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Ransomware
REvil의 Ransomware-as-a-Service 비즈니스 모델보다 앞서 나가기
REvil, also known as Sodinokibi, is a Ransomware-as-a-Service (RaaS) gang responsible for one of the largest ransomware attacks in history. On 14th January 2022, Russia announced it had arrested 14 members of the criminal gang. The move came at the request of the US authorities, who have worked hard with international partners to crack down on the gang. Last year, multiple high-profile attacks were attributed to the REvil group, including the JBS ransomware and Kaseya supply chain incidents.
The arrests are certainly a victory for western law enforcement agencies, and follows November’s announcement from Europol that seven arrests of REvil affiliates had been made in the preceding months. The question is: to what extent will these arrests disrupt the gang’s operations, and for how long?
Early indications from security researchers at ReversingLabs indicates REvil activity has been unaffected. Statistics on REvil implants two weeks after the Russian arrests are unchanged, and if anything indicate a modest increase.
This continued activity implies one of two scenarios:
- The flurry of arrests have only impacted ‘middle men’ within the criminal gang’s hierarchy
- REvil’s ransomware-as-a-service model is resilient enough to survive disruption from law enforcement
Both scenarios are worrisome to those who may fall prey to ransomware gangs, and the reality is likely to be a far more complex mixture of these and other factors. The crackdown on ransomware is long overdue, but the battle is likely to be a long one. Law enforcement agencies need to disrupt the business model to such an extent that it no longer becomes profitable or favorable to be in the ransomware business, and this is likely to take months or even years.
So as the crackdown on ransomware plays out on the biggest stage, what comfort, if any, can security teams take from recent events?
Staying ahead of the evolving RaaS model with AI
A joint report on ransomware issued recently by the FBI, CISA, the NCSC, the ACSC and the NSA highlighted key trends over the past year:
- RaaS has become increasingly professionalized, with business models and processes now well established.
- The business model complicates attribution because there are complex networks of developers, affiliates, and freelancers.
- Ransomware groups are sharing victim information with each other, diversifying the threat to targeted organizations.
In summary, the report illuminates how ransomware gangs have become increasingly adaptable when it comes to evading law enforcement and maximizing profit from ransom payments. Multiple groups have faded away, or retired, only to reappear under a different name and with a slightly updated playbook. The tactics, techniques, and procedures (TTPs) differ from victim to victim, largely because attacks are conducted by different ransomware operators and affiliates.
This is troubling for law enforcement bodies trying to crack down on the individuals behind these attacks. When a RaaS group like REvil consists of an amorphous and ever-changing web of associates, making individual arrests is a constant game of catch up, and will be unlikely to bring down the group as a whole.
The same battle is being played out on the scale of individual attack campaigns. Security tools focused on the hallmarks of previously encountered threats are also in a continuous state of catch up: by the time a single attack is detected, fingerprinted, and stored for next time, attackers and their techniques have moved on.
But there is another option available to defenders, who are increasingly turning to Self-Learning AI to stay one step ahead of attackers. By learning its digital surroundings and identifying subtle deviations indicative of an attack, this technology can detect and respond to novel attacks on the first encounter. Below is an example of how Self-Learning AI detected an attack launched by REvil without the use of rules or signatures.
REvil threat find
In the summer of 2021, a REvil affiliate launched an attack against a health and social care organization – a sector that has seen a big increase in cyber-attacks since the start of the global pandemic. While the attack was detected by Darktrace’s AI without using rules or signatures, the security team was not monitoring Darktrace at the time. In the absence of Autonomous Response – which would have taken targeted action to contain the threat – the attack was allowed to progress.
After gaining access to the network via the laptop of a remote worker, the attacker was able to abuse a legitimate remote desktop (RDP) connection to a corporate jump server to bruteforce additional credentials.
Once equipped with more credentials, the attacker connected to multiple internal devices via RDP, including a second jump server. Data exfiltration began from the initially compromised server over RDP port 3389.
Two weeks later, the attacker identified the organization’s crown jewels, stored on a third server, and attempted to initiate command and control (C2) communications. The server made a number of unusual external connections, including attempts to connect to a rare domain that resembled the pattern of activity associated with REvil’s earlier Kaseya ransomware campaign.
