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April 7, 2020

Four Ways Cyber-Criminals Fly Under the Radar

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07
Apr 2020
Learn how cyber criminals evade detection. Darktrace analyses the four ways they operate under the radar. Read here to stay vigilant against cyber attacks.

The challenge of reliably attributing cyber-threats has amplified in recent years, as adversaries adopt a collection of techniques to ensure that even if their attacks are caught, they themselves escape detection and avoid punishment.

Detecting a threat is, of course, a very different technical challenge compared to tracing that activity back to a human operator. Nevertheless, at some point after the dust has settled, during the post-hoc incident analysis for example, someone somewhere may need to know who the suspects are. And in spite of all of our other advances, and also some recent successes in attributing offensive and cyber-criminal acts, only three out of every 100,000 cyber-crimes are prosecuted. Put simply, this is still an unsolved set of problems. Many of the successes we do have can be attributed more to operational security fails on the criminals’ end than any other active approaches. In fact, some recent trends have actually made reliable attribution even more challenging.

The four cyber-threat trends that make attribution difficult

There are four related trends in how threat-actors can procure and obtain attack capabilities that have resulted in an increase in complexity when attempting to reliably identify Tools, Techniques, and Procedures (TTPs) and attributing them to distinct threat-actors.

A Cybercrime-as-a-Service economy and supply chain allowing cyber-criminals to mix and match off the shelf offensive cyber capabilities.

Expansion of ‘Living off the Land’ (LoL) tool usage by threat-actors to evade traditional, signature-based security defenses, and to obfuscate their activity.

While Code Reuse has always existed in the hacker community, copying nation-state-grade attack code has recently become possible.

The barrier to entry for criminally motivated operators has been lowered, providing the means for less technical criminals, who are only limited by time and their imagination.

Figure 1: The four cyber-threat trends

Threat-actors can mix and match attack tools, creating attack stacks that can be tailored for a variety of campaigns.

Between a professional marketplace of cyber-crime tools and services, the increasing adoption of ‘Living off the Land’ techniques, and the reusing of code leaked from nation-state intelligence services, threat-actors with even the most limited technical ability can conduct highly sophisticated criminal campaigns. Prospective cyber-criminals now have four primary types of attack tools to choose from – with three of them brand new or greatly enhanced. Even more importantly, these threat-actors can mix and match attack tools, creating tactically flexible attack stacks that can be tailored for a variety of campaigns against a diverse set of victims.

Off the shelf attacks

The burgeoning and increasingly professional Cybercrime-as-a-Service market (estimated at $1.6B) provides a thriving marketplace of microservices, attack code, and attack platforms. Anyone with a motive and enough bitcoin and enthusiasm can become the next ‘cyber Don Corleone’. Many of these services offer dedicated account management and professional support 24 hours a day. The commercialization of the cyber-crime supply chain has raised the barrier to entry for Cybercrime-as-a-Service vendors, while at the same time lowering it for cyber-criminal operators.

Living off the Land

‘Living off the Land’ (LoL) and “malware-less” attacks have been on the rise for some time now. What makes these attack methods so dangerous is that they leverage standard operating system tools to conduct their nefarious business, making signature-based approaches that look for malware heuristics ineffective – including signature-based Intrusion Protection Systems.

These attacks in particular demonstrate the need for an approach to cyber security that goes beyond looking at what malware is being used. Rather than relying on static blacklists, security teams are instead turning to a more sophisticated approach that learns ‘normal’ for every user and device across an entire business. From that evolving baseline, this approach to defense can identify and contain anomalous activity indicative of a cyber-threat – all in real time.

Code reuse and repurpose

What is new, and unprecedented, is that cyber-criminals are gaining access to intelligence and nation-state grade attack code.

Hackers have always begged, borrowed, and stolen code from others, including attack code – just two notable examples include the Zeus trojan and RIG exploit kit code leaks that provided the code base for much of the current generation of threats. What is new and unprecedented is that, whether through malice or incompetence, cyber-criminals are gaining access to intelligence and nation-state grade attack code. The Shadowbroker leaks that resulted in Wannacry is one recent example of this trend, and one we expect to accelerate – especially with intelligence services actively outing each other’s methods.

Custom and bespoke techniques

The practice of hackers creating their own tools and researching their own exploits has a long and hallowed tradition, with headline-grabbing zero-days becoming more and more common. Nation-state actors in particular often make a distinction between attack operators and attack code developers, with the ability to request tailored and bespoke code and tools – not unlike the model that has been replicated in the Cybercrime-as-a-Service market. Even when developing custom tools, threat-actors frequently integrate code and exploits from other parties.

Figure 2: The four main attack tool types

When determining who is actually behind these attacks, though, what is most important is the ability to combine all four types of attack tools – this provides a further layer of obfuscation against methods that rely on pattern matching for detection whilst causing additional confusion for would-be investigators. An attacker can use any combination and variation of these tool types to create a different “Chimera” attack stack – making it that much more difficult to identify who is really the operator. Telling apart the operator from the Cybercrime-as-a-Service vendor, for example, is difficult when most of the TTPs that are evaluated are technical and derive from the tooling.

Figure 3: The TTP and Attribution Confusion Chain

Conclusion

As the challenge of attribution intensifies, our focus must turn to defending against cyber-attacks themselves.

The combination of the four threat trends outlined above has lowered the barrier to entry for criminally motivated operators. Less technical adversaries are now able to launch attacks at a speed and scale previously confined to the most organized and well-financed cyber-criminal rings. This change in circumstances has made attribution of offensive cyber activity drastically more complex, and it may be some time before the prosecution rate for cyber-crime gets good enough that it can act as a greater disincentive.

As the challenge of attribution intensifies, our focus must turn to defending against cyber-attacks themselves. You may not ever know who is attacking you, but if you can successfully thwart the full range of threats, new and old, your organization can continue to operate as normal.

Fortunately, defenders’ abilities to detect and respond to cyber-threats have significantly advanced in recent years, thanks to the latest developments in AI and machine learning. Over 3,500 organizations now rely on Cyber AI to detect and contain cyber-threats – whether attackers use pre-existing OS tools to masquerade their attacks or use bespoke and entirely new techniques to bypass rules and signatures. When a threat is identified, AI can respond autonomously by enforcing a user or device’s ‘pattern of life’, allowing ‘business as usual’ whilst ensuring the organization is protected from harm.

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.
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Oliver Rochford
Technical Director
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January 29, 2025

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Inside the SOC

Bytesize Security: Insider Threats in Google Workspace

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What is an insider threat?

An insider threat is a cyber risk originating from within an organization. These threats can involve actions such as an employee inadvertently clicking on a malicious link (e.g., a phishing email) or an employee with malicious intent conducting data exfiltration for corporate sabotage.

Insiders often exploit their knowledge and access to legitimate corporate tools, presenting a continuous risk to organizations. Defenders must protect their digital estate against threats from both within and outside the organization.

For example, in the summer of 2024, Darktrace / IDENTITY successfully detected a user in a customer environment attempting to steal sensitive data from a trusted Google Workspace service. Despite the use of a legitimate and compliant corporate tool, Darktrace identified anomalies in the user’s behavior that indicated malicious intent.

Attack overview: Insider threat

In June 2024, Darktrace detected unusual activity involving the Software-as-a-Service (SaaS) account of a former employee from a customer organization. This individual, who had recently left the company, was observed downloading a significant amount of data in the form of a “.INDD” file (an Adobe InDesign document typically used to create page layouts [1]) from Google Drive.

While the use of Google Drive and other Google Workspace platforms was not unexpected for this employee, Darktrace identified that the user had logged in from an unfamiliar and suspicious IPv6 address before initiating the download. This anomaly triggered a model alert in Darktrace / IDENTITY, flagging the activity as potentially malicious.

A Model Alert in Darktrace / IDENTITY showing the unusual “.INDD” file being downloaded from Google Workspace.
Figure 1: A Model Alert in Darktrace / IDENTITY showing the unusual “.INDD” file being downloaded from Google Workspace.

Following this detection, the customer reached out to Darktrace’s Security Operations Center (SOC) team via the Security Operations Support service for assistance in triaging and investigating the incident further. Darktrace’s SOC team conducted an in-depth investigation, enabling the customer to identify the exact moment of the file download, as well as the contents of the stolen documents. The customer later confirmed that the downloaded files contained sensitive corporate data, including customer details and payment information, likely intended for reuse or sharing with a new employer.

In this particular instance, Darktrace’s Autonomous Response capability was not active, allowing the malicious insider to successfully exfiltrate the files. If Autonomous Response had been enabled, Darktrace would have immediately acted upon detecting the login from an unusual (in this case 100% rare) location by logging out and disabling the SaaS user. This would have provided the customer with the necessary time to review the activity and verify whether the user was authorized to access their SaaS environments.

Conclusion

Insider threats pose a significant challenge for traditional security tools as they involve internal users who are expected to access SaaS platforms. These insiders have preexisting knowledge of the environment, sensitive data, and how to make their activities appear normal, as seen in this case with the use of Google Workspace. This familiarity allows them to avoid having to use more easily detectable intrusion methods like phishing campaigns.

Darktrace’s anomaly detection capabilities, which focus on identifying unusual activity rather than relying on specific rules and signatures, enable it to effectively detect deviations from a user’s expected behavior. For instance, an unusual login from a new location, as in this example, can be flagged even if the subsequent malicious activity appears innocuous due to the use of a trusted application like Google Drive.

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

Appendices

Darktrace Model Detections

SaaS / Resource::Unusual Download Of Externally Shared Google Workspace File

References

[1]https://www.adobe.com/creativecloud/file-types/image/vector/indd-file.html

MITRE ATT&CK Mapping

Technqiue – Tactic – ID

Data from Cloud Storage Object – COLLECTION -T1530

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About the author
Vivek Rajan
Cyber Analyst

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January 28, 2025

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Ransomware

RansomHub Ransomware: Darktrace’s Investigation of the Newest Tool in ShadowSyndicate's Arsenal

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What is ShadowSyndicate?

ShadowSyndicate, also known as Infra Storm, is a threat actor reportedly active since July 2022, working with various ransomware groups and affiliates of ransomware programs, such as Quantum, Nokoyawa, and ALPHV. This threat actor employs tools like Cobalt Strike, Sliver, IcedID, and Matanbuchus malware in its attacks. ShadowSyndicate utilizes the same SSH fingerprint (1ca4cbac895fc3bd12417b77fc6ed31d) on many of their servers—85 as of September 2023. At least 52 of these servers have been linked to the Cobalt Strike command and control (C2) framework [1].

What is RansomHub?

First observed following the FBI's takedown of ALPHV/BlackCat in December 2023, RansomHub quickly gained notoriety as a Ransomware-as-a-Service (RaaS) operator. RansomHub capitalized on the law enforcement’s disruption of the LockBit group’s operations in February 2024 to market themselves to potential affiliates who had previously relied on LockBit’s encryptors. RansomHub's success can be largely attributed to their aggressive recruitment on underground forums, leading to the absorption of ex-ALPHV and ex-LockBit affiliates. They were one of the most active ransomware operators in 2024, with approximately 500 victims reported since February, according to their Dedicated Leak Site (DLS) [2].

ShadowSyndicate and RansomHub

External researchers have reported that ShadowSyndicate had as many as seven different ransomware families in their arsenal between July 2022, and September 2023. Now, ShadowSyndicate appears to have added RansomHub’s their formidable stockpile, becoming an affiliate of the RaaS provider [1].

Darktrace’s analysis of ShadowSyndicate across its customer base indicates that the group has been leveraging RansomHub ransomware in multiple attacks in September and October 2024. ShadowSyndicate likely shifted to using RansomHub due to the lucrative rates offered by this RaaS provider, with affiliates receiving up to 90% of the ransom—significantly higher than the general market rate of 70-80% [3].

In many instances where encryption was observed, ransom notes with the naming pattern “README_[a-zA-Z0-9]{6}.txt” were written to affected devices. The content of these ransom notes threatened to release stolen confidential data via RansomHub’s DLS unless a ransom was paid. During these attacks, data exfiltration activity to external endpoints using the SSH protocol was observed. The external endpoints to which the data was transferred were found to coincide with servers previously associated with ShadowSyndicate activity.

Darktrace’s coverage of ShadowSyndicate and RansomHub

Darktrace’s Threat Research team identified high-confidence indicators of compromise (IoCs) linked to the ShadowSyndicate group deploying RansomHub. The investigation revealed four separate incidents impacting Darktrace customers across various sectors, including education, manufacturing, and social services. In the investigated cases, multiple stages of the kill chain were observed, starting with initial internal reconnaissance and leading to eventual file encryption and data exfiltration.

Attack Overview

Timeline attack overview of ransomhub ransomware

Internal Reconnaissance

The first observed stage of ShadowSyndicate attacks involved devices making multiple internal connection attempts to other internal devices over key ports, suggesting network scanning and enumeration activity. In this initial phase of the attack, the threat actor gathers critical details and information by scanning the network for open ports that might be potentially exploitable. In cases observed by Darktrace affected devices were typically seen attempting to connect to other internal locations over TCP ports including 22, 445 and 3389.

C2 Communication and Data Exfiltration

In most of the RansomHub cases investigated by Darktrace, unusual connections to endpoints associated with Splashtop, a remote desktop access software, were observed briefly before outbound SSH connections were identified.

