as loader malware, means that organizations are likely to be confronted with multiple malicious tools and strains of malware at once. These threats often have non-linear attack patterns and kill chains that can quickly adapt and progress quicker than human security teams are able to react. Therefore, it is more important than ever for organizations to adopt an anomaly approach to combat increasingly versatile and fast-moving threats.
Example of Multi-functional malware
One example of a multi-functional malware recently observed by Darktrace can be seen in Gootloader, a multi-payload loader variant that has been observed in the wild since 2020. It is known to primarily target Windows-based systems across multiple industries in the US, Canada, France, Germany, and South Korea .
How does Gootloader malware work?
Once installed on a target network, Gootloader can download additional malicious payloads that allow threat actors to carry out a range of harmful activities, such as stealing sensitive information or encrypting files for ransom.
The Gootloader malware is known to infect networks via search engine optimization (SEO) poisoning, directing users searching for legitimate documents to compromised websites hosting a malicious payload masquerading as the desired file.
If the malware remains undetected, it paves the way for a second stage payload known as Gootkit, which functions as a banking trojan and information-stealer, or other malware tools including Cobalt Strike and Osiris .
Darktrace detection of Gootloader malware
In late 2023, Darktrace observed one instance of Gootloader affecting a customer in the US. Thanks to its anomaly-focused approach, Darktrace DETECT™ quickly identified the anomalous activity surrounding this emerging attack and brought it to the immediate attention of the customer’s security team. All the while, Darktrace RESPOND™ was in place and able to autonomously intervene, containing the suspicious activity and ensuring the Gootloader compromise could not progress any further.
In September 2023, Darktrace identified an instance of the Gootloader malware attempting to propagate within the network of a customer in the US. Darktrace identified the first indications of the compromise when it detected a device beaconing to an unusual external location and performing network scanning. Following this, the device was observed making additional command-and-control (C2) connections, before finally downloading an executable (.exe) file which likely represented the download of a further malicious payload.
As this customer had subscribed to the Proactive Notification Service (PTN), the suspicious activity was escalated to the Darktrace Security Operations Center (SOC) for further investigation by Darktrace’s expert analysts. The SOC team were able to promptly triage the incident and advise urgent follow-up actions.
Gootloader Attack Overview
Initial Beaconing and Scanning Activity
On September 21, 2023, Darktrace observed the first indications of compromise on the network when a device began to make regular connections to an external endpoint that was considered extremely rare for the network, namely ‘analyzetest[.]ir’.
Although the endpoint did not overtly seem malicious in nature (it appeared to be related to laboratory testing), Darktrace recognized that it had never previously been seen on the customer’s network and therefore should be treated with caution. This initial beaconing activity was just the beginning of the malicious C2 communications, with several additional instances of beaconing detected to numerous suspicious endpoints, including funadhoo.gov[.]mv, tdgroup[.]ru’ and ‘army.mil[.]ng.
Soon thereafter, Darktrace detected the device performing internal reconnaissance, with an unusually large number of connections to other internal locations observed. This scanning activity appeared to primarily be targeting the SMB protocol by scanning port 445.
Within seconds of DETECT’s detection of this suspicious SMB scanning activity, Darktrace RESPOND moved to contain the compromise by blocking the device from connecting to port 445 and enforcing its ‘pattern of life’. Darktrace’s Self-Learning AI enables it to learn a device’s normal behavior and recognize if it deviates from this; by enforcing a pattern of life on an affected device, malicious activity is inhibited but the device is allowed to continue its expected activity, minimizing disruption to business operations.
Following the initial detection of this anomalous activity, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the beaconing and scanning activity and was able to connect these seemingly separate events into one incident. AI Analyst analyzes thousands of connections to hundreds of different endpoints at machine speed and then summarizes its findings in a single pane of glass, giving customers the necessary information to assess the threat and begin remediation if necessary. This significantly lessens the burden for human security teams, saving them previous time and resources, while ensuring they maintain full visibility over any suspicious activity on their network.
Darktrace continued to observe the device carrying out beaconing activity over the next few days, likely representing threat actors attempting to establish communication with their malicious infrastructure and setting up a foothold within the customer’s environment. In one such example, the device was seen connecting to the suspicious endpoint ‘fysiotherapie-panken[.]nl’. Multiple open-source intelligence (OSINT) vendors reported this endpoint to be a known malware delivery host .
Once again, Darktrace RESPOND was in place to quickly intervene in response to these suspicious external connection attempts. Over the course of several days, RESPOND blocked the offending device from connecting to suspicious endpoints via port 443 and enforced its pattern of life. These autonomous actions by RESPOND effectively mitigated and contained the attack, preventing it from escalating further along the kill chain and providing the customer’s security team crucial time to take act and employ their own remediation.
Possible Payload Retrieval
A few days later, on September 26, 2023, Darktrace observed the affected device attempting to download a Windows Portable Executable via file transfer protocol (FTP) from the external location ‘ftp2[.]sim-networks[.]com’, which had never previously been seen on the network. This download likely represented the next step in the Gootloader infection, wherein additional malicious tooling is downloaded to further cement the malicious actors’ control over the device. In response, Darktrace RESPOND immediately blocked the device from making any external connections, ensuring it could not download any suspicious files that may have rapidly escalated the attackers’ efforts.
The observed combination of beaconing activity and a suspicious file download triggered an Enhanced Monitoring breach, a high-fidelity DETECT model designed to detect activities that are more likely to be indicative of compromise. These models are monitored by the Darktrace SOC round the clock and investigated by Darktrace’s expert team of analysts as soon as suspicious activity emerges.
In this case, Darktrace’s SOC triaged the emerging activity and sent an additional notice directly to the customer’s security team, informing them of the compromise and advising on next steps. As this customer had subscribed to Darktrace’s Ask the Expert (ATE) service, they also had a team of expert analysts available to them at any time to aid their investigations.
Loader malware variants such as Gootloader often lay the groundwork for further, potentially more severe threats to be deployed within compromised networks. As such, it is crucial for organizations and their security teams to identify these threats as soon as they emerge and ensure they are effectively contained before additional payloads, like information-stealing malware or ransomware, can be downloaded.
In this instance, Darktrace demonstrated its value when faced with a multi-payload threat by detecting Gootloader at the earliest stage and responding to it with swift targeted actions, halting any suspicious connections and preventing the download of any additional malicious tooling.
Darktrace DETECT recognized that the beaconing and scanning activity performed by the affected device represented a deviation from its expected behavior and was indicative of a potential network compromise. Meanwhile, Darktrace RESPOND ensured that any suspicious activity was promptly shut down, buying crucial time for the customer’s security team to work with Darktrace’s SOC to investigate the threat and quarantine the compromised device.
Credit to: Ashiq Shafee, Cyber Security Analyst, Qing Hong Kwa, Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore