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May 12, 2021

How AI Protects Critical Infrastructure From Ransomware

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12
May 2021
Explore the role of AI in safeguarding critical infrastructure from ransomware, as revealed by Darktrace's latest insights.

Modern Threats to OT Environments

At the 2021 RSA cyber security conference, US Secretary of Homeland Security Alejandro Mayorkas made an era-defining statement regarding the cyber security landscape: “Let me be clear: ransomware now poses a national security threat.”

Last weekend, Mayorkas’ words rang true. A ransomware attack on the Colonial Pipeline – responsible for nearly half of the US East Coast’s diesel, gasoline, and jet fuel – resulted in the shutdown of a critical fuel network supplying a number of Eastern states.

The fallout from the attack demonstrated how widespread and damaging the consequences of ransomware can be. Against critical infrastructure and utilities, cyber-attacks have the potential to disrupt supplies, harm the environment, and even threaten human lives.

Though full details remain to be confirmed, the attack is reported to have been conducted by an affiliate of the cyber-criminal group called DarkSide, and likely leveraged common remote desktop tools. Remote access has been enabled as an exploitable vulnerability within critical infrastructure by the shift to remote work that many organizations made last year, including those with Industrial Control Systems (ICS) and Operational Technology (OT).

The rise of industrial ransomware

Ransomware against industrial environments is on the rise, with a reported 500% increase since 2018. Oftentimes, these threats leverage the convergence of IT and OT systems, first targeting IT before pivoting to OT. This was seen with the EKANS ransomware that included ICS processes in its ‘kill list’, as well as the Cring ransomware that compromised ICS after first exploiting a vulnerability in a virtual private network (VPN).

It remains to be seen whether the initial attack vector in the Colonial Pipeline compromise exploited a technical vulnerability, compromised credentials, or a targeted spear phishing campaign. It has been reported that the attack first impacted IT systems, and that Colonial then shut down OT operations as a safety precaution. Colonial confirms that the ransomware “temporarily halted all pipeline operations and affected some of our IT systems,” showing that, ultimately, both OT and IT were affected. This is a great example of how many OT systems depend on IT, such that an IT cyber-attack has the ability to take down OT and ICS processes.

In addition to locking down systems, the threat actors also stole 100GB of sensitive data from Colonial. This kind of double extortion attack — in which data is exfiltrated before files are encrypted — has unfortunately become the norm rather than the exception, with over 70% of ransomware attacks involving exfiltration. Some ransomware gangs have even announced that they are dropping encryption altogether in favor of data theft and extortion methods.

Earlier this year, Darktrace defended against a double extortion ransomware attack waged against a critical infrastructure organization, which also leveraged common remote access tools. This blog will outline the threat find in depth, showing how Darktrace’s self-learning AI responded autonomously to an attack strikingly similar to the Colonial Pipeline incident.

Darktrace threat find

Ransomware against electric utilities equipment supplier

In an attack against a North American equipment supplier for electrical utilities earlier this year, Darktrace/OT demonstrated its ability to protect critical infrastructure against double extortion ransomware that targeted organizations with ICS and OT.

The ransomware attack initially targeted IT systems, and, thanks to self-learning Cyber AI, was stopped before it could spill over into OT and disrupt operations.

The attacker first compromised an internal server in order to exfiltrate data and deploy ransomware over the course of 12 hours. The short amount of time between initial compromise and deployment is unusual, as ransomware threat actors often wait several days to spread stealthily as far across the cyber ecosystem as possible before striking.

Figure 1: A timeline of the attack

How did the attack bypass the rest of the security stack?

The attacker leveraged ‘Living off the Land’ techniques to blend into the business’ normal ‘patterns of life’, using a compromised admin credential and a remote management tool approved by the organization, in its attempts to remain undetected.

Darktrace commonly sees the abuse of legitimate remote management software in attackers’ arsenal of techniques, tactics, and procedures (TTPs). Remote access is also becoming an increasingly common vector of attack in ICS attacks in particular. For example, in the cyber-incident at the Florida water treatment facility last February, attackers exploited a remote management tool in attempts to manipulate the treatment process.

The specific strain of ransomware deployed by this attacker also successfully evaded detection by anti-virus by using a unique file extension when encrypting files. These forms of ‘signatureless’ ransomware easily slip past legacy approaches to security that rely on rules, signatures, threat feeds, and lists of documented Common Vulnerabilities and Exposures (CVEs), as these are methods that can only detect previously documented threats.