Darktrace for Endpoint, which was running on remote user devices, provided additional visibility, enabling the security team to determine the initially compromised user device. Had Antigena been active on the endpoint, it would have intervened to stop this unusual activity by blocking the specific unusual connections – containing the attack without impacting normal business operations.
Connecting the dots of a low-and-slow attack
The total dwell time of the attacker was 22 days. They were patient, and undertook actions in bursts of activity often with days in between. This pattern of behavior is not uncommon for ransomware attacks, particularly those using the RaaS model in which each step may be performed by different gang members or affiliates.
Darktrace’s Cyber AI Analyst was able to track in real time the complete attack lifecycle over several weeks, stitching together the separate phases of the attack into a coherent security incident.
Figure 1: Cyber AI Analyst reveals the complete attack kill chain
New name, same game
This attack is another case of threat actors living off the land: using legitimate programs and processes that were already in use in the environment to perform malicious activity. This can be very difficult to detect with traditional tools that are based on static use cases and cannot differentiate a legitimate RDP session from a malicious one.
As cyber-criminal groups like REvil continue to defy law enforcement efforts, defenders need to stay ahead with AI technology that learns its environment, adapts as it changes and grows, and responds to threats based on subtle deviations that indicate an emerging attack. Autonomous Response has been adopted by over thousands of organizations across all areas of the digital estate – from email and cloud services to endpoint devices, stopping ransomware attacks early, before encryption is achieved.
Thanks to Darktrace analyst Petal Beharry for her insights on the above threat find.
Technical details
Darktrace model detections:
- Device / RDP Scan
- Device / Bruteforce Activity
- Compliance / Outbound Remote Desktop
- Anomalous Connection / Upload via Remote Desktop
- Anomalous Connection / Download and Upload
- Anomalous Connection / Uncommon 1 GiB Outbound
- Anomalous Connection / Active Remote Desktop Tunnel
- Device / New or Uncommon SMB Named Pipe
- Device / Large Number of Connections to New Endpoints
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Inside the SOC
How Abuse of ‘PerfectData Software’ May Create a Perfect Storm: An Emerging Trend in Account Takeovers
Amidst the ever-changing threat landscape, new tactics, techniques, and procedures (TTPs) seem to emerge daily, creating extreme challenges for security teams. The broad range of attack methods utilized by attackers seems to present an insurmountable problem: how do you defend against a playbook that does not yet exist?
Faced with the growing number of novel and uncommon attack methods, it is essential for organizations to adopt a security solution able to detect threats based on their anomalies, rather than relying on threat intelligence alone.
In March 2023, Darktrace observed an emerging trend in the use of an application known as ‘PerfectData Software’ for probable malicious purposes in several Microsoft 365 account takeovers.
Using its anomaly-based detection, Darktrace DETECT™ was able to identify the activity chain surrounding the use of this application, potentially uncovering a novel piece of threat actor tradecraft in the process.
Microsoft 365 Intrusions
In recent years, Microsoft’s Software-as-a-Service (SaaS) suite, Microsoft 365, along with its built-in identity and access management (IAM) service, Azure Active Directory (Azure AD), have been heavily targeted by threat actors due to their near-ubiquitous usage across industries. Four out of every five Fortune 500 companies, for example, use Microsoft 365 services [1].
Malicious actors typically gain entry to organizations’ Microsoft 365 environments by abusing either stolen account credentials or stolen session cookies [2]. Once inside, actors can access sensitive data within mailboxes or SharePoint repositories, and send out emails or Teams messages. This activity can often result in serious financial harm, especially in cases where the malicious actor’s end-goal is to elicit fraudulent transactions.
Darktrace regularly observes malicious actors behaving in predictable ways once they gain access to customer Microsoft 365 environment. One typical example is the creation of new inbox rules and sending deceitful emails intended to convince recipients to carry out subsequent actions, such as following a malicious link or providing sensitive information. It is also common for actors to register new applications in Azure AD so that they can be used to conduct follow-up activities, like mass-mailing or data theft. The registration of applications in Azure AD therefore seems to be a relatively predictable threat actor behavior [3][4]. Darktrace DETECT understands that unusual application registrations in Azure AD may constitute a deviation in expected behavior, and therefore a possible indicator of account compromise.
These registrations of applications in Azure AD are evidenced by creations of, as well as assignments of permissions to, Service Principals in Azure AD. Darktrace has detected a growing trend in actors creating and assigning permissions to a Service Principal named ‘PerfectData Software’. Further investigation of this Azure AD activity revealed it to be part of an ongoing account takeover.
‘PerfectData Software’ Activity
Darktrace observed variations of the following pattern of activity relating to an application named ‘PerfectData Software’ within its customer base:
- Actor signs in to a Microsoft 365 account from an endpoint associated with a Virtual Private Server (VPS) or Virtual Private Network (VPN) service
- Actor registers an application called 'PerfectData Software' with Azure AD, and then grants permissions to the application
- Actor accesses mailbox data and creates inbox rule
In two separate incidents, malicious actors were observed conducting their activities from endpoints associated with VPN services (HideMyAss (HMA) VPN and Surfshark VPN, respectively) and from endpoints within the Autonomous System AS396073 MAJESTIC-HOSTING-01.
In March 2023, Darktrace observed a malicious actor signing in to a Microsoft 365 account from a Kuwait-based IP address within the Autonomous System, AS198605 AVAST Software s.r.o. This IP address is associated with the VPN service, HMA VPN. Over the next couple of days, an actor (likely the same malicious actor) signed in to the account several more times from two different Nigeria-based endpoints, as well as a VPS-related endpoint and a HMA VPN endpoint.
During their login sessions, the actor performed a variety of actions. First, they created and assigned permissions to a Service Principal named ‘PerfectData Software’. This Service Principal creation represents the registration of an application called ‘PerfectData Software’ in Azure AD. Although the reason for registering this application is unclear, within a few days the actor registered and granted permission to another application, ‘Newsletter Software Supermailer’, and created a new inbox rule names ‘s’ on the mailbox of the hijacked account. This inbox rule moved emails meeting certain conditions to a folder named ‘RSS Subscription. The ‘Newsletter Software Supermailer’ application was likely registered by the actor to facilitate mass-mailing activity.
Immediately after these actions, Darktrace detected the actor sending out thousands of malicious emails from the account. The emails included an attachment named ‘Credit Transfer Copy.html’, which contained a suspicious link. Further investigation revealed that the customer’s network had received several fake invoice emails prior to this initial intrusion activity. Additionally, there was an unusually high volume of failed logins to the compromised account around the time of the initial access.
In a separate case also observed by Darktrace in March 2023, a malicious actor was observed signing in to a Microsoft 365 account from an endpoint within the Autonomous System, AS397086 LAYER-HOST-HOUSTON. The endpoint appears to be related to the VPN service, Surfshark VPN. This login was followed by several failed and successful logins from a VPS-related within the Autonomous System, AS396073 MAJESTIC-HOSTING-01. The actor was then seen registering and assigning permissions to an application called ‘PerfectData Software’. As with the previous example, the motives for this registration are unclear. The actor proceeded to log in several more times from a Surfshark VPN endpoint, however, they were not observed carrying out any further suspicious activity.
It was not clear in either of these examples, nor in fact any of cases observed by Darktrace, why actors had registered and assigned permissions to an application called ‘PerfectData Software’, and there do not appear to be any open-source intelligence (OSINT) resources or online literature related to the malicious usage of an application by that name. That said, there are several websites which appear to provide email migration and data recovery/backup tools under the moniker ‘PerfectData Software’.
It is unclear whether the use of ‘PerfectData Software’ by malicious actors observed on the networks of Darktrace customers was one of these tools. However, given the nature of the tools, it is possible that the actors intended to use them to facilitate the exfiltration of email data from compromises mailboxes.
If the legitimate software ‘PerfectData’ is the application in question in these incidents, it is likely being purchased and misused by attackers for malicious purposes. It is also possible the application referenced in the incidents is a spoof of the legitimate ‘PerfectData’ software designed to masquerade a malicious application as legitimate.
