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December 12, 2022

ML Integration for Third-Party EDR Alerts

The advantages and benefits of combining EDR technologies with Darktrace: how this integration can enhance your cybersecurity strategy.
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.
Written by
Max Heinemeyer
Global Field CISO
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12
Dec 2022

This blog demonstrates how we use EDR integration in Darktrace for detection & investigation. We’ll look at four key features, which are summarized with an example below:  

1)    Contextualizing existing Darktrace information – E.g. ‘There was a Microsoft Defender for Endpoint (MDE) alert 5 minutes after Darktrace saw the device beacon to an unusual destination on the internet. Let me pivot back into the Defender UI’
2)    Cross-data detection engineering
‘Darktrace, create an alert or trigger a response if you see a specific MDE alert and a native Darktrace detection on the same entity over a period of time’
3)    Applying unsupervised machine learning to third-party EDR alerts
‘Darktrace, create an alert or trigger a response if there is a specific MDE alert that is unusual for the entity, given the context’
4)    Use third-party EDR alerts to trigger AI Analyst
‘AI Analyst, this low-fidelity MDE alert flagged something on the endpoint. Please take a deep look at that device at the time of the Defender alert, conduct an investigation on Darktrace data and share your conclusions about whether there is more to it or not’ 

MDE is used as an example above, but Darktrace’s EDR integration capabilities extend beyond MDE to other EDRs as well, for example to Sentinel One and CrowdStrike EDR.

Darktrace brings its Self-Learning AI to your data, no matter where it resides. The data can be anywhere – in email environments, cloud, SaaS, OT, endpoints, or the network, for example. Usually, we want to get as close to the raw data as possible to get the maximum context for our machine learning. 

We will explain how we leverage high-value integrations from our technology partners to bring further context to Darktrace, but also how we apply our Self-Learning AI to third-party data. While there are a broad range of integrations and capabilities available, we will primarily look at Microsoft Defender for Endpoint, CrowdStrike, and SentinelOne and focus on detection in this blog post. 

The Nuts and Bolts – Setting up the Integration

Darktrace is an open platform – almost everything it does is API-driven. Our system and machine learning are flexible enough to ingest new types of data & combine it with already existing information.  

The EDR integrations mentioned here are part of our 1-click integrations. All it requires is the right level of API access from the EDR solutions and the ability for Darktrace to communicate with the EDR’s API. This type of integration can be setup within minutes – it currently doesn’t require additional Darktrace licenses.

Figure 1: Set-up of Darktrace Graph Security API integration

As soon as the setup is complete, it enables various additional capabilities. 
Let’s look at some of the key detection & investigation-focussed capabilities step-by-step.

Contextualizing Existing Darktrace Information

The most basic, but still highly-useful integration is enriching existing Darktrace information with EDR alerts. Darktrace shows a chronological history of associated telemetry and machine learning for each entity observed in the entities event log. 

With an EDR integration enabled, we now start to see EDR alerts for the respective entities turn up in the entity’s event log at the correct point in time – with a ton of context and a 1-click pivot back to the native EDR console: 

Figure 2: A pivot from the Darktrace Threat Visualizer to Microsoft Defender

This context is extremely useful to have in a single screen during investigations. Context is king – it reduces time-to-meaning and skill required to understand alerts.

Cross-Data Detection Engineering

When an EDR integration is activated, Darktrace enables an additional set of detections that leverage the new EDR alerts. This comes out of the box and doesn’t require any further detection engineering. It is worth mentioning though that the new EDR information is being made available in the background for bespoke detection engineering, if advanced users want to leverage these as custom metrics.

The trick here is that the added context provided by the additional EDR alerts allows for more refined detections – primarily to detect malicious activity with higher confidence. A network detection showing us beaconing over an unusual protocol or port combination to a rare destination on the internet is great – but seeing within Darktrace that CrowdStrike detected a potentially hostile file or process three minutes prior to the beaconing detection on the same device will greatly help to prioritize the detections and aid a subsequent investigation.

Here is an example of what this looks like in Darktrace:

Figure 3: A combined model breach in the Threat Visualizer

Applying Unsupervised Machine Learning to Third-Party EDR Alerts


Once we start seeing EDR alerts in Darktrace, we can start treating it like any other data – by applying unsupervised machine learning to it. This means we can then understand how unusual a given EDR detection is for each device in question. This is extremely powerful – it allows to reduce noisy alerts without requiring ongoing EDR alert tuning and opens a whole world of new detection capabilities.

As an example – let’s imagine a low-level malware alert keeps appearing from the EDR on a specific device. This might be a false-positive in the EDR, or just not of interest for the security team, but they may not have the resources or knowledge to further tune their EDR and get rid of this noisy alert.

While Darktrace keeps adding this as contextual information in the device’s event log, it could, depending on the context of the device, the EDR alert, and the overall environment, stop alerting on this particular EDR malware alert on this specific device if it stops being unusual. Over time, noise is reduced across the environment – but if that particular EDR alert appears on another device, or on the same device in a different context, it might get flagged again, as it now is unusual in the given context.

Darktrace then goes a step further, taking those unusual EDR alerts and combining them with unusual activity seen in other Darktrace coverage areas, like the network for example. Combining an unusual EDR alert with an unusual lateral movement attempt, for example, allows it to find these combined, high-precision, cross-data set anomalous events that are highly indicative of an active cyber-attack – without having to pre-define the exact nature of what ‘unusual’ looks like.

Figure 4: Combined EDR & network detection using unsupervised machine learning in Darktrace

Use Third-Party EDR Alerts to Trigger AI Analyst

Everything we discussed so far is great for improving precision in initial detections, adding context, and cutting through alert-noise. We don’t stop there though – we can also now use the third-party EDR alerts to trigger our investigation engine, the AI Analyst.

Cyber AI Analyst replicates and automates typical level 1 and level 2 Security Operations Centre (SOC) workflows. It is usually triggered by every native Darktrace detection. This is not a SOAR where playbooks are statically defined – AI Analyst builds hypotheses, gathers data, evaluates the data & reports on its findings based on the context of each individual scenario & investigation. 

Darktrace can use EDR alerts as starting points for its investigation, with every EDR alert ingested now triggering AI Analyst. This is similar to giving a (low-level) EDR alert to a human analyst and telling them: ‘Go and take a look at information in Darktrace and try to conclude whether there is more to this EDR alert or not.’

The AI Analyst subsequently looks at the entity which had triggered the EDR alert and investigates all available Darktrace data on that entity, over a period of time, in light of that EDR alert. It does not pivot outside Darktrace itself for that investigation (e.g. back into the Microsoft console) but looks at all of the context natively available in Darktrace. If concludes that there is more to this EDR alert – e.g. a bigger incident – it will report on that and clearly flag it. The report can of course be directly downloaded as a PDF to be shared with other stakeholders.

This comes in handy for a variety of reasons – primarily to further automate security operations and alleviate pressure from human teams. AI Analyst’s investigative capabilities sit on top of everything we discussed so far (combining EDR detections with detections from other coverage areas, applying unsupervised machine learning to EDR detections, …).

However, it can also come in handy to follow up on low-severity EDR alerts for which you might not have the human resources to do so.

The below screenshot shows an example of a concluded AI Analyst investigation that was triggered by an EDR alert:

Figure 5: An AI Analyst incident trained on third-party data

The Impact of EDR Integrations

The purpose behind all of this is to augment human teams, save them time and drive further security automation.

By ingesting third-party endpoint alerts, combining it with our existing intelligence and applying unsupervised machine learning to it, we achieve that further security automation. 

Analysts don’t have to switch between consoles for investigations. They can leverage our high-fidelity detections that look for unusual endpoint alerts, in combination with our already powerful detections across cloud and email systems, zero trust architecture, IT and OT networks, and more. 

In our experience, this pinpoints the needle in the haystack – it cuts through noise and reduces the mean-time-to-detect and mean-time-to-investigate drastically.

All of this is done out of the box in Darktrace once the endpoint integrations are enabled. It does not need a data scientist to make the machine learning work. Nor does it need a detection engineer or threat hunter to create bespoke, meaningful detections. We want to reduce the barrier to entry for using detection and investigation solutions – in terms of skill and experience required. The system is still flexible, transparent, and open, meaning that advanced users can create their own combined detections, leveraging unsupervised machine learning across different data sets with a few clicks.

