How AI Detected A Hacker Hiding in Energy Grid Within Hours
08
Oct 2020
Darktrace's AI swiftly detected a hacker infiltrating an energy grid within hours. Learn about how AI identified the threat and uncovered anomalous behavior.
A cyber-criminal had already made the first steps of a critical intrusion at a European energy organization when the company deployed AI for cyber defense. Despite the attacker already lurking in the system, Darktrace was able to recognize that their activity deviated from the learned ‘pattern of life’ of the rest of the organization.
The hacker had compromised a desktop and established Command & Control (C2), downloading executable files disguised as harmless PNG files. But Darktrace autonomously grouped the desktop into a ‘peer group’ of similar devices, recognizing that it’s behavior was anomalous in comparison to the wider group.
The intrusion used many common evasion techniques to bypass traditional tools, including ‘Living off the Land’ techniques and masquerading malware behind commonly used file types. Upon Darktrace’s detection, later analysis of these ‘harmless’ files suggests they could lead to possible remote access of the compromised device, with use of the Metasploit framework.
Attack details
Figure 1: A timeline of the attack
Immediately upon installation, Darktrace began monitoring the behavior of around 5,000 devices, establishing their ‘pattern of life’, as well as that of their peer groups, and the wider organization. Just two hours into this learning process, an adminstator’s desktop was observed making suspicious connections to multiple domains hosted on IP 78.142.XX.XXX. The regular nature of these connections suggests that the infection was already established on the device.
The next day, the desktop was observed downloading a suspicious executable file named d.png, and multiple similar downloads subsequently occurred.
Executable files are often masqueraded as other file types in order to help bypass security measures, however the mismatched file extension here was immediately detected by Darktrace and flagged for further investigation.
A lack of OSINT for the download source at the time of this activity meant other security measures may have missed the suspicious HTTP connections. However, the rarity of the IP on the network alongside the unusual behavior in comparison to other network devices led Darktrace to quickly detect this malicious beaconing.
An overview of the infected device
After the first model breach, Darktrace continued to monitor the infected device, graphically representing the regular connections to the malicious endpoint w.gemlab[.]top. The device made several connections to this endpoint at precise, 3-hour intervals, suggesting some automated activity. No other devices in the peer group displayed this sort of behavior.
Figure 2: Darktrace presenting the connections in a graph, with model breaches represented by orange dots
Darktrace detected the suspicious nature of these HTTP connections, clearly surfacing the model breach for the security team to review and remediate.
Figure 3: Darktrace surfacing high-level details of the model breach
Figure 4: The device event log
Detecting a threat already inside
This example of a sophisticated attack shows an attempt to ‘blend in’ to the noise of regular traffic. However, Darktrace’s Immune System was still able to identify the signs of malintent, given its ability to auto-detect and cluster ‘peer groups’ of users and devices, thereby still recognizing abnormal behavior on the single compromised device. Despite only being active for a few hours, Darktrace immediately flagged the activity for further investigation.
Without Darktrace’s real-time detections and alerts – and a quick response from the security team to contain the threat — the potential ramifications of this intrusion can’t be understated. With effective command and control and sufficient privileges granted, cyber-criminals have been known to disrupt entire energy grids leading to mass blackouts in Ukraine and Estonia. Alternatively, hackers could have held large volumes of sensitive files to ransom, causing huge financial and reputational damage to the firm in question.
This isn’t the first time Darktrace has identified existing infections in customer environments – and it’s unlikely to be the last. A self-learning approach to cyber defence is not limited to identifying changes in the environment, but can detect existing compromises as well as novel and advanced attacks that evade traditional rules and signatures.
Thanks to Darktrace analyst Emma Foulger for her insights on the above threat find.
IoCs:IoCCommentcloud.apcdn[.]ruMultiple downloads of file from this endpoint URI: /d.png Hash: 82e1c9727ae04a19c8a155559e1855349e528244w.gemlab[.]topFirst observed C2 connection was seen to this hostnamecloud.gemlab[.]top img.gemlab[.]top img.apcdn[.]ruOther C2 communication seen to these hostnames
Darktrace model detections:
Device / Suspicious Domain
Compromise / Agent Beacon (Long Period)
Anomalous File / EXE from Rare External Location
Anomalous File / Masqueraded File Transfer
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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
Max Heinemeyer
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.
