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November 8, 2022

[Part 2] Typical Steps of a Raccoon Stealer v2 Infection

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08
Nov 2022
Since the release of version 2 of Raccoon Stealer, Darktrace’s SOC has observed a surge in activity. See the typical steps used by this new threat!

Raccoon Stealer Malware

Since the release of version 2 of Raccoon Stealer in May 2022, Darktrace has observed huge volumes of Raccoon Stealer v2 infections across its client base. The info-stealer, which seeks to obtain and then exfiltrate sensitive data saved on users’ devices, displays a predictable pattern of network activity once it is executed. In this blog post, we will provide details of this pattern of activity, with the goal of helping security teams to recognize network-based signs of Raccoon Stealer v2 infection within their own networks. 

What is Raccoon Stealer?

Raccoon Stealer is a classic example of information-stealing malware, which cybercriminals typically use to gain possession of sensitive data saved in users’ browsers and cryptocurrency wallets. In the case of browsers, targeted data typically includes cookies, saved login details, and saved credit card details. In the case of cryptocurrency wallets (henceforth, ‘crypto-wallets’), targeted data typically includes public keys, private keys, and seed phrases [1]. Once sensitive browser and crypto-wallet data is in the hands of cybercriminals, it will likely be used to conduct harmful activities, such as identity theft, cryptocurrency theft, and credit card fraud.

How do you obtain Raccoon Stealer?

Like most info-stealers, Raccoon Stealer is purchasable. The operators of Raccoon Stealer sell Raccoon Stealer samples to their customers (called ‘affiliates’), who then use the info-stealer to gain possession of sensitive data saved on users’ devices. Raccoon Stealer affiliates typically distribute their samples via SEO-promoted websites providing free or cracked software. 

Is Raccoon Stealer Still Active?

On the 25th of March 2022, the operators of Raccoon Stealer announced that they would be suspending their operations because one of their core developers had been killed during the Russia-Ukraine conflict [2]. The presence of the hardcoded RC4 key ‘edinayarossiya’ (Russian for ‘United Russia’) within observed Raccoon Stealer v2 samples [3] provides potential evidence of the Raccoon Stealer operators’ allegiances.

Recent details shared by the US Department of Justice [4]/[5] indicate that it was in fact the arrest, rather than the death, of an operator which led the Raccoon Stealer team to suspend their operations [6]. As a result of the FBI, along with law enforcement partners in Italy and the Netherlands, dismantling Raccoon Stealer infrastructure in March 2022 [4], the Raccoon Stealer team was forced to build a new version of the info-stealer.  

On the 17th May 2022, the completion of v2 of the info-stealer was announced on the Raccoon Stealer Telegram channel [7].  Since its release in May 2022, Raccoon Stealer v2 has become extremely popular amongst cybercriminals. The prevalence of Raccoon Stealer v2 in the wider landscape has been reflected in Darktrace’s client base, with hundreds of infections being observed within client networks on a monthly basis.   

Since Darktrace’s SOC first saw a Raccoon Stealer v2 infection on the 22nd May 2022, the info-stealer has undergone several subtle changes. However, the info-stealer’s general pattern of network activity has remained essentially unchanged.  

How Does Raccoon Stealer v2 Infection Work?

A Raccoon Stealer v2 infection typically starts with a user attempting to download cracked or free software from an SEO-promoted website. Attempting to download software from one of these cracked/free software websites redirects the user’s browser (typically via several .xyz or .cfd endpoints) to a page providing download instructions. In May, June, and July, many of the patterns of download behavior observed by Darktrace’s SOC matched the pattern of behavior observed in a cracked software campaign reported by Avast in June [8].   

webpage whose download instructions led to a Raccoon Stealer v2
Figure 1: Above is a webpage whose download instructions led to a Raccoon Stealer v2 sample hosted on Discord CDN
example of a webpage whose download instructions led to a Raccoon Stealer v2
Figure 2: Above is an example of a webpage whose download instructions led to a Raccoon Stealer v2 sample hosted on Bitbucket
example of a webpage whose download instructions led to a Raccoon Stealer v2
Figure 3: Above is an example of a webpage whose download instructions led to a Raccoon Stealer v2 sample hosted on MediaFire

Following the instructions on the download instruction page causes the user’s device to download a password-protected RAR file from a file storage service such as ‘cdn.discordapp[.]com’, ‘mediafire[.]com’, ‘mega[.]nz’, or ‘bitbucket[.]org’. Opening the downloaded file causes the user’s device to execute Raccoon Stealer v2. 

The Event Log for an infected device,
Figure 4: The Event Log for an infected device, taken from Darktrace’s Threat Visualiser interface, shows a device contacting two cracked software websites (‘crackedkey[.]org’ and ‘crackedpc[.]co’) before contacting a webpage (‘premiumdownload[.]org) providing instructions to download Raccoon Stealer v2 from Bitbucket

Once Raccoon Stealer v2 is running on a device, it will make an HTTP POST request with the target URI ‘/’ and an unusual user-agent string (such as ‘record’, ‘mozzzzzzzzzzz’, or ‘TakeMyPainBack’) to a C2 server. This POST request consists of three strings: a machine GUID, a username, and a 128-bit RC4 key [9]. The posted data has the following form:

machineId=X | Y & configId=Z (where X is a machine GUID, Y is a username and Z is a 128-bit RC4 key) 

PCAP showing a device making an HTTP POST request with the User Agent header ‘record’ 
Figure 5:PCAP showing a device making an HTTP POST request with the User Agent header ‘record’ 
PCAP showing a device making an HTTP POST request with the User Agent header ‘mozzzzzzzzzzz’
Figure 6: PCAP showing a device making an HTTP POST request with the User Agent header ‘mozzzzzzzzzzz’
PCAP showing a device making an HTTP POST request with the User Agent header ‘TakeMyPainBack’
Figure 7: PCAP showing a device making an HTTP POST request with the User Agent header ‘TakeMyPainBack’

The C2 server responds to the info-stealer’s HTTP POST request with custom-formatted configuration details. These configuration details consist of fields which tell the info-stealer what files to download, what data to steal, and what target URI to use in its subsequent exfiltration POST requests. Below is a list of the fields Darktrace has observed in the configuration details retrieved by Raccoon Stealer v2 samples:

  • a ‘libs_mozglue’ field, which specifies a download address for a Firefox library named ‘mozglue.dll’
  • a ‘libs_nss3’ field, which specifies a download address for a Network System Services (NSS) library named ‘nss3.dll’ 
  • a ‘libs_freebl3’ field, which specifies a download address for a Network System Services (NSS) library named ‘freebl3.dll’
  • a ‘libs_softokn3’ field, which specifies a download address for a Network System Services (NSS) library named ‘softokn3.dll’
  • a ‘libs_nssdbm3’ field, which specifies a download address for a Network System Services (NSS) library named ‘nssdbm3.dll’
  • a ‘libs_sqlite3’ field, which specifies a download address for a SQLite command-line program named ‘sqlite3.dll’
  • a ‘libs_ msvcp140’ field, which specifies a download address for a Visual C++ runtime library named ‘msvcp140.dll’
  • a ‘libs_vcruntime140’ field, which specifies a download address for a Visual C++ runtime library named ‘vcruntime140.dll’
  • a ‘ldr_1’ field, which specifies the download address for a follow-up payload for the sample to download 
  • ‘wlts_X’ fields (where X is the name of a crypto-wallet application), which specify data for the sample to obtain from the specified crypto-wallet application
  • ‘ews_X’ fields (where X is the name of a crypto-wallet browser extension), which specify data for the sample to obtain from the specified browser extension
  • ‘xtntns_X’ fields (where X is the name of a password manager browser extension), which specify data for the sample to obtain from the specified browser extension
  • a ‘tlgrm_Telegram’ field, which specifies data for the sample to obtain from the Telegram Desktop application 
  • a ‘grbr_Desktop’ field, which specifies data within a local ‘Desktop’ folder for the sample to obtain 
  • a ‘grbr_Documents’ field, which specifies data within a local ‘Documents’ folder for the sample to obtain
  • a ‘grbr_Recent’ field, which specifies data within a local ‘Recent’ folder for the sample to obtain
  • a ‘grbr_Downloads’ field, which specifies data within a local ‘Downloads’ folder for the sample to obtain
  • a ‘sstmnfo_System Info.txt’ field, which specifies whether the sample should gather and exfiltrate a profile of the infected host 
  • a ‘scrnsht_Screenshot.jpeg’ field, which specifies whether the sample should take and exfiltrate screenshots of the infected host
  • a ‘token’ field, which specifies a 32-length string of hexadecimal digits for the sample to use as the target URI of its HTTP POST requests containing stolen data 

After retrieving its configuration data, Raccoon Stealer v2 downloads the library files specified in the ‘libs_’ fields. Unusual user-agent strings (such as ‘record’, ‘qwrqrwrqwrqwr’, and ‘TakeMyPainBack’) are used in the HTTP GET requests for these library files. In all Raccoon Stealer v2 infections seen by Darktrace, the paths of the URLs specified in the ‘libs_’ fields have the following form:

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/X (where X is the name of the targeted DLL file) 

Advanced Search logs for an infected host
Figure 8: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device making an HTTP POST request to retrieve configuration details, and then making HTTP GET requests with the User Agent header ‘record’ for DLL files
Advanced Search logs for an infected host
Figure 9: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device making an HTTP POST request to retrieve configuration details, and then making HTTP GET requests with the User Agent header ‘qwrqrwrqwrqwr’ for DLL files
Advanced Search logs for an infected host
Figure 10: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device making an HTTP POST request to retrieve configuration details, and then making HTTP GET requests with the User Agent header ‘TakeMyPainBack’ for DLL files

Raccoon Stealer v2 uses the DLLs which it downloads to gain access to sensitive data (such as cookies, credit card details, and login details) saved in browsers running on the infected host.  

Depending on the data provided in the configuration details, Raccoon Stealer v2 will typically seek to obtain, in addition to sensitive data saved in browsers, the following information:

  • Information about the Operating System and applications installed on the infected host
  • Data from specified crypto-wallet software
  • Data from specified crypto-wallet browser extensions
  • Data from specified local folders
  • Data from Telegram Desktop
  • Data from specified password manager browser extensions
  • Screenshots of the infected host 

Raccoon Stealer v2 exfiltrates the data which it obtains to its C2 server by making HTTP POST requests with unusual user-agent strings (such as ‘record’, ‘rc2.0/client’, ‘rqwrwqrqwrqw’, and ‘TakeMyPainBack’) and target URIs matching the 32-length string of hexadecimal digits specified in the ‘token’ field of the configuration details. The stolen data exfiltrated by Raccoon Stealer typically includes files named ‘System Info.txt’, ‘---Screenshot.jpeg’, ‘\cookies.txt’, and ‘\passwords.txt’. 

Advanced Search logs for an infected host
Figure 11: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating files named ‘System Info.txt’ and ‘---Screenshot.jpeg’
Advanced Search logs for an infected host
Figure 12: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating a file named ‘System Info.txt’ 
Advanced Search logs for an infected host
Figure 13: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating files named ‘System Info.txt’, ‘\cookies.txt’ and ‘\passwords.txt’
Advanced Search logs for an infected host
Figure 14: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating a file named ‘System Info.txt’

If a ‘ldr_1’ field is present in the retrieved configuration details, then Raccoon Stealer will complete its operation by downloading the binary file specified in the ‘ldr_1’ field. In all observed cases, the paths of the URLs specified in the ‘ldr_1’ field end in a sequence of digits, followed by ‘.bin’. The follow-up payload seems to vary between infections, likely due to this additional-payload feature being customizable by Raccoon Stealer affiliates. In many cases, the info-stealer, CryptBot, was delivered as the follow-up payload. 

Darktrace Coverage of Raccoon Stealer

Once a user’s device becomes infected with Raccoon Stealer v2, it will immediately start to communicate over HTTP with a C2 server. The HTTP requests made by the info-stealer have an empty Host header (although Host headers were used by early v2 samples) and highly unusual User Agent headers. When Raccoon Stealer v2 was first observed in May 2022, the user-agent string ‘record’ was used in its HTTP requests. Since then, it appears that the operators of Raccoon Stealer have made several changes to the user-agent strings used by the info-stealer,  likely in an attempt to evade signature-based detections. Below is a timeline of the changes to the info-stealer’s user-agent strings, as observed by Darktrace’s SOC:

  • 22nd May 2022: Samples seen using the user-agent string ‘record’
  • 2nd July 2022: Samples seen using the user-agent string ‘mozzzzzzzzzzz’
  • 29th July 2022: Samples seen using the user-agent string ‘rc2.0/client’
  • 10th August 2022: Samples seen using the user-agent strings ‘qwrqrwrqwrqwr’ and ‘rqwrwqrqwrqw’
  • 16th Sep 2022: Samples seen using the user-agent string ‘TakeMyPainBack’

The presence of these highly unusual user-agent strings within infected devices’ HTTP requests causes the following Darktrace DETECT/Network models to breach:

  • Device / New User Agent
  • Device / New User Agent and New IP
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Device / Three or More New User Agents

These DETECT models look for devices making HTTP requests with unusual user-agent strings, rather than specific user-agent strings which are known to be malicious. This method of detection enables the models to continually identify Raccoon Stealer v2 HTTP traffic, despite the changes made to the info-stealer’s user-agent strings.   