There are of course more endpoint integration capabilities available than what we covered here, and we will explore these in future blog posts.

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.
Written by
Max Heinemeyer
Global Field CISO

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May 29, 2025

Why attack-centric approaches to email security can’t cope with modern threats

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What’s the problem with an attack-centric mindset?

For decades, traditional email security strategies have been built around an attack-centric mindset. Secure Email Gateways (SEGs) and other legacy solutions operate on the principle of identifying and blocking known threats. These systems rely heavily on predefined threat intelligence – blacklists, malware signatures, and reputation-based analysis – to filter out malicious content before it reaches the inbox.

While this approach was sufficient when email threats were relatively static and signature-based, it’s increasingly ineffective against the sophistication of modern attacks. Techniques like spear phishing, business email compromise (BEC), and supply chain attacks often bypass traditional SEG defenses because they lack obvious malicious indicators. Instead, they leverage social engineering, look-alike domains, and finely tuned spoofing tactics that are designed to evade detection.

The challenge extends beyond just legacy SEGs. Many modern email security providers have inherited the same attack-centric principles, even if they've reimagined the technology stack. While some vendors have shifted to API-based deployments and incorporated AI to automate pattern recognition, the underlying approach remains the same: hunting for threats based on known indicators. This methodology, though it’s undergone modernization using AI, still leaves gaps when it comes to novel, hyper-targeted threats that manipulate user behavior rather than deploy predictable malicious signatures. Attack-centric security will always remain one step behind the attacker.

By the way, native email security already covers the basics

One of the most overlooked realities in email security is that native solutions like Microsoft 365’s built-in security already handle much of the foundational work of attack-centric protection. Through advanced threat intelligence, anti-phishing measures, and malware detection, Microsoft 365 actively scans incoming emails for known threats, using global telemetry to identify patterns and block suspicious content before it even reaches the user’s inbox.

This means that for many organizations, a baseline level of protection against more obvious, signature-based attacks is already in place – but many are still disabling these protections in favour of another attack-centric solution. By layering another attack-centric solution on top, they are effectively duplicating efforts without enhancing their security posture. This overlap can lead to unnecessary complexity, higher costs, and a false sense of enhanced protection when in reality, it’s more of the same.

Rather than duplicating attack-centric protections, the real opportunity lies in addressing the gaps that remain: the threats that are specifically crafted to evade traditional detection methods. This is where a business-centric approach becomes indispensable, complementing the foundational security that’s already built into your infrastructure.

Introducing… the business-centric approach

To effectively defend against advanced threats, organizations need to adopt a business-centric approach to email security. Unlike attack-centric models that hunt for known threats, business-centric security focuses on understanding the typical behaviors, relationships, and communication patterns within your organization. Rather than solely reacting to threats as they are identified, this model continuously learns what “normal” looks like for each user and each inbox.

By establishing a baseline of expected behaviors, business-centric solutions can rapidly detect anomalies that suggest compromise, such as sudden changes in sending patterns, unusual login locations, or subtle shifts in communication tone. This proactive detection method is especially powerful against spear phishing, business email compromise (BEC), and supply chain attacks that are engineered to bypass static defenses. This approach also scales with your organization, learning and adapting as new users are onboarded, communication patterns evolve, and external partners are added.

In an era where AI-driven threats are becoming the norm, having email security that knows your users and inboxes better than the attacker does is a critical advantage.

Why native + business-centric email security is the winning formula

By pairing native security with a business-centric model, organizations can cover the full spectrum of threats – from signature-based malware to sophisticated, socially engineered attacks. Microsoft 365’s in-built security manages the foundational risks, while business-centric defense identifies subtle anomalies and targeted threats that legacy approaches miss.

Layering Darktrace on top of your native Microsoft security eliminates duplicate capabilities, costs and workflows without reducing functionality

Rather than layering redundant attack-centric solutions on top of existing protections, the future of email security lies in leveraging what’s already in place and building on it with smarter, behavior-based detection. The Swiss Cheese Model is a useful one to refer to here: by acknowledging that no single defense can offer complete protection, layering defenses that plug each other’s gaps – like slices of Swiss cheese – becomes critical.