The only way to detect never-before-seen threats like signatureless ransomware is for a technology to find anomalous behavior, rather than rely on lists of ‘known bads’. This can be achieved with self-learning technology, which spots even the most subtle deviations from the normal ‘patterns of life’ for all devices, users, and all the connections between them.

Darktrace insights

Initial compromise and establishing foothold

Despite the abuse of a legitimate tool and the absence of known signatures, Darktrace/OT was able to use a holistic understanding of normal activity to detect the malicious activity at multiple points in the attack lifecycle.

The first clear sign of an emerging threat that was alerted by Darktrace was the unusual use of a privileged credential. The device also served an unusual remote desktop protocol (RDP) connection from a Veeam server shortly before the incident, indicating that the attacker may have moved laterally from elsewhere in the network.

Three minutes later, the device initiated a remote management session which lasted 21 hours. This allowed the attacker to move throughout the broader cyber ecosystem while remaining undetected by traditional defences. Darktrace, however, was able to detect unusual remote management usage as another early warning indicative of an attack.

Double threat part one: Data exfiltration

One hour after the initial compromise, Darktrace detected unusual volumes of data being sent to a 100% rare cloud storage solution, pCloud. The outbound data was encrypted using SSL, but Darktrace created multiple alerts relating to large internal downloads and external uploads that were a significant deviation from the device’s normal ‘pattern of life’.

The device continued to exfiltrate data for nine hours. Analysis of the files downloaded by the device, which were transferred using the unencrypted SMB protocol, suggests that they were sensitive in nature. Fortunately, Darktrace was able to pinpoint the specific files that were exfiltrated so that the customer could immediately evaluate the potential implications of the compromise.

Double threat part two: File encryption

A short time later, at 01:49 local time, the compromised device began encrypting files in a SharePoint back-up share drive. Over the next three and a half hours, the device encrypted over 13,000 files on at least 20 SMB shares. In total, Darktrace produced 23 alerts for the device in question, which amounted to 48% of all the alerts produced in the corresponding 24-hour period.

Darktrace’s Cyber AI Analyst then automatically launched an investigation, identifying the internal data transfers and the file encryption over SMB. From this, it was able to present incident reports that connected the dots among these disparate anomalies, piecing them together into a coherent security narrative. This put the security team in a position to immediately take remediating action.

If the customer had been using Darktrace’s autonomous response technology, there is no doubt the activity would have been halted before significant volumes of data could have been exfiltrated or files encrypted. Fortunately, after seeing both the alerts and Cyber AI Analyst reports, the customer was able to use Darktrace’s ‘Ask the Expert’ (ATE) service for incident response to mitigate the impact of the attack and assist with disaster recovery.

Figure 2: AI Analyst Incident reporting an unusual reprogram command using the MODBUS protocol. The incident includes a plain English summary, relevant technical information, and the investigation process used by the AI.  

Detecting the threat before it could disrupt critical infrastructure

The targeted supplier was overseeing OT and had close ties to critical infrastructure. By facilitating the early-stage response, Darktrace prevented the ransomware from spreading further onto the factory floor. Crucially, Darktrace also minimized operational disruption, helping to avoid the domino effect which the attack could have had, affecting not only the supplier itself, but also the electric utilities that this supplier supports.

As both the recent Colonial Pipeline incident and the above threat find reveal, ransomware is a pressing concern for organizations overseeing industrial operations across all forms of critical infrastructure, from pipelines to the power grid and its suppliers. With self-learning AI, these attack vectors can be dealt with before the damage is done through real-time threat detection, autonomous investigations, and — if activated — targeted machine-speed response.

Looking forward: Using Self-Learning AI to protect critical infrastructure across the board

In late April, the Biden administration announced an ambitious effort to “safeguard US critical infrastructure from persistent and sophisticated threats.” The Department of Energy’s (DOE) 100-day plan specifically seeks technologies “that will provide cyber visibility, detection, and response capabilities for industrial control systems of electric utilities.”

The Biden administration’s cyber sprint clearly calls for a technology that protects critical energy infrastructure, rather than merely best practice measures and regulations. As seen in the above threat find, Darktrace AI is a powerful technology that leverages unsupervised machine learning to autonomously safeguard critical infrastructure and its suppliers with machine speed and precision.

Darktrace enhances detection, mitigation, and forensic capabilities to detect  sophisticated and novel attacks, along with insider threats and pre-existing infections, using Self-Learning Cyber AI, without rules, signatures, or lists of CVEs. Incident investigations provided in real time by Cyber AI Analyst jumpstart remediation with actionable insights, containing emerging attacks at their early stages, before they escalate into crisis.

Enable near real-time situational awareness and response capabilities

Darktrace immediately understands, identifies, and investigates all anomalous activity in ICS/OT networks, whether human or machine driven. Additionally, Darktrace actions targeted response where appropriate to neutralize threats, either actively or in human confirmation mode. Because Self-learning AI adapts alongside evolutions in the ecosystem, organizations benefit from real-time awareness with no tuning or human input necessary

Deploy technologies to increase visibility of threats in ICS and OT systems

Darktrace contextualizes security events, adapts to novel techniques, and translates findings into a security narrative that can be actioned by humans in minutes. Delivering a unified view across IT and OT systems.

Darktrace detects, investigates, and responds to threats at higher Purdue levels and in IT systems before they ‘spill over’ into OT. ‘Plug and play’ deployment seamlessly integrates with technological architecture, presenting 3D network topology with granular visibility into all users, devices, and subnets.

Darktrace's asset identification continuously catalogues all ICS/OT devices and identifies and investigates all threatening activity indicative of emerging attacks – be it ICS ransomware, APTs, zero-day exploits, insider threats, pre-existing infections, DDoS, crypto-mining, misconfigurations, or never-before-seen attacks.

Thanks to Darktrace analyst Oakley Cox for his insights on the above threat find.

Darktrace model detections:

  • Initial compromise:
  • User / New Admin Credential on Client
  • Data exfiltration:
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Low and Slow Exfiltration
  • Device / Anomalous SMB Followed by Multiple Model Breaches
  • Anomalous Connection / Download and Upload
  • File encryption:
  • Compromise / Ransomware / Suspicious SMB Activity
  • Anomalous Connection / SMB Enumeration
  • Device / Anomalous RDP Followed by Multiple Model Breaches
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous Connection / Sustained MIME Type Conversion
  • Anomalous Connection / Suspicious Read Write Ratio
  • Device / Multiple Lateral Movement Model Breaches

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
David Masson
VP, Enterprise Security

David Masson is Darktrace’s Director of Enterprise Security, and has over two decades of experience working in fast moving security and intelligence environments in the UK, Canada and worldwide. With skills developed in the civilian, military and diplomatic worlds, he has been influential in the efficient and effective resolution of various unique national security issues. David is an operational solutions expert and has a solid reputation across the UK and Canada for delivery tailored to customer needs. At Darktrace, David advises strategic customers across North America and is also a regular contributor to major international and national media outlets in Canada where he is based. He holds a master’s degree from Edinburgh University.

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November 1, 2024

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Inside the SOC

Phishing and Persistence: Darktrace’s Role in Defending Against a Sophisticated Account Takeover

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The exploitation of SaaS platforms

As businesses continue to grow and evolve, the need for sharing ideas through productivity and cloud Software-as-a-Service (SaaS) platforms is becoming increasingly crucial. However, these platforms have also become prime targets for cyber attackers.

Threat actors often exploit these widely-used services to gain unauthorized access, steal sensitive information, and disrupt business operations. The growing reliance on SaaS platforms makes them attractive entry points for cybercriminals, who use sophisticated techniques such as phishing, social engineering, and malware to compromise these systems.

Services like Microsoft 365 are regularly targeted by threat actors looking for an entry point into an organization’s environment to carry out malicious activities. Securing these platforms is crucial to protect business data and ensure operational continuity.

Darktrace / EMAIL detection of the phishing attack

In a recent case, Darktrace observed a customer in the manufacturing sector receiving a phishing email that led to a threat actor logging in and creating an email rule. Threat actors often create email rules to move emails to their inbox, avoiding detection. Additionally, Darktrace detected a spoofed domain registered by the threat actor. Despite already having access to the customer’s SaaS account, the actor seemingly registered this domain to maintain persistence on the network, allowing them to communicate with the spoofed domain and conduct further malicious activity.