After retrieving configuration details from a C2 server, Raccoon Stealer v2 samples make HTTP GET requests for several DLL libraries. Since these GET requests are directed towards highly unusual IP addresses, the downloads of the DLLs cause the following DETECT models to breach:

  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Script from Rare External Location
  • Anomalous File / Multiple EXE from Rare External Locations

Raccoon Stealer v2 samples send data to their C2 server via HTTP POST requests with an absent Host header. Since these POST requests lack a Host header and have a highly unusual destination IP, their occurrence causes the following DETECT model to breach:

  • Anomalous Connection / Posting HTTP to IP Without Hostname

Certain Raccoon Stealer v2 samples download (over HTTP) a follow-up payload once they have exfiltrated data. Since the target URIs of the HTTP GET requests made by v2 samples end in a sequence of digits followed by ‘.bin’, the samples’ downloads of follow-up payloads cause the following DETECT model to breach:

  • Anomalous File / Numeric File Download

If Darktrace RESPOND/Network is configured within a customer’s environment, then Raccoon Stealer v2 activity should cause the following inhibitive actions to be autonomously taken on infected systems: 

  • Enforce pattern of life — This action results in a device only being able to make connections which are normal for it to make
  • Enforce group pattern of life — This action results in a device only being able to make connections which are normal for it or any of its peers to make
  • Block matching connections — This action results in a device being unable to make connections to particular IP/Port pairs
  • Block all outgoing traffic — This action results in a device being unable to make any connections 
The Event Log for an infected device
Figure 15: The Event Log for an infected device, taken from Darktrace’s Threat Visualiser interface, shows Darktrace RESPOND taking inhibitive actions in response to the HTTP activities of a Raccoon Stealer v2 sample downloaded from MediaFire

Given that Raccoon Stealer v2 infections move extremely fast, with the time between initial infection and data exfiltration sometimes less than a minute, the availability of Autonomous Response technology such as Darktrace RESPOND is vital for the containment of Raccoon Stealer v2 infections.  

Timeline of Darktrace stopping raccoon stealer.
Figure 16: Figure displaying the steps of a Raccoon Stealer v2 infection, along with the corresponding Darktrace detections

Conclusion

Since the release of Raccoon Stealer v2 back in 2022, the info-stealer has relentlessly infected the devices of unsuspecting users. Once the info-stealer infects a user’s device, it retrieves and then exfiltrates sensitive information within a matter of minutes. The distinctive pattern of network behavior displayed by Raccoon Stealer v2 makes the info-stealer easy to spot. However, the changes which the Raccoon Stealer operators make to the User Agent headers of the info-stealer’s HTTP requests make anomaly-based methods key for the detection of the info-stealer’s HTTP traffic. The operators of Raccoon Stealer can easily change the superficial features of their malware’s C2 traffic, however, they cannot easily change the fact that their malware causes highly unusual network behavior. Spotting this behavior, and then autonomously responding to it, is likely the best bet which organizations have at stopping a Raccoon once it gets inside their networks.  

Thanks to the Threat Research Team for its contributions to this blog.

References

[1] https://www.microsoft.com/security/blog/2022/05/17/in-hot-pursuit-of-cryware-defending-hot-wallets-from-attacks/

[2] https://twitter.com/3xp0rtblog/status/1507312171914461188

[3] https://www.esentire.com/blog/esentire-threat-intelligence-malware-analysis-raccoon-stealer-v2-0

[4] https://www.justice.gov/usao-wdtx/pr/newly-unsealed-indictment-charges-ukrainian-national-international-cybercrime-operation

[5] https://www.youtube.com/watch?v=Fsz6acw-ZJ

[6] https://riskybiznews.substack.com/p/raccoon-stealer-dev-didnt-die-in

[7] https://medium.com/s2wblog/raccoon-stealer-is-back-with-a-new-version-5f436e04b20d