This combination also allows security teams to focus their efforts more effectively. With native solutions catching broad-based, known threats, the business-centric layer can prioritize real anomalies, minimizing false positives and accelerating response times. Organizations benefit from reduced overlap, streamlined costs, and a stronger overall security posture.

Download the full guide to take the first step towards achieving your next-generation security stack.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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May 30, 2025

PumaBot: Novel Botnet Targeting IoT Surveillance Devices

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Introduction: PumaBot attacking IoT devices

Darktrace researchers have identified a custom Go-based Linux botnet named “PumaBot” targeting embedded Linux Internet of Things (IoT) devices. Rather than scanning the Internet, the malware retrieves a list of targets from a command-and-control (C2) server and attempts to brute-force SSH credentials. Upon gaining access, it receives remote commands and establishes persistence using system service files. This blog post provides a breakdown of its key functionalities, and explores binaries related to the campaign.

Technical Analysis

Filename: jierui

md5: cab6f908f4dedcdaedcdd07fdc0a8e38

The Go-based botnet gains initial access through brute-forcing SSH credentials across a list of harvested IP addresses. Once it identifies a valid credential pair, it logs in, deploys itself, and begins its replication process.

Overview of Jierui functions
Figure 1: Overview of Jierui functions.

The domain associated with the C2 server did not resolve to an IP address at the time of analysis. The following details are a result of static analysis of the malware.

The malware begins by retrieving a list of IP addresses of likely devices with open SSH ports from the C2 server (ssh.ddos-cc[.]org) via the getIPs() function. It then performs brute-force login attempts on port 22 using credential pairs also obtained from the C2 through the readLinesFromURL(), brute(), and trySSHLogin() functions.

Within trySSHLogin(), the malware performs several environment fingerprinting checks. These are used to avoid honeypots and unsuitable execution environments, such as restricted shells. Notably, the malware checks for the presence of the string “Pumatronix”, a manufacturer of surveillance and traffic camera systems, suggesting potential IoT targeting or an effort to evade specific devices [1].

Fingerprinting of “Pumatronix”.
Figure 2: Fingerprinting of “Pumatronix”.

If the environment passes these checks, the malware executes uname -a to collect basic system information, including the OS name, kernel version, and architecture. This data, along with the victim's IP address, port, username, and password, is then reported back to the C2 in a JSON payload.

Of note, the bot uses X-API-KEY: jieruidashabi, within a custom header when it communicates with the C2 server over HTTP.

The malware writes itself to /lib/redis, attempting to disguise itself as a legitimate Redis system file. It then creates a persistent systemd service in /etc/systemd/system, named either redis.service or mysqI.service (note the spelling of mysql with a capital I) depending on what has been hardcoded into the malware. This allows the malware to persist across reboots while appearing benign.

[Unit]
Description=redis Server Service

[Service]
Type=simple
Restart=always
RestartSec=1
User=root
ExecStart=/lib/redis e

[Install]
WantedBy=multi-user.target

In addition to gaining persistence with a systemd service, the malware also adds its own SSH keys into the users’ authorized_keys file. This ensures that access can be maintained, even if the service is removed.

A function named cleankill() contains an infinite loop that repeatedly attempts to execute the commands “xmrig” and “networkxm”. These are launched without full paths, relying on the system's PATH variable suggesting that the binaries may be downloaded or unpacked elsewhere on the system. The use of “time.Sleep” between attempts indicates this loop is designed to ensure persistence and possibly restart mining components if they are killed or missing.

During analysis of the botnet, Darktrace discovered related binaries that appear to be part of a wider campaign targeting Linux systems.

Filename: ddaemon
Md5: 48ee40c40fa320d5d5f8fc0359aa96f3

Ddaemon is a Go-based backdoor. The malware begins by parsing command line arguments and if conditions are met, enters a loop where it periodically verifies the MD5 hash of the binary. If the check fails or an update is available, it downloads a new version from a C2 server (db.17kp[.]xyz/getDdaemonMd5), verifies it and replaces the existing binary with a file of the same name and similar functionality (8b37d3a479d1921580981f325f13780c).