Darktrace Coverage
Cases of ‘PerfectData Software’ activity chains detected by Darktrace typically began with an actor signing into an internal user’s Microsoft 365 account from a VPN or VPS-related endpoint. These login events, along with the suspicious email and/or brute-force activity which preceded them, caused the following DETECT models to breach:
- SaaS / Access / Unusual External Source for SaaS Credential Use
- SaaS / Access / Suspicious Login Attempt
- SaaS / Compromise / Login From Rare Following Suspicious Login Attempt(s)
- SaaS / Email Nexus / Unusual Location for SaaS and Email Activity
Subsequent activities, including inbox rule creations, registration of applications in Azure AD, and mass-mailing activity, resulted in breaches of the following DETECT models.
- SaaS / Admin / OAuth Permission Grant
- SaaS / Compromise / Unusual Logic Following OAuth Grant
- SaaS / Admin / New Application Service Principal
- IaaS / Admin / Azure Application Administration Activities
- SaaS / Compliance / New Email Rule
- SaaS / Compromise / Unusual Login and New Email Rule
- SaaS / Email Nexus / Suspicious Internal Exchange Activity
- SaaS / Email Nexus / Possible Outbound Email Spam
- SaaS / Compromise / Unusual Login and Outbound Email Spam
- SaaS / Compromise / Suspicious Login and Suspicious Outbound Email(s)
In cases where Darktrace RESPOND™ was enabled in autonomous response mode, ‘PerfectData Software’ activity chains resulted in breaches of the following RESPOND models:
• Antigena / SaaS / Antigena Suspicious SaaS Activity Block
• Antigena / SaaS / Antigena Significant Compliance Activity Block
In response to these model breaches, Darktrace RESPOND took immediate action, performing aggressive, inhibitive actions, such as forcing the actor to log out of the SaaS platform, and disabling the user entirely. When applied autonomously, these RESPOND actions would seriously impede an attacker’s progress and minimize network disruption.
In addition, Darktrace Cyber AI Analyst was able to autonomously investigate registrations of the ‘PerfectData Software’ application and summarized its findings into digestible reports.
Conclusion
Due to the widespread adoption of Microsoft 365 services in the workplace and continued emphasis on a remote workforce, account hijackings now pose a more serious threat to organizations around the world than ever before. The cases discussed here illustrate the tendency of malicious actors to conduct their activities from endpoints associated with VPN services, while also registering new applications, like PerfectData Software, with malicious intent.
While it was unclear exactly why the malicious actors were using ‘PerfectData Software’ as part of their account hijacking, it is clear that either the legitimate or spoofed version of the application is becoming an very likely emergent piece of threat actor tradecraft.
Darktrace DETECT’s anomaly-based approach to threat detection allowed it to recognize that the use of ‘PerfectData Software’ represented a deviation in the SaaS user’s expected behavior. While Darktrace RESPOND, when enabled in autonomous response mode, was able to quickly take preventative action against threat actors, blocking the potential use of the application for data exfiltration or other nefarious purposes.
Appendices
MITRE ATT&CK Mapping
Reconnaissance:
• T1598 – Phishing for Information
Credential Access:
• T1110 – Brute Force
Initial Access:
• T1078.004 – Valid Accounts: Cloud Accounts
Command and Control:
• T1105 – Ingress Tool Transfer
Persistence:
• T1098.003 – Account Manipulation: Additional Cloud Roles
Collection:
• T1114 – Email Collection
Defense Evasion:
• T1564.008 – Hide Artifacts: Email Hiding Rules
Lateral Movement:
• T1534 – Internal Spearphishing
Unusual Source IPs
• 5.62.60[.]202 (AS198605 AVAST Software s.r.o.)
• 160.152.10[.]215 (AS37637 Smile-Nigeria-AS)
• 197.244.250[.]155 (AS37705 TOPNET)
• 169.159.92[.]36 (AS37122 SMILE)
• 45.62.170[.]237 (AS396073 MAJESTIC-HOSTING-01)
• 92.38.180[.]49 (AS202422 G-Core Labs S.A)
• 129.56.36[.]26 (AS327952 AS-NATCOM)
• 92.38.180[.]47 (AS202422 G-Core Labs S.A.)