[8] https://blog.avast.com/fakecrack-campaign

[9] https://blog.sekoia.io/raccoon-stealer-v2-part-2-in-depth-analysis/

Appendices

MITRE ATT&CK Mapping

Resource Development

• T1588.001 — Obtain Capabilities: Malware

• T1608.001 — Stage Capabilities: Upload Malware

• T1608.005 — Stage Capabilities: Link Target

• T1608.006 — Stage Capabilities: SEO Poisoning

Execution

•  T1204.002 — User Execution: Malicious File

Credential Access

• T1555.003 — Credentials from Password Stores:  Credentials from Web Browsers

• T1555.005 — Credentials from Password Stores:  Password Managers

• T1552.001 — Unsecured Credentials: Credentials  In Files

Command and Control

•  T1071.001 — Application Layer Protocol: Web Protocols

•  T1105 — Ingress Tool Transfer

IOCS

Type

IOC

Description

User-Agent String

record

String used in User Agent header of  Raccoon Stealer v2’s HTTP requests

User-Agent  String

mozzzzzzzzzzz

String used inUser Agent header of Raccoon Stealer v2’s HTTP requests

User-Agent String

rc2.0/client

String used in User Agent header of  Raccoon Stealer v2’s HTTP requests

User-Agent  String

qwrqrwrqwrqwr

String used in  User Agent header of Raccoon Stealer v2’s HTTP requests

User-Agent String

rqwrwqrqwrqw

String used in User Agent header of  Raccoon Stealer v2’s HTTP requests

User-Agent  String

TakeMyPainBack

String used in  User Agent header of Raccoon Stealer v2’s HTTP requests

Domain Name

brain-lover[.]xyz  

Raccoon Stealer v2 C2 infrastructure

Domain  Name

polar-gift[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

cool-story[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

fall2sleep[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

broke-bridge[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

use-freedom[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

just-trust[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

soft-viper[.]site

Raccoon Stealer  v2 C2 infrastructure

Domain Name

tech-lover[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

heal-brain[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

love-light[.]xyz

Raccoon Stealer v2 C2 infrastructure

IP  Address

104.21.80[.]14

Raccoon Stealer  v2 C2 infrastructure

IP Address

107.152.46[.]84

Raccoon Stealer v2 C2 infrastructure

IP  Address

135.181.147[.]255

Raccoon Stealer  v2 C2 infrastructure

IP Address

135.181.168[.]157

Raccoon Stealer v2 C2 infrastructure

IP  Address

138.197.179[.]146

Raccoon Stealer  v2 C2 infrastructure

IP Address

141.98.169[.]33

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.170[.]100

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.19.170[.]175

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.170[.]98

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.19.173[.]33

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.173[.]72

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.19.247[.]175

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.247[.]177

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.70.125[.]95

Raccoon Stealer v2 C2 infrastructure

IP  Address

152.89.196[.]234

Raccoon Stealer  v2 C2 infrastructure

IP Address

165.225.120[.]25

Raccoon Stealer v2 C2 infrastructure

IP  Address

168.100.10[.]238

Raccoon Stealer  v2 C2 infrastructure

IP Address

168.100.11[.]23

Raccoon Stealer v2 C2 infrastructure

IP  Address

168.100.9[.]234

Raccoon Stealer  v2 C2 infrastructure

IP Address

170.75.168[.]118

Raccoon Stealer v2 C2 infrastructure

IP  Address

172.67.173[.]14

Raccoon Stealer  v2 C2 infrastructure

IP Address

172.86.75[.]189

Raccoon Stealer v2 C2 infrastructure

IP  Address

172.86.75[.]33

Raccoon Stealer  v2 C2 infrastructure

IP Address

174.138.15[.]216

Raccoon Stealer v2 C2 infrastructure

IP  Address

176.124.216[.]15

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.106.92[.]14

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.173.34[.]161

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.173.34[.]161  

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.225.17[.]198

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.225.19[.]190

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.225.19[.]229

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.53.46[.]103

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.53.46[.]76

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.53.46[.]77

Raccoon Stealer v2 C2 infrastructure

IP  Address

188.119.112[.]230

Raccoon Stealer  v2 C2 infrastructure

IP Address

190.117.75[.]91

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.106.191[.]182

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.149.129[.]135

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.149.129[.]144

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.149.180[.]210

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.149.185[.]192

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.233.193[.]50

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]138

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]17

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]192

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]213

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]214

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]215

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]26

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]45

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.56.146[.]177

Raccoon Stealer  v2 C2 infrastructure

IP Address

194.180.174[.]180

Raccoon Stealer v2 C2 infrastructure

IP  Address

195.201.148[.]250

Raccoon Stealer  v2 C2 infrastructure

IP Address

206.166.251[.]156

Raccoon Stealer v2 C2 infrastructure

IP  Address

206.188.196[.]200

Raccoon Stealer  v2 C2 infrastructure

IP Address

206.53.53[.]18

Raccoon Stealer v2 C2 infrastructure

IP  Address

207.154.195[.]173

Raccoon Stealer  v2 C2 infrastructure

IP Address

213.252.244[.]2

Raccoon Stealer v2 C2 infrastructure

IP  Address

38.135.122[.]210

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.10.20[.]248

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.11.19[.]99

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.133.216[.]110

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.133.216[.]145

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.133.216[.]148

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.133.216[.]249

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.133.216[.]71

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.140.146[.]169

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.140.147[.]245

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.142.212[.]100

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.142.213[.]24

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.142.215[.]91

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.142.215[.]91  

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.142.215[.]92

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.144.29[.]18

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.144.29[.]243

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.15.156[.]11

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.15.156[.]2

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.15.156[.]31

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.15.156[.]31

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.150.67[.]156

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.153.230[.]183

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.153.230[.]228

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.159.251[.]163

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.159.251[.]164

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.61.136[.]67

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.61.138[.]162

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.67.228[.]8

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.67.231[.]202

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.67.34[.]152

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.67.34[.]234

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.144[.]187

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.8.144[.]54

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.144[.]55

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.8.145[.]174

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.145[.]83

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.8.147[.]39

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.147[.]79

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.84.0.152

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.86.86[.]78

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.54[.]110

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.54[.]110

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.54[.]95

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.55[.]115

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.55[.]117

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.55[.]193

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.55[.]198

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.55[.]20

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.55[.]84

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.92.156[.]150

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.36[.]154

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.36[.]230

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.36[.]231

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.36[.]232

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.36[.]233

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.39[.]34

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.39[.]74

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.39[.]75

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.39[.]77

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.118[.]33

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.176[.]62

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.177[.]217

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.177[.]234

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.177[.]43

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.177[.]47

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.177[.]92

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.177[.]98

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.22[.]142

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.23[.]100

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.23[.]25

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.23[.]76

Raccoon Stealer v2 C2 infrastructure

IP  Address

51.195.166[.]175

Raccoon Stealer  v2 C2 infrastructure

IP Address

51.195.166[.]176

Raccoon Stealer v2 C2 infrastructure

IP  Address

51.195.166[.]194

Raccoon Stealer  v2 C2 infrastructure

IP Address

51.81.143[.]169

Raccoon Stealer v2 C2 infrastructure

IP  Address

62.113.255[.]110

Raccoon Stealer  v2 C2 infrastructure

IP Address

65.109.3[.]107

Raccoon Stealer v2 C2 infrastructure

IP  Address

74.119.192[.]56

Raccoon Stealer  v2 C2 infrastructure

IP Address

74.119.192[.]73

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.232.39[.]101

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.73.133[.]0

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.73.133[.]4

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.73.134[.]45

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.75.230[.]25

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.75.230[.]39

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.75.230[.]70

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.75.230[.]93

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.100[.]101

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.102[.]12

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.102[.]230

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.102[.]44

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.102[.]57

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.102[.]84

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.103[.]31

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.73[.]154

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.73[.]213

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.73[.]32

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.74[.]67

Raccoon Stealer  v2 C2 infrastructure

IP Address

78.159.103[.]195

Raccoon Stealer v2 C2 infrastructure

IP  Address

78.159.103[.]196

Raccoon Stealer  v2 C2 infrastructure

IP Address

80.66.87[.]23

Raccoon Stealer v2 C2 infrastructure

IP  Address

80.66.87[.]28

Raccoon Stealer  v2 C2 infrastructure

IP Address

80.71.157[.]112

Raccoon Stealer v2 C2 infrastructure

IP  Address

80.71.157[.]138

Raccoon Stealer  v2 C2 infrastructure

IP Address

80.92.204[.]202

Raccoon Stealer v2 C2 infrastructure

IP  Address

87.121.52[.]10

Raccoon Stealer  v2 C2 infrastructure

IP Address

88.119.175[.]187

Raccoon Stealer v2 C2 infrastructure

IP  Address

89.185.85[.]53

Raccoon Stealer  v2 C2 infrastructure

IP Address

89.208.107[.]42

Raccoon Stealer v2 C2 infrastructure

IP  Address

89.39.106[.]78

Raccoon Stealer  v2 C2 infrastructure

IP Address

91.234.254[.]126

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.104[.]16

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.104[.]17

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.104[.]18

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.106[.]116

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.106[.]224

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.107[.]132

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.107[.]138

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.96[.]109

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.97[.]129

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.97[.]53

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.97[.]56

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.97[.]57

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.98[.]5

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.244[.]114

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.158.244[.]119

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.244[.]21

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.158.247[.]24

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.247[.]26

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.158.247[.]30

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.247[.]44

Raccoon Stealer v2 C2 infrastructure

IP  Address

95.216.109[.]16

Raccoon Stealer  v2 C2 infrastructure

IP Address

95.217.124[.]179

Raccoon Stealer v2 C2 infrastructure

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/mozglue.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/nss3.dll