The malware uses main_downloadNetwork() to retrieve the binary “networkxm” into /usr/src/bao/networkxm. Additionally, the bash script “installx.sh” is also retrieved from the C2 and executed. The binary ensures persistence by writing a custom systemd service unit that auto starts on boot and executes ddaemon.

Filename: networkxm
Md5: be83729e943d8d0a35665f55358bdf88

The networkxm binary functions as an SSH brute-force tool, similar to the botnet. First it checks its own integrity using MD5 hashes and contacts the C2 server (db.17kp[.]xyz) to compare its hash with the latest version. If an update is found, it downloads and replaces itself.

Part of networkxm checking MD5 hash.
Figure 3: Part of networkxm checking MD5 hash.
MD5 hash
Figure 4: MD5 hash

After verifying its validity, it enters an infinite loop where it fetches a password list from the C2 (/getPassword), then attempts SSH connections across a list of target IPs from the /getIP endpoint. As with the other observed binaries, a systemd service is created if it doesn’t already exist for persistence in /etc/systemd/system/networkxm.service.

Bash script installx.sh.
Figure 5: Bash script installx.sh.

Installx.sh is a simple bash script used to retrieve the script “jc.sh” from 1.lusyn[.]xyz, set permissions, execute and clear bash history.

Figure 6: Snippet of bash script jc.sh.

The script jc.sh starts by detecting the operating system type Debian-based or Red Hat-based and determines the location of the pam_unix.so file. Linux Pluggable Authentication Modules (PAM) is a framework that allows for flexible and centralized user authentication on Linux systems. PAM allows system administrators to configure how users are authenticated for services like login, SSH, or sudo by plugging in various authentication modules.

Jc.sh then attempts to fetch the current version of PAM installed on the system and formats that version to construct a URL. Using either curl or wget, the script downloads a replacement pam_unix.so file from a remote server and replaces the existing one, after disabling file immutability and backing up the original.

The script also downloads and executes an additional binary named “1” from the same remote server. Security settings are modified including enabling PAM in the SSH configuration and disabling SELinux enforcement, before restarting the SSH service. Finally, the script removes itself from the system.

Filename: Pam_unix.so_v131
md5: 1bd6bcd480463b6137179bc703f49545

Based on the PAM version that is retrieved from the bash query, the new malicious PAM replaces the existing PAM file. In this instance, pam_unix.so_v131 was retrieved from the server based on version 1.3.1. The purpose of this binary is to act as a rootkit that steals credentials by intercepting successful logins. Login data can include all accounts authenticated by PAM, local and remote (SSH). The malware retrieves the logged in user, the password and verifies that the password is valid. The details are stored in a file “con.txt” in /usr/bin/.

Function storing logins to con.txt
Figure 7: Function storing logins to con.txt

Filename: 1

md5: cb4011921894195bcffcdf4edce97135

In addition to the malicious PAM file, a binary named “1” is also retrieved from the server http://dasfsdfsdfsdfasfgbczxxc[.]lusyn[.]xyz/jc/1. The binary “1” is used as a watcher for the malicious PAM file using inotify to monitor for “con.txt” being written or moved to /usr/bin/.

Following the daemonize() function, the binary is run daemonized ensuring it runs silently in the background. The function read_and_send_files() is called which reads the contents of “/usr/bin/con.txt”, queries the system IP with ifconfig.me, queries SSH ports and sends the data to the remote C2 (http://dasfsdfsdfsdfasfgbczxxc[.]lusyn[.]xyz/api/).

Command querying SSH ports.
Figure 8: Command querying SSH ports.

For persistence, a systemd service (my_daemon.service) is created to autostart the binary and ensure it restarts if the service has been terminated. Finally, con.txt is deleted, presumably to remove traces of the malware.

Conclusion

The botnet represents a persistent Go-based SSH threat that leverages automation, credential brute-forcing, and native Linux tools to gain and maintain control over compromised systems. By mimicking legitimate binaries (e.g., Redis), abusing systemd for persistence, and embedding fingerprinting logic to avoid detection in honeypots or restricted environments, it demonstrates an intent to evade defenses.