From Royal to BlackSuit: Understanding the Tactics and Impact of a Sophisticated Ransomware Strain
What is BlackSuit Ransomware?
Since late 2023, Darktrace has detected BlackSuit ransomware infiltrating multiple customer networks in the US. This ransomware has targeted a wide range of industries, including arts, entertainment, real estate, public administration, defense, and social security.
Emerging in May 2023, BlackSuit is believed to be a spinoff of Royal ransomware due to similarities in code and Conti, and most likely consists of Russian and Eastern European hackers [1]. Recorded Future reported that the ransomware had affected 95 organizations worldwide, though the actual number is likely much higher [2]. While BlackSuit does not appear to focus on any particular sector, it has targeted multiple organizations in the healthcare, eduction, IT, government, retail and manufacturing industries [3]. Employing double extortion tactics, BlackSuit not only encrypts files but also steals sensitive data to leverage ransom payments.
BlackSuit has demanded over USD 500 million in ransoms, with the highest individual demand reaching USD 60 million [4]. Notable targets include CDK Global, Japanese media conglomerate Kadokawa, multiple educational institutions, Octapharma Plasma, and the government of Brazil [5][6][7][8].
Darktrace’s Coverage of BlackSuit Ransomware Attack
Case 1, November 2023
The earliest attack on a Darktrace customer by BlackSuit was detected at the start of November 2023. The unusual network activity began on a weekend—a time commonly chosen by ransomware groups to increase their chances of success, as many security teams operate with reduced staff. Darktrace identified indicators of the attackers’ presence on the network for almost two weeks, during which a total of 15 devices exhibited suspicious behavior.
The attack commenced with unusual internal SMB (Server Message Block) connections using a compromised service account. An internal device uploaded an executable (zzza.exe) to a domain controller (DC) and shortly after, wrote a script (socks5.ps1) to another device. According to a Cybersecurity Advisory from the CISA (Cybersecurity and Infrastructure Security Agency, US), the script file was a PowerShell reverse proxy [9].
Approximately an hour and a half later, the device to which the script was written exhibited uncommon WMI (Windows Management Instrumentation) activity. Two hours after receiving the executable file, the DC was observed making an outgoing NTLM request, using PowerShell to remotely execute commands, distributing differently named executable files (<PART OF THE CUSTOMER’S NAME>.exe), and controlling services on other devices.
Eighteen hours after the start of the unusual activity, Darktrace detected another device making repeated connections to “mystuff.bublup[.]com”, which the aforementioned CISA Advisory identifies as a domain used by BlackSuit for data exfiltration [9].
About ten minutes after the suspicious executables were distributed across the network, and less than 24 hours after the start of the unusual activity, file encryption began. A total of ten devices were seen appending the “.blacksuit” extension to files saved on other devices using SMB, as well as writing ransom notes (readme.blacksuit.txt). The file encryption lasted less than 20 minutes.
Figure 1: An example of the contents of a BlackSuit ransom note being written over SMB.
During this compromise, external connections to endpoints related to ConnectWise’s ScreenConnect remote management tool were also seen from multiple servers, suggesting that the tool was likely being abused for command-and-control (C2) activity. Darktrace identified anomalous connectivity associated with ScreenConnect was seen up to 11 days after the start of the attack.
10 days after the start of the compromise, an account belonging to a manager was detected adding “.blacksuit” extensions to the customer’s Software-a-Service (SaaS) resources while connecting from 173.251.109[.]106. Six minutes after file encryption began, Darktrace flagged the unusual activity and recommended a block. However, since Autonomous Response mode was not enabled, the customer’s security team needed to manually confirm the action. Consequently, suspicious activity continued for about a week after the initial encryption. This included disabling authentication on the account and an unusual Teams session initiated from the suspicious external endpoint 216.151.180[.]147.