URI used in download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/freebl3.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/softokn3.dll

URI used in download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/nssdbm3.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/sqlite3.dll

URI used in download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/msvcp140.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/vcruntime140.dll

URI used in download of library file

URI

/C9S2G1K6I3G8T3X7/56296373798691245143.bin

URI used in  download of follow-up payload

URI

/O6K3E4G6N9S8S1/91787438215733789009.bin

URI used in download of follow-up  payload

URI

/Z2J8J3N2S2Z6X2V3S0B5/45637662345462341.bin

URI used in  download of follow-up payload

URI

/rgd4rgrtrje62iuty/19658963328526236.bin

URI used in download of follow-up  payload

URI

/sd325dt25ddgd523/81852849956384.bin

URI used in  download of follow-up payload

URI

/B0L1N2H4R1N5I5S6/40055385413647326168.bin

URI used in download of follow-up  payload

URI

/F5Q8W3O3O8I2A4A4B8S8/31427748106757922101.bin

URI used in  download of follow-up payload

URI

/36141266339446703039.bin

URI used in download of follow-up  payload

URI

/wH0nP0qH9eJ6aA9zH1mN/1.bin

URI used in  download of follow-up payload

URI

/K2X2R1K4C6Z3G8L0R1H0/68515718711529966786.bin

URI used in download of follow-up  payload

URI

/C3J7N6F6X3P8I0I0M/17819203282122080878.bin

URI used in  download of follow-up payload

URI

/W9H1B8P3F2J2H2K7U1Y7G5N4C0Z4B/18027641.bin

URI used in download of follow-up  payload

URI

/P2T9T1Q6P7Y5J3D2T0N0O8V/73239348388512240560937.bin

URI used in  download of follow-up payload

URI

/W5H6O5P0E4Y6P8O1B9D9G0P9Y9G4/671837571800893555497.bin

URI used in download of follow-up  payload

URI

/U8P2N0T5R0F7G2J0/898040207002934180145349.bin

URI used in  download of follow-up payload

URI

/AXEXNKPSBCKSLMPNOMNRLUEPR/3145102300913020.bin

URI used in download of follow-up  payload

URI

/wK6nO2iM9lE7pN7e/7788926473349244.bin

URI used in  download of follow-up payload

URI

/U4N9B5X5F5K2A0L4L4T5/84897964387342609301.bin

URI used in download of follow-up  payload

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
Sam Lister
SOC Analyst
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February 13, 2025

Why Darktrace / EMAIL excels against APTs

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What are APTs?

An Advanced Persistent Threat (APT) describes an adversary with sophisticated levels of expertise and significant resources, with the ability to carry out targeted cyber campaigns. These campaigns may penetrate an organization and remain undetected for long periods, allowing attackers to gather intelligence or cause damage over time.

Over the last few decades, the term APT has evolved from being almost exclusively associated with nation-state actors to a broader definition that includes highly skilled, well-resourced threat groups. While still distinct from mass, opportunistic cybercrime or "spray and pray" attacks, APT now refers to the elite tier of adversaries, whether state-sponsored or not, who demonstrate advanced capabilities, persistence, and a clear strategic focus. This shift reflects the growing sophistication of cyber threats, where non-state actors can now rival nation-states in executing covert, methodical intrusions to achieve long-term objectives.

These attacks are resource-intensive for threat actors to execute, but the potential rewards—ranging from financial gain to sensitive data theft—can be significant. In 2020, Business Email Compromise (BEC) attacks netted cybercriminals over $1.8 billion.1

And recently, the advent of AI has helped to automate launching these attacks, lowering the barriers to entry and making it more efficient to orchestrate the kind of attack that might previously have taken weeks to create. Research shows that AI can do 90% of a threat actor’s work2 – reducing time-to-target by automating tasks rapidly and avoiding errors in phishing communications. Email remains the most popular vector for initiating these sophisticated attacks, making it a critical battleground for cyber defense.

What makes APTs so successful?

The success of Advanced Persistent Threats (APTs) lies in their precision, persistence, and ability to exploit human and technical vulnerabilities. These attacks are carefully tailored to specific targets, using techniques like social engineering and spear phishing to gain initial access.

Once inside, attackers move laterally through networks, often remaining undetected for months or even years, silently gathering intelligence or preparing for a decisive strike. Alternatively, they might linger inside an account within the M365 environment, which could be even more valuable in terms of gathering information – in 2023 the average time to identify a breach in 2023 was 204 days.3

The subtle and long-term outlook nature of APTs makes them highly effective, as traditional security measures often fail to identify the subtle signs of compromise.

How Darktrace’s approach is designed to catch the most advanced threats

Luckily for our customers, Darktrace’s AI approach is uniquely equipped to detect and neutralize APTs. Unlike the majority of email security solutions that rely on static rules and signatures, or that train their AI on previous known-bad attack patterns, Darktrace leverages Self-Learning AI that baselines normal patterns of behavior within an organization, to immediately detect unusual activity that may signal an APT in progress.  

But in the modern era of email threats, no email security solution can guarantee 100% effectiveness. Because attackers operate with great sophistication, carefully adapting their tactics to evade detection – whether by altering attachments, leveraging compromised accounts, or moving laterally across an organization – a siloed security approach risks missing these subtle, multi-domain threats. That’s why a robust defense-in-depth strategy is essential to mitigate APTs.

Real-world threat finds: Darktrace / EMAIL in action

Let’s take a look at some real-world scenarios where Darktrace / EMAIL stopped tactics associated with APT campaigns in their tracks – from adversary-in-the-middle attacks to suspicious lateral movement.

1: How Darktrace disrupted an adversary-in-the-middle attack by identifying abnormal login redirects and blocking credential exfiltration

In October 2024, Darktrace detected an adversary-in-the-middle (AiTM) attack targeting a Darktrace customer. The attack began with a phishing email from a seemingly legitimate Dropbox address, which contained multiple link payloads inviting the recipient to access a file. Other solutions would have struggled to catch this attack, as the initial AitM attack was launched through delivering a malicious URL through a trusted vendor or service. Once compromised, the threat actor could have laid low on the target account, gathering reconnaissance, without detection from the email security solution.  

Darktrace / EMAIL identified the abnormal login redirects and flagged the suspicious activity. Darktrace / IDENTITY then detected unusual login patterns and blocked credential exfiltration attempts, effectively disrupting the attack and preventing the adversary from gaining unauthorized access. Read more.