While it does not appear to propagate automatically like a traditional worm, it does maintain worm-like behavior by brute-forcing targets, suggesting a semi-automated botnet campaign focused on device compromise and long-term access.

[related-resource]

Recommendations

  1. Monitor for anomalous SSH login activity, especially failed login attempts across a wide IP range, which may indicate brute-force attempts.
  2. Audit systemd services regularly. Look for suspicious entries in /etc/systemd/system/ (e.g., misspelled or duplicate services like mysqI.service) and binaries placed in non-standard locations such as /lib/redis.
  3. Inspect authorized_keys files across user accounts for unknown SSH keys that may enable unauthorized access.
  4. Filter or alert on outbound HTTP requests with non-standard headers, such as X-API-KEY: jieruidashabi, which may indicate botnet C2 communication.
  5. Apply strict firewall rules to limit SSH exposure rather than exposing port 22 to the internet.

Appendices

References

1.     https://pumatronix.com/

Indicators of Compromise (IoCs)

Hashes

cab6f908f4dedcdaedcdd07fdc0a8e38 - jierui

a9412371dc9247aa50ab3a9425b3e8ba - bao

0e455e06315b9184d2e64dd220491f7e - networkxm

cb4011921894195bcffcdf4edce97135 - 1
48ee40c40fa320d5d5f8fc0359aa96f3 - ddaemon
1bd6bcd480463b6137179bc703f49545 - pam_unix.so_v131

RSA Key

ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQC0tH30Li6Gduh0Jq5A5dO5rkWTsQlFttoWzPFnGnuGmuF+fwIfYvQN1z+WymKQmX0ogZdy/CEkki3swrkq29K/xsyQQclNm8+xgI8BJdEgTVDHqcvDyJv5D97cU7Bg1OL5ZsGLBwPjTo9huPE8TAkxCwOGBvWIKUE3SLZW3ap4ciR9m4ueQc7EmijPHy5qds/Fls+XN8uZWuz1e7mzTs0Pv1x2CtjWMR/NF7lQhdi4ek4ZAzj9t/2aRvLuNFlH+BQx+1kw+xzf2q74oBlGEoWVZP55bBicQ8tbBKSN03CZ/QF+JU81Ifb9hy2irBxZOkyLN20oSmWaMJIpBIsh4Pe9 @root

Network

http://ssh[.]ddos-cc.org:55554

http://ssh[.]ddos-cc.org:55554/log_success

http://ssh[.]ddos-cc.org:55554/get_cmd

http://ssh[.]ddos-cc.org:55554/pwd.txt

https://dow[.]17kp.xyz/

https://input[.]17kp.xyz/

https://db[.]17kp[.]xyz/

http://1[.]lusyn[.]xyz

http://1[.]lusyn[.]xyz/jc/1

http://1[.]lusyn[.]xyz/jc/jc.sh

http://1[.]lusyn[.]xyz/jc/aa

http://1[.]lusyn[.]xyz/jc/cs

http://dasfsdfsdfsdfasfgbczxxc[.]lusyn[.]xyz/api

http://dasfsdfsdfsdfasfgbczxxc[.]lusyn[.]xyz/jc

Detection Rule

rule Linux_PumaBot

{

  meta:

      description = "Rule to match on PumaBot samples"

      author = "tgould@cadosecurity.com"

  strings:

      $xapikey = "X-API-KEY" ascii

      $get_ips = "?count=5000" ascii

      $exec_start = "ExecStart=/lib/redis" ascii

      $svc_name1 = "redis.service" ascii

      $svc_name2 = "mysqI.service" ascii

      $uname = "uname -a" ascii

      $pumatronix = "Pumatronix" ascii

  condition:

      uint32(0) == 0x464c457f and

      all of (

          $xapikey,

          $uname,

          $get_ips,

          $exec_start

      ) and any of (

          $svc_name1,

          $svc_name2

      ) and $pumatronix

}

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About the author
Tara Gould
Threat Researcher
Your data. Our AI.
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