Case 2, February 2024
Another BlackSuit compromise occurred at the start of February 2024, when Darktrace identified approximately 50 devices exhibiting ransomware-related activity in another US customer’s environment. Further investigation revealed that a significant number of additional devices had also been compromised. These devices were outside Darktrace’s purview to the customer’s specific deployment configuration. The threat actors managed to exfiltrate around 4 TB of data.
Initial access to the network was gained via a virtual private network (VPN) compromise in January 2024, when suspicious connections from a Romanian IP address were detected. According to CISA, the BlackSuit group often utilizes the services of initial access brokers (IAB)—actors who specialize in infiltrating networks, such as through VPNs, and then selling that unauthorized access to other threat actors [9]. Other initial access vectors include phishing emails, RDP (Remote Desktop Protocol) compromise, and exploitation of vulnerable public-facing applications.
Similar to the first case, the file encryption began at the end of the working week. During this phase of the attack, affected devices were observed encrypting files on other internal devices using two compromised administrator accounts. The encryption activity lasted for approximately six and a half hours. Multiple alerts were sent to the customer from Darktrace’s Security Operations Centre (SOC) team, who began reviewing the activity within four minutes of the start of the file encryption.
Figure 2: Darktrace’s Cyber AI Analyst clustering together multiple events related to unusual activity on the network, including file encryption over SMB by BlackSuit.
Figure 3: A spike in model alerts on the day when file encryption by BlackSuit was observed in the network.
In this case, the threat actor utilized SystemBC proxy malware for command and control (C2). A domain controller (DC) was seen connecting to 137.220.61[.]94 on the same day the file encryption took place. The DC was also observed connecting to a ProxyScrape domain around the same time, which is related to the SOCKS5 protocol used by SystemBC. During this compromise, RDP, SSH, and SMB were used for lateral movement within the network.
Figure 4: A Cyber AI Analyst investigation alerting to a device on the VPN subnet making suspicious internal SSH connections due to malicious actors moving laterally within the network.
Signs of threat actors potentially being on the network were observed as early as two days prior to the file encryption. This included unusual internal network scanning via multiple protocols (ICMP, SMB, RDP, etc.), credential brute-forcing, SMB access failures, and anonymous SMBv1 sessions. These activities were traced to IP addresses belonging to two desktop devices in the VPN subnet associated with two regular employee user accounts. Threat actors were seemingly able to exploit at least one of these accounts due to LDAP legacy policies being in place on the customer’s environment.
Figure 5: A Cyber AI Analyst incident summary alerting to a device on the VPN subnet conducting internal reconnaissance.
Figure 6: Examples of the proposed Darktrace Autonomous Response actions on the day BlackSuit initiated file encryption.
Case 3, August 2024
The most recently observed BlackSuit compromise occurred in August 2024, when a device was observed attempting to brute-force the credentials of an IT administrator. This activity continued for 11 days.
Once the admin’s account was successfully compromised, network scanning, unusual WMI, and SAMR (Security Account Manager Remote protocol) activity followed. A spike in the use of this account was detected on a Sunday—once again, the attackers seemingly targeting the weekend—when the account was used by nearly 50 different devices.
The compromised admin’s account was exploited for data gathering via SMB, resulting in the movement of 200 GB of data between internal devices in preparation for exfiltration. The files were then archived using the naming convention “*.part<number>.rar”.
Around the same time, Darktrace observed data transfers from 19 internal devices to “bublup-media-production.s3.amazonaws[.]com,” totaling just over 200 GB—the same volume of data gathered internally. Connections to other Bublup domains were also detected. The internal data download and external data transfer activity took approximately 8-9 hours.
Unfortunately, Darktrace was not configured in Autonomous Response mode at the time of the attack, meaning any mitigative actions to stop the data gathering or exfiltration required human confirmation.
Figure 7: One of the compromised devices was seen sending 80 GB of data to bublup-media-production.s3.amazonaws[.]com within a span of 4 hours.