Darktrace / EMAIL can help prevent compromises like this one by blocking suspicious emails as soon as they are identified. Darktrace’s AI-driven email detection and response recognizes anomalies that might indicate phishing attempts and applies mitigative actions autonomously to prevent the escalation of an attack.

Unfortunately, in this case, Darktrace was not configured in Autonomous Response mode at the time of the attack, meaning actions had to be manually applied by the customer’s security team. Had it been fully enabled, it would have held the emails, preventing them from reaching the intended recipient and stopping the attack at its inception.

However, Darktrace’s Managed Threat Detection alerted the Security Operations Center (SOC) team to the compromise, enabling them to thoroughly investigate the incident and notify the customer before further damage could occur.

The Managed Threat Detection service continuously monitors customer networks for suspicious activities that may indicate an emerging threat. When such activities are detected, alerts are sent to Darktrace’s expert Cyber Analysts for triage, significantly speeding up the remediation process.

Attack Overview

On May 2, 2024, Darktrace detected a threat actor targeting a customer in the manufacturing sector then an unusual login to their SaaS environment was observed prior to the creation of a new email rule.

Darktrace immediately identified the login as suspicious due to the rarity of the source IP (31.222.254[.]27) and ASN, coupled with the absence of multi-factor authentication (MFA), which was typically required for this account.

The new email rule was intended to mark emails as read and moved to the ‘Conversation History’ folder for inbound emails from a specific domain. The rule was named “….,,,”, likely the attacker attempting to setup their new rule with an unnoteworthy name to ensure it would not be noticed by the account’s legitimate owner. Likewise, by moving emails from a specific domain to ‘Conversation History’, a folder that is rarely used by most users, any phishing emails sent by that domain would remain undetected by the user.

Darktrace’s detection of the unusual SaaS login and subsequent creation of the new email rule “….,,,”.
Figure 1: Darktrace’s detection of the unusual SaaS login and subsequent creation of the new email rule “….,,,”.

The domain in question was identified as being newly registered and an example of a typosquat domain. Typosquatting involves registering new domains with intentional misspelling designed to convince users to visit fake, and often malicious, websites. This technique is often used in phishing campaigns to create a sense of legitimacy and trust and deceive users into providing sensitive information. In this case, the suspicious domain closely resembled several of the customer’s internal domains, indicating an attempt to impersonate the organization’s legitimate internal sites to gain the target’s trust. Furthermore, the creation of this lookalike domain suggests that the attack was highly targeted at this specific customer.

Interestingly, the threat actor registered this spoofed domain despite already having account access. This was likely intended to ensure persistence on the network without having to launch additional phishing attacks. Such use of spoofed domain could allow an attacker to maintain a foothold in their target network and escalate their malicious activities without having to regain access to the account. This persistence can be used for various purposes, including data exfiltration, spreading malware, or launching further attacks.

Following this, Darktrace detected a highly anomalous email being sent to the customer’s account from the same location as the initial unsual SaaS login. Darktrace’s anomaly-based detection is able to identify threats that human security teams and traditional signature-based methods might miss. By analyzing the expected behavior of network users, Darktrace can recognize the subtle deviations from the norm that may indicate malicious activity. Unfortunately, in this instance, without Darktrace’s Autonomous Response capability enabled, the phishing email was able to successfully reach the recipient. While Darktrace / EMAIL did suggest that the email should be held from the recipients inbox, the customer was required to manually approve it.

Despite this, the Darktrace SOC team were still able to support the customer as they were subscribed to the Managed Threat Detection service. Following the detection of the highlight anomalous activity surrounding this compromise, namely the unusual SaaS login followed by a new email rule, an alert was sent to the Darktrace SOC for immediate triage, who then contacted the customer directly urging immediate action.

Conclusion

This case underscores the need to secure SaaS platforms like Microsoft 365 against sophisticated cyber threats. As businesses increasingly rely on these platforms, they become prime targets for attackers seeking unauthorized access and disruption.

Darktrace’s anomaly-based detection and response capabilities are crucial in identifying and mitigating such threats. In this instance, Darktrace detected a phishing email that led to a threat actor logging in and creating a suspicious email rule. The actor also registered a spoofed domain to maintain persistence on the network.