Following this, Darktrace detected outbound SSH connections to the external IP address 46.161.27[.]151 using WinSCP, an open-source SSH client for Windows used for secure file transfer. The Cybersecurity and Infrastructure Security Agency (CISA) identified this IP address as malicious and associated it with ShadowSyndicate’s C2 infrastructure [4]. During connections to this IP, multiple gigabytes of data were exfiltrated from customer networks via SSH.

Data exfiltration attempts were consistent across investigated cases; however, the method of egress varied from one attack to another, as one would expect with a RaaS strain being employed by different affiliates. In addition to transfers to ShadowSyndicate’s infrastructure, threat actors were also observed transferring data to the cloud storage and file transfer service, MEGA, via HTTP connections using the ‘rclone’ user agent – a command-line program used to manage files on cloud storage. In another case, data exfiltration activity occurred over port 443, utilizing SSL connections.

Lateral Movement

In investigated incidents, lateral movement activity began shortly after C2 communications were established. In one case, Darktrace identified the unusual use of a new administrative credential which was quickly followed up with multiple suspicious executable file writes to other internal devices on the network.

The filenames for this executable followed the regex naming convention “[a-zA-Z]{6}.exe”, with two observed examples being “bWqQUx.exe” and “sdtMfs.exe”.

Cyber AI Analyst Investigation Process for the SMB Writes of Suspicious Files to Multiple Devices' incident.
Figure 1: Cyber AI Analyst Investigation Process for the SMB Writes of Suspicious Files to Multiple Devices' incident.

Additionally, script files such as “Defeat-Defender2.bat”, “Share.bat”, and “def.bat” were also seen written over SMB, suggesting that threat actors were trying to evade network defenses and detection by antivirus software like Microsoft Defender.

File Encryption

Among the three cases where file encryption activity was observed, file names were changed by adding an extension following the regex format “.[a-zA-Z0-9]{6}”. Ransom notes with a similar naming convention, “README_[a-zA-Z0-9]{6}.txt”, were written to each share. While the content of the ransom notes differed slightly in each case, most contained similar text. Clear indicators in the body of the ransom notes pointed to the use of RansomHub ransomware in these attacks. As is increasingly the case, threat actors employed double extortion tactics, threatening to leak confidential data if the ransom was not paid. Like most ransomware, RansomHub included TOR site links for communication between its "customer service team" and the target.

Figure 2: The graph shows the behavior of a device with encryption activity, using the “SMB Sustained Mimetype Conversion” and “Unusual Activity Events” metrics over three weeks.

Since Darktrace’s Autonomous Response capability was not enabled during the compromise, the ransomware attack succeeded in its objective. However, Darktrace’s Cyber AI Analyst provided comprehensive coverage of the kill chain, enabling the customer to quickly identify affected devices and initiate remediation.

Figure 3: Cyber AI Analyst panel showing the critical incidents of the affected device from one of the cases investigated.

In lieu of Autonomous Response being active on the networks, Darktrace was able to suggest a variety of manual response actions intended to contain the compromise and prevent further malicious activity. Had Autonomous Response been enabled at the time of the attack, these actions would have been quickly applied without any human interaction, potentially halting the ransomware attack earlier in the kill chain.

Figure 4: A list of suggested Autonomous Response actions on the affected devices."

Conclusion

The Darktrace Threat Research team has noted a surge in attacks by the ShadowSyndicate group using RansomHub’s RaaS of late. RaaS has become increasingly popular across the threat landscape due to its ease of access to malware and script execution. As more individual threat actors adopt RaaS, security teams are struggling to defend against the increasing number of opportunistic attacks.

For customers subscribed to Darktrace’s Security Operations Center (SOC) services, the Analyst team promptly investigated detections of the aforementioned unusual and anomalous activities in the initial infection phases. Multiple alerts were raised via Darktrace’s Managed Threat Detection to warn customers of active ransomware incidents. By emphasizing anomaly-based detection and response, Darktrace can effectively identify devices affected by ransomware and take action against emerging activity, minimizing disruption and impact on customer networks.

Credit to Kwa Qing Hong (Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore) and Signe Zahark (Principal Cyber Analyst, Japan)

Appendices

Darktrace Model Detections

Antigena Models / Autonomous Response:

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Insider Threat / Antigena SMB Enumeration Block

Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity

Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach

Antigena / Network / Significant Anomaly / Antigena Significant Server Anomaly Block

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

Antigena / Network / External Threat / Antigena File then New Outbound Block


Network Reconnaissance:

Device / Network Scan

Device / ICMP Address Scan

Device / RDP Scan
Device / Anomalous LDAP Root Searches
Anomalous Connection / SMB Enumeration
Device / Spike in LDAP Activity

C2:

Enhanced Monitoring - Device / Lateral Movement and C2 Activity

Enhanced Monitoring - Device / Initial Breach Chain Compromise

Enhanced Monitoring - Compromise / Suspicious File and C2

Compliance / Remote Management Tool On Server

Anomalous Connection / Outbound SSH to Unusual Port


External Data Transfer:

Enhanced Monitoring - Unusual Activity / Enhanced Unusual External Data Transfer

Unusual Activity / Unusual External Data Transfer

Anomalous Connection / Data Sent to Rare Domain

Unusual Activity / Unusual External Data to New Endpoint

Compliance / SSH to Rare External Destination

Anomalous Connection / Application Protocol on Uncommon Port

Enhanced Monitoring - Anomalous File / Numeric File Download

Anomalous File / New User Agent Followed By Numeric File Download

Anomalous Server Activity / Outgoing from Server

Device / Large Number of Connections to New Endpoints

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

Anomalous Connection / Uncommon 1 GiB Outbound

Lateral Movement:

User / New Admin Credentials on Server

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / High Volume of New or Uncommon Service Control

Anomalous File / Internal / Executable Uploaded to DC

Anomalous Connection / Suspicious Activity On High Risk Device

File Encryption:

Compliance / SMB Drive Write

Anomalous File / Internal / Additional Extension Appended to SMB File

Compromise / Ransomware / Possible Ransom Note Write

Anomalous Connection / Suspicious Read Write Ratio

List of Indicators of Compromise (IoCs)

IoC - Type - Description + Confidence

83.97.73[.]198 - IP - Data exfiltration endpoint

108.181.182[.]143 - IP - Data exfiltration endpoint

46.161.27[.]151 - IP - Data exfiltration endpoint

185.65.212[.]164 - IP - Data exfiltration endpoint

66[.]203.125.21 - IP - MEGA endpoint used for data exfiltration

89[.]44.168.207 - IP - MEGA endpoint used for data exfiltration

185[.]206.24.31 - IP - MEGA endpoint used for data exfiltration

31[.]216.148.33 - IP - MEGA endpoint used for data exfiltration

104.226.39[.]18 - IP - C2 endpoint

103.253.40[.]87 - IP - C2 endpoint

*.relay.splashtop[.]com - Hostname - C2 & data exfiltration endpoint

gfs***n***.userstorage.mega[.]co.nz - Hostname - MEGA endpoint used for data exfiltration

w.api.mega[.]co.nz - Hostname - MEGA endpoint used for data exfiltration

ams-rb9a-ss.ams.efscloud[.]net - Hostname - Data exfiltration endpoint

MITRE ATT&CK Mapping

Tactic - Technqiue

RECONNAISSANCE – T1592.004 Client Configurations

RECONNAISSANCE – T1590.005 IP Addresses

RECONNAISSANCE – T1595.001 Scanning IP Blocks

RECONNAISSANCE – T1595.002 Vulnerability Scanning

DISCOVERY – T1046 Network Service Scanning

DISCOVERY – T1018 Remote System Discovery

DISCOVERY – T1083 File and Directory Discovery
INITIAL ACCESS - T1189 Drive-by Compromise

INITIAL ACCESS - T1190 Exploit Public-Facing Application

COMMAND AND CONTROL - T1001 Data Obfuscation

COMMAND AND CONTROL - T1071 Application Layer Protocol

COMMAND AND CONTROL - T1071.001 Web Protocols

COMMAND AND CONTROL - T1573.001 Symmetric Cryptography

COMMAND AND CONTROL - T1571 Non-Standard Port

DEFENSE EVASION – T1078 Valid Accounts

DEFENSE EVASION – T1550.002 Pass the Hash

LATERAL MOVEMENT - T1021.004 SSH

LATERAL MOVEMENT – T1080 Taint Shared Content

LATERAL MOVEMENT – T1570 Lateral Tool Transfer

LATERAL MOVEMENT – T1021.002 SMB/Windows Admin Shares

COLLECTION - T1185 Man in the Browser

EXFILTRATION - T1041 Exfiltration Over C2 Channel

EXFILTRATION - T1567.002 Exfiltration to Cloud Storage

EXFILTRATION - T1029 Scheduled Transfer

IMPACT – T1486 Data Encrypted for Impact

References

1.     https://www.group-ib.com/blog/shadowsyndicate-raas/

2.     https://www.techtarget.com/searchsecurity/news/366617096/ESET-RansomHub-most-active-ransomware-group-in-H2-2024

3.     https://cyberint.com/blog/research/ransomhub-the-new-kid-on-the-block-to-know/

4.     https://www.cisa.gov/sites/default/files/2024-05/AA24-131A.stix_.xml

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