Figure 1: Overview of the malicious email in the Darktrace / EMAIL console, highlighting Dropbox associated content/link payloads

2: How Darktrace stopped lateral movement to block NTLM hash theft

In early 2024, Darktrace detected an attack by the TA577 threat group, which aimed to steal NTLM hashes to gain unauthorized access to systems. The attack began with phishing emails containing ZIP files that connected to malicious infrastructure.  

A traditional email security solution would have likely missed this attack by focusing too heavily on analyzing the zip file payloads or relying on reputation analysis to understand whether the infrastructure was registered as bad before this activity was a recognized IoC.

Because it correlates activity across domains, Darktrace identified unusual lateral movement within the network and promptly blocked the attempts to steal NTLM hashes, effectively preventing the attackers from accessing sensitive credentials and securing the network. Read more.

Figure 2: A summary of anomaly indicators seen for a campaign email sent by TA577, as detected by Darktrace / EMAIL

3: How Darktrace prevented the WarmCookie backdoor deployment embedded in phishing emails

In mid-2024, Darktrace identified a phishing campaign targeting organizations with emails impersonating recruitment firms. These emails contained malicious links that, when clicked, deployed the WarmCookie backdoor.  

These emails are difficult to detect, as they use social engineering tactics to manipulate users into engaging with emails and following the embedded malicious links – but if a security solution is not analysing content and context, these could be allowed through.

In several observed cases across customer environments, Darktrace detected and blocked the suspicious behavior associated with WarmCookie that had already managed to evade customers’ native email security. By using behavioral analysis to correlate anomalous activity across the digital estate, Darktrace was able to identify the backdoor malware strain and notify customers. Read more.

Conclusion

These threat examples highlight a key principle of the Darktrace approach – that a backwards-facing approach grounded in threat intelligence will always be one step behind.

Most threat actors operate in campaigns, carefully crafting attacks and testing them across multiple targets. Once a campaign is identified, good defenders and traditional security solutions quickly update their defenses with new threat intelligence, rules, and signatures. However, APTs have the resources to rapidly adapt – spinning up new infrastructure, modifying payloads and altering their attack footprint to evade detection.

This is where Darktrace / EMAIL excels. Only by analyzing each user, message and interaction can an email security solution hope to catch the types of highly-sophisticated attacks that have the potential to cause major reputational and financial damage. Darktrace / EMAIL ensures that even the most subtle threats are detected and blocked with autonomous response, before causing impact – helping organizations remain one step ahead of increasingly adaptive threat actors.

Download the Darktrace / EMAIL Solution Brief

Discover the most advanced cloud-native AI email security solution to protect your domain and brand while preventing phishing, novel social engineering, business email compromise, account takeover, and data loss.

  • Gain up to 13 days of earlier threat detection and maximize ROI on your current email security
  • Experience 20-25% more threat blocking power with Darktrace / EMAIL
  • Stop the 58% of threats bypassing traditional email security

References

[1] FBI Internet Crime Report 2020

[2] https://www.optiv.com/insights/discover/blog/future-security-automation-how-ai-machine-learning-and-automation-are

[3] IBM Cost of a Data Breach Report 2023

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About the author
Carlos Gray
Product Manager

Blog

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Compliance

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February 11, 2025

NIS2 Compliance: Interpreting 'State-of-the-Art' for Organisations

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NIS2 Background

17 October 2024 marked the deadline for European Union (EU) Member States to implement the NIS2 Directive into national law. The Directive aims to enhance the EU’s cybersecurity posture by establishing a high common level of cybersecurity for critical infrastructure and services. It builds on its predecessor, the 2018 NIS Directive, by expanding the number of sectors in scope, enforcing greater reporting requirements and encouraging Member States to ensure regulated organisations adopt ‘state-of-the-art' security measures to protect their networks, OT and IT systems.  

Timeline of NIS2
Figure 1: Timeline of NIS2

The challenge of NIS2 & 'state-of-the-art'

Preamble (51) - "Member States should encourage the use of any innovative technology, including artificial intelligence, the use of which could improve the detection and prevention of cyberattacks, enabling resources to be diverted towards cyberattacks more effectively."
Article 21 - calls on Member States to ensure that essential and important entities “take appropriate and proportionate” cyber security measures, and that they do so by “taking into account the state-of-the-art and, where applicable, relevant European and international standards, as well as the cost of implementation.”

Regulartory expectations and ambiguity of NIS2

While organisations in scope can rely on technical guidance provided by ENISA1 , the EU’s agency for cybersecurity, or individual guidelines provided by Member States or Public-Private Partnerships where they have been published,2 the mention of ‘state-of-the-art' remains up to interpretation in most Member States. The use of the phrase implies that cybersecurity measures must evolve continuously to keep pace with emerging threats and technological advancements without specifying what ‘state-of-the-art’ actually means for a given context and risk.3  

This ambiguity makes it difficult for organisations to determine what constitutes compliance at any given time and could lead to potential inconsistencies in implementation and enforcement. Moreover, the rapid pace of technological change means that what is considered "state-of-the-art" today will become outdated, further complicating compliance efforts.

However, this is not unique to NIS regulation. As EU scholars have noted, while “state-of-the-art" is widely referred to in legal text relating to technology, there is no standardised legal definition of what it actually constitutes.4

Defining state-of-the-art cybersecurity

In this blog, we outline technical considerations for state-of-the-art cybersecurity. We draw from expertise within our own business and in academia as well as guidelines and security standards set by national agencies, such as Germany’s Federal Office for Information Security (BSI) or Spain’s National Security Framework (ENS), to put forward five criteria to define state-of-the-art cybersecurity.

The five core criteria include:

  • Continuous monitoring
  • Incident correlation
  • Detection of anomalous activity
  • Autonomous response
  • Proactive cyber resilience

These principles build on long-standing security considerations, such as business continuity, vulnerability management and basic security hygiene practices.  

Although these considerations are written in the context of the NIS2 Directive, they are likely to also be relevant for other jurisdictions. We hope these criteria help organisations understand how to best meet their responsibilities under the NIS2 Directive and assist Competent Authorities in defining compliance expectations for the organisations they regulate.  

Ultimately, adopting state-of-the-art cyber defences is crucial for ensuring that organisations are equipped with the best tools to combat new and fast-growing threats. Leading technical authorities, such as the UK National Cyber Security Centre (NCSC), recognise that adoption of AI-powered cyber defences will offset the increased volume and impact of AI on cyber threats.5

State of the art cybersecurity in the context of NIS2

1. Continuous monitoring

Continuous monitoring is required to protect an increasingly complex attack surface from attackers.

First, organisations' attack surfaces have expanded following the widespread adoption of hybrid or cloud infrastructures and the increased adoption of connected Internet of Things (IoT) devices.6 This exponential growth creates a complex digital environment for organisations, making it difficult for security teams to track all internet-facing assets and identify potential vulnerabilities.