Darktrace / EMAIL, with its AI-driven detection and analysis, can block suspicious emails before they reach the intended recipient, preventing attacks at their inception. Meanwhile, Darktrace’s SOC team promptly investigated the activity and alerted the customer to the compromise, enabling them to take immediate action to remediate the issue and prevent any further damage.

Credit to Vivek Rajan (Cyber Security Analyst) and Ryan Traill (Threat Content Lead).

Appendices

Darktrace Model Detections

  • SaaS / Access / Unusual External Source for SaaS Credential Use
  • SaaS / Compromise / Login From Rare Endpoint While User Is Active
  • SaaS / Resource / Unusual Access to Delegated Resource by Non Owner
  • SaaS / Email Nexus / Unusual Login Location Following Sender Spoof
  • Compliance / Anomalous New Email Rule
  • SaaS / Compromise / Unusual Login and New Email Rule

Indicators of Compromise (IoCs)

IoC - Type - Description + Confidence

31.222.254[.]27 – IP -  Suspicious Login Endpoint

MITRE ATT&CK Mapping

Tactic – Technqiue – Sub-technique of (if applicable)

Cloud Accounts - DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS - T1078.004 - T1078

Cloud Service Dashboard – DISCOVERY - T1538

Compromise Accounts - RESOURCE DEVELOPMENT - T1586

Steal Web Session Cookie - CREDENTIAL ACCESS - T1539

Outlook Rules – PERSISTENCE - T1137.005 - T1137

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About the author
Vivek Rajan
Cyber Analyst

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October 31, 2024

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Inside the SOC

Lifting the Fog: Darktrace’s Investigation into Fog Ransomware

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Introduction to Fog Ransomware

As ransomware attacks continue to be launched at an alarming rate, Darktrace’s Threat Research team has identified that familiar strains like Akira, LockBit, and BlackBasta remain among the most prevalent threats impacting its customers, as reported in the First 6: Half-Year Threat Report 2024. Despite efforts by law agencies, like dismantling the infrastructure of cybercriminals and shutting down their operations [2], these groups continue to adapt and evolve.

As such, it is unsurprising that new ransomware variants are regularly being created and launched to get round law enforcement agencies and increasingly adept security teams. One recent example of this is Fog ransomware.

What is Fog ransomware?

Fog ransomware is strain that first appeared in the wild in early May 2024 and has been observed actively using compromised virtual private network (VPN) credentials to gain access to organization networks in the education sector in the United States.

Darktrace's detection of Fog Ransomware

In June 2024, Darktrace observed instances of Fog ransomware across multiple customer environments. The shortest time observed from initial access to file encryption in these attacks was just 2 hours, underscoring the alarming speed with which these threat actors can achieve their objectives.

Darktrace identified key activities typical of a ransomware kill chain, including enumeration, lateral movement, encryption, and data exfiltration. In most cases, Darktrace was able to successfully halt the progression Fog attacks in their early stages by applying Autonomous Response actions such as quarantining affected devices and blocking suspicious external connections.

To effectively illustrate the typical kill chain of Fog ransomware, this blog focuses on customer environments that did not have Darktrace’s Autonomous Response enabled. In these cases, the attack progressed unchecked and reached its intended objectives until the customer received Darktrace’s alerts and intervened.

Darktrace’s Coverage of Fog Ransomware

Initial Intrusion

After actors had successfully gained initial access into customer networks by exploiting compromised VPN credentials, Darktrace observed a series of suspicious activities, including file shares, enumeration and extensive scanning. In one case, a compromised domain controller was detected making outgoing NTLM authentication attempts to another internal device, which was subsequently used to establish RDP connections to a Windows server running Hyper-V.

Given that the source was a domain controller, the attacker could potentially relay the NTLM hash to obtain a domain admin Kerberos Ticket Granting Ticket (TGT). Additionally, incoming NTLM authentication attempts could be triggered by tools like Responder, and NTLM hashes used to encrypt challenge response authentication could be abused by offline brute-force attacks.

Darktrace also observed the use of a new administrative credential on one affected device, indicating that malicious actors were likely using compromised privileged credentials to conduct relay attacks.

Establish Command-and-Control Communication (C2)

In many instances of Fog ransomware investigated by Darktrace’s Threat Research team, devices were observed making regular connections to the remote access tool AnyDesk. This was exemplified by consistent communication with the endpoint “download[.]anydesk[.]com” via the URI “/AnyDesk.exe”. In other cases, Darktrace identified the use of another remote management tool, namely SplashTop, on customer servers.