• 107.179.20[.]214 (AS397086 LAYER-HOST-HOUSTON)
• 45.62.170[.]31 (AS396073 MAJESTIC-HOSTING-01)
References
[1] https://www.investing.com/academy/statistics/microsoft-facts/
[2] https://intel471.com/blog/countering-the-problem-of-credential-theft
[3] https://darktrace.com/blog/business-email-compromise-to-mass-phishing-campaign-attack-analysis
[4] https://darktrace.com/blog/breakdown-of-a-multi-account-compromise-within-office-365
Blog
클라우드
Darktrace Integrates Self-Learning AI with Amazon Security Lake to Support Security Investigations
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Darktrace has deepened its relationship with AWS by integrating its detection and response capabilities with Amazon Security Lake.
This development will allow mutual customers to seamlessly combine Darktrace AI’s bespoke understanding of their organization with the Threat Intelligence offered by other security tools, and investigate all of their alerts in one central location.
This integration will improve the value security teams get from both products, streamlining analyst workflows and improving their ability to detect and respond to the full spectrum of known and unknown cyber-threats.
How Darktrace and Amazon Security Lake augment security teams
Amazon Security Lake is a newly-released service that automatically centralizes an organization’s security data from cloud, on-premises, and custom sources into a customer owned purpose-built data lake. Both Darktrace and Amazon Security Lake support the Open Cybersecurity Schema Framework (OCSF), an open standard to simplify, combine, and analyze security logs.
Customers can store security logs, events, alerts, and other relevant data generated by various AWS services and security tools. By consolidating security data in a central lake, organizations can gain a holistic view of their security posture, perform advanced analytics, detect anomalies and open investigations to improve their security practices.
With Darktrace DETECT and RESPOND AI engines covering all assets across IT, OT, network, endpoint, IoT, email and cloud, organizations can augment the value of their security data lakes by feeding Darktrace’s rich and context-aware datapoints to Amazon Security Lake.
Amazon Security Lake empowers security teams to improve the protection of your digital estate:
- Quick and painless data normalization
- Fast-tracks ability to investigate, triage and respond to security events
- Broader visibility aids more effective decision-making
- Surfaces and prioritizes anomalies for further investigation
- Single interface for seamless data management
How will Darktrace customers benefit?
Across the Cyber AI Loop, all Darktrace solutions have been architected with AWS best practices in mind. With this integration, Darktrace is bringing together its understanding of ‘self’ for every organization with the centralized data visibility of the Amazon Security Lake. Darktrace’s unique approach to cyber security, powered by groundbreaking AI research, delivers a superior dataset based on a deep and interconnected understanding of the enterprise.
Where other cyber security solutions are trained to identify threats based on historical attack data and techniques, Darktrace DETECT gains a bespoke understanding of every digital environment, continuously analyzing users, assets, devices and the complex relationships between them. Our AI analyzes thousands of metrics to reveal subtle deviations that may signal an evolving issue – even unknown techniques and novel malware. It distinguishes between malicious and benign behavior, identifying harmful activity that typically goes unnoticed. This rich dataset is fed into RESPOND, which takes precise action to neutralize threats against any and every asset, no matter where data resides.
Both DETECT and RESPOND are supported by Darktrace Self-Learning AI, which provides full, real-time visibility into an organization’s systems and data. This always-on threat analysis already makes humans better at cyber security, improving decisions and outcomes based on total visibility of the digital ecosystem, supporting human performance with AI coverage and empowering security teams to proactively protect critical assets.
Converting Darktrace alerts to the Amazon Security Lake Open Cybersecurity Schema Framework (OCSF) supplies the Security Operations Center (SOC) and incident response team with contextualized data, empowering them to accelerate their investigation, triage and response to potential cyber threats.
Darktrace is available for purchase on the AWS Marketplace.
Learn more about how Darktrace provides full-coverage, AI-powered cloud security for AWS, or see how our customers use Darktrace in their AWS cloud environments.