Second, with the significant increase in the speed and sophistication of cyber-attacks, organisations face a greater need to detect security threats and non-compliance issues in real-time.  

Continuous monitoring, defined by the U.S. National Institute of Standards and Technology (NIST) as the ability to maintain “ongoing awareness of information security, vulnerabilities, and threats to support organizational risk management decisions,”7 has therefore become a cornerstone of an effective cybersecurity strategy. By implementing continuous monitoring, organisations can ensure a real-time understanding of their attack surface and that new external assets are promptly accounted for. For instance, Spain’s technical guidelines for regulation, as set forth by the National Security Framework (Royal Decree 311/2022), highlight the importance of adopting continuous monitoring to detect anomalous activities or behaviours and to ensure timely responses to potential threats (article 10).8  

This can be achieved through the following means:  

All assets that form part of an organisation's estate, both known and unknown, must be identified and continuously monitored for current and emerging risks. Germany’s BSI mandates the continuous monitoring of all protocol and logging data in real-time (requirement #110).9 This should be conducted alongside any regular scans to detect unknown devices or cases of shadow IT, or the use of unauthorised or unmanaged applications and devices within an organisation, which can expose internet-facing assets to unmonitored risks. Continuous monitoring can therefore help identify potential risks and high-impact vulnerabilities within an organisation's digital estate and eliminate potential gaps and blind spots.

Organisations looking to implement more efficient continuous monitoring strategies may turn to automation, but, as the BSI notes, it is important for responsible parties to be immediately warned if an alert is raised (reference 110).10 Following the BSI’s recommendations, the alert must be examined and, if necessary, contained within a short period of time corresponding with the analysis of the risk at hand.

Finally, risk scoring and vulnerability mapping are also essential parts of this process. Looking across the Atlantic, the US’ National Institute of Standards and Technology (NIST) defines continuous monitoring as “maintaining ongoing awareness of information security, vulnerabilities, and threats to support organizational risk management decisions”.11 Continuous monitoring helps identify potential risks and significant vulnerabilities within an organisation's digital assets, fostering a dynamic understanding of risk. By doing so, risk scoring and vulnerability mapping allows organisations to prioritise the risks associated with their most critically exposed assets.

2. Correlation of incidents across your entire environment

Viewing and correlating incident alerts when working with different platforms and tools poses significant challenges to SecOps teams. Security professionals often struggle to cross-reference alerts efficiently, which can lead to potential delays in identifying and responding to threats. The complexity of managing multiple sources of information can overwhelm teams, making it difficult to maintain a cohesive understanding of the security landscape.

This fragmentation underscores the need for a centralised approach that provides a "single pane of glass" view of all cybersecurity alerts. These systems streamline the process of monitoring and responding to incidents, enabling security teams to act more swiftly and effectively. By consolidating alerts into a unified interface, organisations can enhance their ability to detect and mitigate threats, ultimately improving their overall security posture.  

To achieve consolidation, organisations should consider the role automation can play when reviewing and correlating incidents. This is reflected in Spain’s technical guidelines for national security regulations regarding the requirements for the “recording of activity” (reinforcement R5).12 Specifically, the guidelines state that:  

"The system shall implement tools to analyses and review system activity and audit information, in search of possible or actual security compromises. An automatic system for collection of records, correlation of events and automatic response to them shall be available”.13  

Similarly, the German guidelines stress that automated central analysis is essential not only for recording all protocol and logging data generated within the system environment but also to ensure that the data is correlated to ensure that security-relevant processes are visible (article 115).14

Correlating disparate incidents and alerts is especially important when considering the increased connectivity between IT and OT environments driven by business and functional requirements. Indeed, organisations that believe they have air-gapped systems are now becoming aware of points of IT/OT convergence within their systems. It is therefore crucial for organisations managing both IT and OT environments to be able to visualise and secure devices across all IT and OT protocols in real-time to identify potential spillovers.  

By consolidating data into a centralised system, organisations can achieve a more resilient posture. This approach exposes and eliminates gaps between people, processes, and technology before they can be exploited by malicious actors. As seen in the German and Spanish guidelines, a unified view of security alerts not only enhances the efficacy of threat detection and response but also ensures comprehensive visibility and control over the organisation's cybersecurity posture.

3. Detection of anomalous activity  

Recent research highlights the emergence of a "new normal" in cybersecurity, marked by an increase in zero-day vulnerabilities. Indeed, for the first time since sharing their annual list, the Five Eyes intelligence alliance reported that in 2023, the majority of the most routinely exploited vulnerabilities were initially exploited as zero-days.15  

To effectively combat these advanced threats, policymakers, industry and academic stakeholders alike recognise the importance of anomaly-based techniques to detect both known and unknown attacks.

As AI-enabled threats become more prevalent,16 traditional cybersecurity methods that depend on lists of "known bads" are proving inadequate against rapidly evolving and sophisticated attacks. These legacy approaches are limited because they can only identify threats that have been previously encountered and cataloged. However, cybercriminals are constantly developing new, never-before-seen threats, such as signatureless ransomware or living off the land techniques, which can easily bypass these outdated defences.

The importance of anomaly detection in cybersecurity can be found in Spain’s technical guidelines, which states that “tools shall be available to automate the prevention and response process by detecting and identifying anomalies17” (reinforcement R4 prevention and automatic response to "incident management”).  

Similarly, the UK NCSC’s Cyber Assessment Framework (CAF) highlights how anomaly-based detection systems are capable of detecting threats that “evade standard signature-based security solutions” (Principle C2 - Proactive Security Event Discovery18). The CAF’s C2 principle further outlines:  

“The science of anomaly detection, which goes beyond using pre-defined or prescriptive pattern matching, is a challenging area. Capabilities like machine learning are increasingly being shown to have applicability and potential in the field of intrusion detection.”19

By leveraging machine learning and multi-layered AI techniques, organisations can move away from static rules and signatures, adopting a more behavioural approach to identifying and containing risks. This shift not only enhances the detection of emerging threats but also provides a more robust defence mechanism.

A key component of this strategy is behavioral zero trust, which focuses on identifying unauthorized and out-of-character attempts by users, devices, or systems. Implementing a robust procedure to verify each user and issuing the minimum required access rights based on their role and established patterns of activity is essential. Organisations should therefore be encouraged to follow a robust procedure to verify each user and issue the minimum required access rights based on their role and expected or established patterns of activity. By doing so, organisations can stay ahead of emerging threats and embrace a more dynamic and resilient cybersecurity strategy.  

4. Autonomous response

The speed at which cyber-attacks occur means that defenders must be equipped with tools that match the sophistication and agility of those used by attackers. Autonomous response tools are thus essential for modern cyber defence, as they enable organisations to respond to both known and novel threats in real time.  