In ransomware attacks, threat actors often use such legitimate remote access tools to establish command-and-control (C2) communication. The use of such services not only complicates the identification of malicious activities but also enables attackers to leverage existing infrastructure, rather than having to implement their own.

Internal Reconnaissance

Affected devices were subsequently observed making an unusual number of failed internal connections to other internal locations over ports such as 80 (HTTP), 3389 (RDP), 139 (NetBIOS) and 445 (SMB). This pattern of activity strongly indicated reconnaissance scanning behavior within affected networks. A further investigation into these HTTP connections revealed the URIs “/nice ports”/Trinity.txt.bak”, commonly associated with the use of the Nmap attack and reconnaissance tool.

Simultaneously, some devices were observed engaging in SMB actions targeting the IPC$ share and the named pipe “srvsvc” on internal devices. Such activity aligns with the typical SMB enumeration tactics, whereby attackers query the list of services running on a remote host using a NULL session, a method often employed to gather information on network resources and vulnerabilities.

Lateral Movement

As attackers attempted to move laterally through affected networks, Darktrace observed suspicious RDP activity between infected devices. Multiple RDP connections were established to new clients, using devices as pivots to propagate deeper into the networks, Following this, devices on multiple networks exhibited a high volume of SMB read and write activity, with internal share drive file names being appended with the “.flocked” extension – a clear sign of ransomware encryption. Around the same time, multiple “readme.txt” files were detected being distributed across affected networks, which were later identified as ransom notes.

Further analysis of the ransom note revealed that it contained an introduction to the Fog ransomware group, a summary of the encryption activity that had been carried out, and detailed instructions on how to communicate with the attackers and pay the ransom.

Packet capture (PCAP) of the ransom note file titled “readme.txt”.
Figure 1: Packet capture (PCAP) of the ransom note file titled “readme.txt”.

Data Exfiltration

In one of the cases of Fog ransomware, Darktrace’s Threat Research team observed potential data exfiltration involving the transfer of internal files to an unusual endpoint associated with the MEGA file storage service, “gfs302n515[.]userstorage[.]mega[.]co[.]nz”.

This exfiltration attempt suggests the use of double extortion tactics, where threat actors not only encrypt victim’s data but also exfiltrate it to threaten public exposure unless a ransom is paid. This often increases pressure on organizations as they face the risk of both data loss and reputational damage caused by the release of sensitive information.

Darktrace’s Cyber AI Analyst autonomously investigated what initially appeared to be unrelated events, linking them together to build a full picture of the Fog ransomware attack for customers’ security teams. Specifically, on affected networks Cyber AI Analyst identified and correlated unusual scanning activities, SMB writes, and file appendages that ultimately suggested file encryption.

Cyber AI Analyst’s analysis of encryption activity on one customer network.
Figure 2: Cyber AI Analyst’s analysis of encryption activity on one customer network.
Figure 3: Cyber AI Analysts breakdown of the investigation process between the linked incident events on one customer network.

Safeguarding vulnerable sectors with real-time ransomware mitigation

As novel and fast-moving ransomware variants like Fog persist across the threat landscape, the time taken for from initial compromise to encryption has significantly decreased due to the enhanced skill craft and advanced malware of threat actors. This trend particularly impacts organizations in the education sector, who often have less robust cyber defenses and significant periods of time during which infrastructure is left unmanned, and are therefore more vulnerable to quick-profit attacks.

Traditional security methods may fall short against these sophisticated attacks, where stealthy actors evade detection by human-managed teams and tools. In these scenarios Darktrace’s AI-driven product suite is able to quickly detect and respond to the initial signs of compromise through autonomous analysis of any unusual emerging activity.

When Darktrace’s Autonomous Response capability was active, it swiftly mitigated emerging Fog ransomware threats by quarantining devices exhibiting malicious behavior to contain the attack and blocking the exfiltration of sensitive data, thus preventing customers from falling victim to double extortion attempts.