Once the information was stolen, the threat actor moved on to the final stage of the attack—file encryption. Five internal devices, using either the compromised admin account or connecting via anonymous SMBv1 sessions, were seen encrypting files and writing ransom notes to five other devices on the network. The attempts at file encryption continued for around two hours, but Darktrace’s Autonomous Response capability was able to block the activity and prevent the attack from escalating.
Conclusion
The persistent and evolving threat posed by ransomware like BlackSuit underscores the critical importance of robust cybersecurity measures across all sectors. Since its emergence in 2023, BlackSuit has demonstrated a sophisticated approach to infiltrating networks, leveraging double extortion tactics, and demanding substantial ransoms. The cases highlighted above illustrate the varied methods and persistence of BlackSuit attackers, from exploiting VPN vulnerabilities to abusing remote management tools and targeting off-hours to maximize impact.
Although many similar connection patterns, such as the abuse of Bublup services for data exfiltration or the use of SOCKS5 proxies for C2, were observed during cases investigated by Darktrace, BlackSuit actors are highly sophisticated and tailors their attacks to each target organization. The consequences of a successful attack can be highly disruptive, and remediation efforts can be time-consuming and costly. This includes taking the entire network offline while responding to the incident, restoring encrypted files from backups (if available), dealing with damage to the organization’s reputation, and potential lawsuits.
These BlackSuit ransomware incidents emphasize the need for continuous vigilance, timely updates to security protocols, and the adoption of autonomous response technologies to swiftly counteract such attacks. As ransomware tactics continue to evolve, organizations must remain agile and informed to protect their critical assets and data. By learning from these incidents and enhancing their cybersecurity frameworks, organizations can better defend against the relentless threat of ransomware and ensure the resilience of their operations in an increasingly digital world.
Credit to Signe Zaharka (Principal Cyber Analyst) and Adam Potter (Senior Cyber Analyst)
Darktrace’s First 6: Half-Year Threat Report 2024 highlights the latest attack trends and key threats observed by the Darktrace Threat Research team in the first six months of 2024.
Focuses on anomaly detection and behavioral analysis to identify threats
Maps mitigated cases to known, publicly attributed threats for deeper context
Offers guidance on improving security posture to defend against persistent threats
Appendices
Darktrace Model Detections
Anomalous Connection / Data Sent to Rare Domain
Anomalous Connection / High Volume of New or Uncommon Service Control
Anomalous Connection / New or Uncommon Service Control
Anomalous Connection / Rare WinRM Outgoing
Anomalous Connection / SMB Enumeration
Anomalous Connection / Suspicious Activity On High Risk Device
Anomalous Connection / Suspicious Read Write Ratio
Anomalous Connection / Suspicious Read Write Ratio and Unusual SMB
Anomalous Connection / Sustained MIME Type Conversion
.blacksuit - File extension – When encrypting the files, this extension is appended to the filename – High
readme.blacksuit.txt – ransom note - A file demanding cryptocurrency payment in exchange for decrypting the victim's files and not leaking the stolen data – High
mystuff.bublup[.]com, bublup-media-production.s3.amazonaws[.]com – data exfiltration domains related to an organization and project management app that has document sharing functionality – High
137.220.61[.]94:4001 – SystemBC C2 related IP address (this tool is often used by other ransomware groups as well) - Medium
173.251.109[.]106 – IP address seen during a SaaS BlackSuit compromise (during file encryption) – Medium
216.151.180[.]147 – IP address seen during a SaaS BlackSuit compromise (during an unusual Teams session) - Medium
MITRE ATT&CK Mapping
Tactic - Technqiue
Account Manipulation - PERSISTENCE - T1098
Alarm Suppression - INHIBIT RESPONSE FUNCTION - T0878
Application Layer Protocol - COMMAND AND CONTROL - T1071
Automated Collection - COLLECTION - T1119
Block Command Message - INHIBIT RESPONSE FUNCTION - T0803
Block Reporting Message - INHIBIT RESPONSE FUNCTION - T0804
Onomastics Gymnastics: How Darktrace Detects Spoofing and Business Email Compromise in Multi-Name Users
Note: For privacy reasons, actual surnames and email addresses observed in these incidents below have been replaced with fictitious placeholder names, using the common Spanish names “Fulano” and “Mengano”.