These tools leverage a deep contextual and behavioral understanding of the organisation to take precise actions, effectively containing threats without disrupting business operations.

To avoid unnecessary business disruptions and maintain robust security, especially in more sensitive networks such as OT environments, it is crucial for organisations to determine the appropriate response depending on their environment. This can range from taking autonomous and native actions, such as isolating or blocking devices, or integrating their autonomous response tool with firewalls or other security tools to taking customized actions.  

Autonomous response solutions should also use a contextual understanding of the business environment to make informed decisions, allowing them to contain threats swiftly and accurately. This means that even as cyber-attacks evolve and become more sophisticated, organisations can maintain continuous protection without compromising operational efficiency.  

Indeed, research into the adoption of autonomous cyber defences points to the importance of implementing “organisation-specific" and “context-informed” approaches.20  To decide the appropriate level of autonomy for each network action, it is argued, it is essential to use evidence-based risk prioritisation that is customised to the specific operations, assets, and data of individual enterprises.21

By adopting autonomous response solutions, organisations can ensure their defences are as dynamic and effective as the threats they face, significantly enhancing their overall security posture.

5. Proactive cyber resilience  

Adopting a proactive approach to cybersecurity is crucial for organisations aiming to safeguard their operations and reputation. By hardening their defences enough so attackers are unable to target them effectively, organisations can save significant time and money. This proactive stance helps reduce business disruption, reputational damage, and the need for lengthy, resource-intensive incident responses.

Proactive cybersecurity incorporates many of the strategies outlined above. This can be seen in a recent survey of information technology practitioners, which outlines four components of a proactive cybersecurity culture: (1) visibility of corporate assets, (2) leveraging intelligent and modern technology, (3) adopting consistent and comprehensive training methods and (4) implementing risk response procedures.22 To this, we may also add continuous monitoring which allows organisations to understand the most vulnerable and high-value paths across their architectures, allowing them to secure their critical assets more effectively.  

Alongside these components, a proactive cyber strategy should be based on a combined business context and knowledge, ensuring that security measures are aligned with the organisation's specific needs and priorities.  

This proactive approach to cyber resilience is reflected in Spain’s technical guidance (article 8.2): “Prevention measures, which may incorporate components geared towards deterrence or reduction of the exposure surface, should eliminate or reduce the likelihood of threats materializing.”23 It can also be found in the NCSC’s CAF, which outlines how organisations can achieve “proactive attack discovery” (see Principle C2).24 Likewise, Belgium’s NIS2 transposition guidelines mandate the use of preventive measures to ensure the continued availability of services in the event of exceptional network failures (article 30).25  

Ultimately, a proactive approach to cybersecurity not only enhances protection but also lowers regulatory risk and supports the overall resilience and stability of the organisation.

Looking forward

The NIS2 Directive marked a significant regulatory milestone in strengthening cybersecurity across the EU.26 Given the impact of emerging technologies, such as AI, on cybersecurity, it is to see that Member States are encouraged to promote the adoption of ‘state-of-the-art' cybersecurity across regulated entities.  

In this blog, we have sought to translate what state-of-the-art cybersecurity may look like for organisations looking to enhance their cybersecurity posture. To do so, we have built on existing cybersecurity guidance, research and our own experience as an AI-cybersecurity company to outline five criteria: continuous monitoring, incident correlation, detection of anomalous activity, autonomous response, and proactive cyber resilience.

By embracing these principles and evolving cybersecurity practices in line with the state-of-the-art, organisations can comply with the NIS2 Directive while building a resilient cybersecurity posture capable of withstanding evolutions in the cyber threat landscape. Looking forward, it will be interesting to see how other jurisdictions embrace new technologies, such as AI, in solving the cybersecurity problem.

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References

[1] https://www.enisa.europa.eu/publications/implementation-guidance-on-nis-2-security-measures

[2] https://www.teletrust.de/fileadmin/user_upload/2023-05_TeleTrusT_Guideline_State_of_the_art_in_IT_security_EN.pdf

[3] https://kpmg.com/uk/en/home/insights/2024/04/what-does-nis2-mean-for-energy-businesses.html

[4] https://orbilu.uni.lu/bitstream/10993/50878/1/SCHMITZ_IFIP_workshop_sota_author-pre-print.pdf

[5]https://www.ncsc.gov.uk/report/impact-of-ai-on-cyber-threat

[6] https://www.sciencedirect.com/science/article/pii/S2949715923000793

[7] https://csrc.nist.gov/glossary/term/information_security_continuous_monitoring

[8] https://ens.ccn.cni.es/es/docman/documentos-publicos/39-boe-a-2022-7191-national-security-framework-ens/file

[10] https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/KRITIS/Konkretisierung_Anforderungen_Massnahmen_KRITIS.html

[11] https://nvlpubs.nist.gov/nistpubs/legacy/sp/nistspecialpublication800-137.pdf

[12] https://ens.ccn.cni.es/es/docman/documentos-publicos/39-boe-a-2022-7191-national-security-framework-ens/file

[13] https://ens.ccn.cni.es/es/docman/documentos-publicos/39-boe-a-2022-7191-national-security-framework-ens/file

[14] https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/KRITIS/Konkretisierung_Anforderungen_Massnahmen_KRITIS.html

[15] https://therecord.media/surge-zero-day-exploits-five-eyes-report

[16] https://www.ncsc.gov.uk/report/impact-of-ai-on-cyber-threat

[17] https://ens.ccn.cni.es/es/docman/documentos-publicos/39-boe-a-2022-7191-national-security-framework-ens/file

[18] https://www.ncsc.gov.uk/collection/cyber-assessment-framework/caf-objective-c-detecting-cyber-security-events/principle-c2-proactive-security-event-discovery

[19] https://www.ncsc.gov.uk/collection/cyber-assessment-framework/caf-objective-c-detecting-cyber-security-events/principle-c2-proactive-security-event-discovery

[20] https://cetas.turing.ac.uk/publications/autonomous-cyber-defence-autonomous-agents

[21] https://cetas.turing.ac.uk/publications/autonomous-cyber-defence-autonomous-agents

[22] https://www.researchgate.net/publication/376170443_Cultivating_Proactive_Cybersecurity_Culture_among_IT_Professional_to_Combat_Evolving_Threats

[23] https://ens.ccn.cni.es/es/docman/documentos-publicos/39-boe-a-2022-7191-national-security-framework-ens/file

[24] https://www.ncsc.gov.uk/collection/cyber-assessment-framework/caf-objective-c-detecting-cyber-security-events/principle-c2-proactive-security-event-discovery

[25] https://www.ejustice.just.fgov.be/mopdf/2024/05/17_1.pdf#page=49

[26] ENISA, NIS Directive 2

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About the author
Livia Fries
Public Policy Manager, EMEA
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