Insights from Darktrace’s First 6: Half-year threat report for 2024

First 6: half year threat report darktrace screenshot

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

Credit to Qing Hong Kwa (Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore) and Ryan Traill (Threat Content Lead)

Darktrace Model Detections:

- Anomalous Server Activity::Anomalous External Activity from Critical Network Device

- Anomalous Connection::SMB Enumeration

- Anomalous Connection::Suspicious Read Write Ratio and Unusual SMB

- Anomalous Connection::Uncommon 1 GiB Outbound

- Anomalous File::Internal::Additional Extension Appended to SMB File

- Compliance::Possible Cleartext LDAP Authentication

- Compliance::Remote Management Tool On Server

- Compliance::SMB Drive Write

- Compromise::Ransomware::SMB Reads then Writes with Additional Extensions

- Compromise::Ransomware::Possible Ransom Note Write

- Compromise::Ransomware::Ransom or Offensive Words Written to SMB

- Device::Attack and Recon Tools

- User::New Admin Credentials on Client

- Unusual Activity::Anomalous SMB Move & Write

- Unusual Activity::Internal Data Transfer

- Unusual Activity::Unusual External Data Transfer

- Unusual Activity::Enhanced Unusual External Data Transfer

Darktrace Model Detections:

- Antigena::Network::External Threat::Antigena Suspicious File Block

- Antigena::Network::External Threat::Antigena Suspicious File Pattern of Life Block

- Antigena::Network::External Threat::Antigena File then New Outbound Block

- Antigena::Network::External Threat::Antigena Ransomware Block

- Antigena::Network::External Threat::Antigena Suspicious Activity Block

- Antigena::Network::Significant Anomaly::Antigena Controlled and Model Breach

- Antigena::Network::Significant Anomaly::Antigena Enhanced Monitoring from Server Block

- Antigena::Network::Significant Anomaly::Antigena Breaches Over Time Block

- Antigena::Network::Significant Anomaly::Antigena Significant Server Anomaly Block

- Antigena::Network::Insider Threat::Antigena Internal Data Transfer Block

- Antigena::Network::Insider Threat::Antigena Large Data Volume Outbound Block

- Antigena::Network::Insider Threat::Antigena SMB Enumeration Block

AI Analyst Incident Coverage

- Encryption of Files over SMB

- Scanning of Multiple Devices

- SMB Writes of Suspicious Files

MITRE ATT&CK Mapping

(Technique Name) – (Tactic) – (ID) – (Sub-Technique of)

Data Obfuscation - COMMAND AND CONTROL - T1001

Remote System Discovery - DISCOVERY - T1018

SMB/Windows Admin Shares - LATERAL MOVEMENT - T1021.002 - T1021

Rename System Utilities - DEFENSE EVASION - T1036.003 - T1036

Network Sniffing - CREDENTIAL ACCESS, DISCOVERY - T1040

Exfiltration Over C2 Channel - EXFILTRATION - T1041

Data Staged - COLLECTION - T1074

Valid Accounts - DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS - T1078

Taint Shared Content - LATERAL MOVEMENT - T1080

File and Directory Discovery - DISCOVERY - T1083

Email Collection - COLLECTION - T1114

Automated Collection - COLLECTION - T1119

Network Share Discovery - DISCOVERY - T1135

Exploit Public-Facing Application - INITIAL ACCESS - T1190

Hardware Additions - INITIAL ACCESS - T1200

Remote Access Software - COMMAND AND CONTROL - T1219

Data Encrypted for Impact - IMPACT - T1486

Pass the Hash - DEFENSE EVASION, LATERAL MOVEMENT - T1550.002 - T1550

Exfiltration to Cloud Storage - EXFILTRATION - T1567.002 - T1567

Lateral Tool Transfer - LATERAL MOVEMENT - T1570

List of Indicators of Compromise (IoCs)

IoC – Type – Description

/AnyDesk.exe - Executable File - Remote Access Management Tool

gfs302n515[.]userstorage[.]mega[.]co[.]nz- Domain - Exfiltration Domain

*.flocked - Filename Extension - Fog Ransomware Extension

readme.txt - Text File - Fog Ransom Note

xql562evsy7njcsngacphcerzjfecwotdkobn3m4uxu2gtqh26newid[.]onion - Onion Domain - Threat Actor’s Communication Channel

References

[1] https://arcticwolf.com/resources/blog/lost-in-the-fog-a-new-ransomware-threat/

[2] https://intel471.com/blog/assessing-the-disruptions-of-ransomware-gangs

[3] https://www.pcrisk.com/removal-guides/30167-fog-ransomware

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About the author
Qing Hong Kwa
Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore
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