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April 22, 2024
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The State of AI in Cybersecurity: How AI will impact the cyber threat landscape in 2024

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22
Apr 2024
Part 2: This blog discusses the impact of AI on the cyber threat landscape based on data from Darktrace’s State of AI Cybersecurity Report. Get the latest insights into the evolving challenges faced by organizations, the growing demand for skilled professionals, and the need for integrated security solutions.

About the AI Cybersecurity Report

We surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.

This blog is continuing the conversation from our last blog post “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners” which was an overview of the entire report. This blog will focus on one aspect of the overarching report, the impact of AI on the cyber threat landscape.

To access the full report click here.

Are organizations feeling the impact of AI-powered cyber threats?

Nearly three-quarters (74%) state AI-powered threats are now a significant issue. Almost nine in ten (89%) agree that AI-powered threats will remain a major challenge into the foreseeable future, not just for the next one to two years.

However, only a slight majority (56%) thought AI-powered threats were a separate issue from traditional/non AI-powered threats. This could be the case because there are few, if any, reliable methods to determine whether an attack is AI-powered.

Identifying exactly when and where AI is being applied may not ever be possible. However, it is possible for AI to affect every stage of the attack lifecycle. As such, defenders will likely need to focus on preparing for a world where threats are unique and are coming faster than ever before.

a hypothetical cyber attack augmented by AI at every stage

Are security stakeholders concerned about AI’s impact on cyber threats and risks?

The results from our survey showed that security practitioners are concerned that AI will impact organizations in a variety of ways. There was equal concern associated across the board – from volume and sophistication of malware to internal risks like leakage of proprietary information from employees using generative AI tools.

What this tells us is that defenders need to prepare for a greater volume of sophisticated attacks and balance this with a focus on cyber hygiene to manage internal risks.

One example of a growing internal risks is shadow AI. It takes little effort for employees to adopt publicly-available text-based generative AI systems to increase their productivity. This opens the door to “shadow AI”, which is the use of popular AI tools without organizational approval or oversight. Resulting security risks such as inadvertent exposure of sensitive information or intellectual property are an ever-growing concern.

Are organizations taking strides to reduce risks associated with adoption of AI in their application and computing environment?

71.2% of survey participants say their organization has taken steps specifically to reduce the risk of using AI within its application and computing environment.

16.3% of survey participants claim their organization has not taken these steps.

These findings are good news. Even as enterprises compete to get as much value from AI as they can, as quickly as possible, they’re tempering their eager embrace of new tools with sensible caution.

Still, responses varied across roles. Security analysts, operators, administrators, and incident responders are less likely to have said their organizations had taken AI risk mitigation steps than respondents in other roles. In fact, 79% of executives said steps had been taken, and only 54% of respondents in hands-on roles agreed. It seems that leaders believe their organizations are taking the needed steps, but practitioners are seeing a gap.

Do security professionals feel confident in their preparedness for the next generation of threats?

A majority of respondents (six out of every ten) believe their organizations are inadequately prepared to face the next generation of AI-powered threats.

The survey findings reveal contrasting perceptions of organizational preparedness for cybersecurity threats across different regions and job roles. Security administrators, due to their hands-on experience, express the highest level of skepticism, with 72% feeling their organizations are inadequately prepared. Notably, respondents in mid-sized organizations feel the least prepared, while those in the largest companies feel the most prepared.

Regionally, participants in Asia-Pacific are most likely to believe their organizations are unprepared, while those in Latin America feel the most prepared. This aligns with the observation that Asia-Pacific has been the most impacted region by cybersecurity threats in recent years, according to the IBM X-Force Threat Intelligence Index.

The optimism among Latin American respondents could be attributed to lower threat volumes experienced in the region, but it's cautioned that this could change suddenly (1).

What are biggest barriers to defending against AI-powered threats?

The top-ranked inhibitors center on knowledge and personnel. However, issues are alluded to almost equally across the board including concerns around budget, tool integration, lack of attention to AI-powered threats, and poor cyber hygiene.

The cybersecurity industry is facing a significant shortage of skilled professionals, with a global deficit of approximately 4 million experts (2). As organizations struggle to manage their security tools and alerts, the challenge intensifies with the increasing adoption of AI by attackers. This shift has altered the demands on security teams, requiring practitioners to possess broad and deep knowledge across rapidly evolving solution stacks.

Educating end users about AI-driven defenses becomes paramount as organizations grapple with the shortage of professionals proficient in managing AI-powered security tools. Operationalizing machine learning models for effectiveness and accuracy emerges as a crucial skill set in high demand. However, our survey highlights a concerning lack of understanding among cybersecurity professionals regarding AI-driven threats and the use of AI-driven countermeasures indicating a gap in keeping pace with evolving attacker tactics.

The integration of security solutions remains a notable problem, hindering effective defense strategies. While budget constraints are not a primary inhibitor, organizations must prioritize addressing these challenges to bolster their cybersecurity posture. It's imperative for stakeholders to recognize the importance of investing in skilled professionals and integrated security solutions to mitigate emerging threats effectively.

To access the full report click here.

References

1. IBM, X-Force Threat Intelligence Index 2024, Available at: https://www.ibm.com/downloads/cas/L0GKXDWJ

2. ISC2, Cybersecurity Workforce Study 2023, Available at: https://media.isc2.org/-/media/Project/ISC2/Main/Media/ documents/research/ISC2_Cybersecurity_Workforce_Study_2023.pdf?rev=28b46de71ce24e6ab7705f6e3da8637e

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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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