Naming conventions
Modeling names and their variants of members of an organization is a critical component to properly detect if those same names and variants are being spoofed by malicious actors. For many predominantly English-speaking organizations, these variants can largely be captured by variants of a person’s given name (e.g. James-Jimmy-Jim) and a consistent, singular surname or family name (e.g. Smith). Naming conventions, however, are far from universal. This piece will review how Darktrace / EMAIL manages the common naming conventions of much of the Spanish-speaking world, and can use its modeling to create high-fidelity detections of multiple types of spoofing attempts.
A brief summary of the common convention across Spain and much of Spanish-speaking America: most people are given one or two given names (e.g. Roberto, Juan, María, Natalia), and their surnames are the first surname of their father, followed by the first surname of their mother. While there are various exceptions to this norm, the below graphic Wikipedia [1][2] highlights the general rule.
Figure 1: Example Spanish naming convention for father “José García Torres” and mother “María Acosta Gómez” for child “Pablo García Acosta”. If shortened to one surname, the convention holds the child would be referred to as “Pablo García” [1].
Detection of improper name usage
Implicit in the above comment that shortening to one surname follows the convention of using the first surname, shortening to the second surname is often a tell-tale sign of someone unfamiliar with the person or their broader culture. This can be a useful corroborating feature in detecting a spoof attempt – analogous to a spelling error.
In the case of a Spanish customer, this misuse of name shortening contributed to the detection of a spoof attempt trying to solicit a response by impersonating an internal user forwarding information about ‘Data Protection’.
Figure 2: The Cyber AI Analyst summary of the Darktrace / EMAIL detections shows the use of the Gmail sender impersonating Isabel Maria Fulano Mengano, but incorrectly uses the second surname Mengano.
While the limited communication history from the sender and the nature of the text content already marks the mail as suspicious, Darktrace / EMAIL notes the personal name used in the email is similar to a high-value user (‘whale’ to use the terminology of spearphishing). The additional context provided by the detection of the attempted spoof prompted more severe actioning of this email, leading to a ‘Hold’ action instead of a less-severe ‘Unspoof’ action via a banner on the email.
Figure 3: The content summary of the sender showing the ‘Personal’ field of the email being ‘Isabel Mengano’, breaking from the standard name-shortening convention. The additional metrics identify features that might be anomalous about the sender.
Malicious email properly using both surnames
Misusing the name-shortening convention is not the only way that Darktrace / EMAIL can detect spoofing attempts. In the case of another Spanish customer, Darktrace observed a whale impersonation being sent to 230 users with solicitation content, but no links or attachments. Although the name was modeled internally in the “Surname, Given-name” format, Darktrace identified the spoofing attempt targeting a high-value user and took action, blocking the series of emails from reaching end-user inboxes to prevent unsuspecting users from responding.
Figure 4: Cyber AI Analyst summary of a suspicious email. The personal field is visible as ‘juan fulano mengano’, which is consistent with the reverse-order modelled user ‘fulano mengano, juan’. The subject line ‘Urgent Request’ sent to 230 users gives an intuitive indicator of the emails potentially being part of a malicious solicitation campaign.
In Summary: A case of onomastics gymnastics
The variety in valid usage of human language can be a barrier to evaluating when a given text is benign or malicious. Despite this, Darktrace / EMAIL is designed to manage this variety, as exemplified by the detections of two spoofing attempts seen against organizations using the distinct Spanish-speaking world’s common naming convention. The scope of this design as seen in this onomastic context, extends to a wide range of detections surrounding emails and their behavioral anomalies.
Credit to Roberto Romeu (Principal Cyber Analyst), Justin Torres (Senior Cyber Analyst) and Natalia Sánchez Rocafort (Senior Analyst Consultant).
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![One of the compromised devices was seen sending 80 GB of data to bublup-media-production.s3.amazonaws[.]com within a span of 4 hours.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/673513f1135d0e009caf256f_6735136a725738f4afe4de2b_Fig%25207.png)
