Blog
/
AI
/
February 10, 2025

From Hype to Reality: How AI is Transforming Cybersecurity Practices

AI hype is everywhere, but not many vendors are getting specific. Darktrace’s multi-layered AI combines various machine learning techniques for behavioral analytics, real-time threat detection, investigation, and autonomous response.
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
Nicole Carignan
SVP, Security & AI Strategy, Field CISO
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
10
Feb 2025

AI is everywhere, predominantly because it has changed the way humans interact with data. AI is a powerful tool for data analytics, predictions, and recommendations, but accuracy, safety, and security are paramount for operationalization.

In cybersecurity, AI-powered solutions are becoming increasingly necessary to keep up with modern business complexity and this new age of cyber-threat, marked by attacker innovation, use of AI, speed, and scale. The emergence of these new threats calls for a varied and layered approach in AI security technology to anticipate asymmetric threats.

While many cybersecurity vendors are adding AI to their products, they are not always communicating the capabilities or data used clearly. This is especially the case with Large Language Models (LLMs). Many products are adding interactive and generative capabilities which do not necessarily increase the efficacy of detection and response but rather are aligned with enhancing the analyst and security team experience and data retrieval.

Consequently, many  people erroneously conflate generative AI with other types of AI. Similarly, only 31% of security professionals report that they are “very familiar” with supervised machine learning, the type of AI most often applied in today’s cybersecurity solutions to identify threats using attack artifacts and facilitate automated responses. This confusion around AI and its capabilities can result in suboptimal cybersecurity measures, overfitting, inaccuracies due to ineffective methods/data, inefficient use of resources, and heightened exposure to advanced cyber threats.

Vendors must cut through the AI market and demystify the technology in their products for safe, secure, and accurate adoption. To that end, let’s discuss common AI techniques in cybersecurity as well as how Darktrace applies them.

Modernizing cybersecurity with AI

Machine learning has presented a significant opportunity to the cybersecurity industry, and many vendors have been using it for years. Despite the high potential benefit of applying machine learning to cybersecurity, not every AI tool or machine learning model is equally effective due to its technique, application, and data it was trained on.

Supervised machine learning and cybersecurity

Supervised machine models are trained on labeled, structured data to facilitate automation of a human-led trained tasks. Some cybersecurity vendors have been experimenting with supervised machine learning for years, with most automating threat detection based on reported attack data using big data science, shared cyber-threat intelligence, known or reported attack behavior, and classifiers.

In the last several years, however, more vendors have expanded into the behavior analytics and anomaly detection side. In many applications, this method separates the learning, when the behavioral profile is created (baselining), from the subsequent anomaly detection. As such, it does not learn continuously and requires periodic updating and re-training to try to stay up to date with dynamic business operations and new attack techniques. Unfortunately, this opens the door for a high rate of daily false positives and false negatives.

Unsupervised machine learning and cybersecurity

Unlike supervised approaches, unsupervised machine learning does not require labeled training data or human-led training. Instead, it independently analyzes data to detect compelling patterns without relying on knowledge of past threats. This removes the dependency of human input or involvement to guide learning.

However, it is constrained by input parameters, requiring a thoughtful consideration of technique and feature selection to ensure the accuracy of the outputs. Additionally, while it can discover patterns in data as they are anomaly-focused, some of those patterns may be irrelevant and distracting.

When using models for behavior analytics and anomaly detection, the outputs come in the form of anomalies rather than classified threats, requiring additional modeling for threat behavior context and prioritization. Anomaly detection performed in isolation can render resource-wasting false positives.

LLMs and cybersecurity

LLMs are a major aspect of mainstream generative AI, and they can be used in both supervised and unsupervised ways. They are pre-trained on massive volumes of data and can be applied to human language, machine language, and more.

With the recent explosion of LLMs in the market, many vendors are rushing to add generative AI to their products, using it for chatbots, Retrieval-Augmented Generation (RAG) systems, agents, and embeddings. Generative AI in cybersecurity can optimize data retrieval for defenders, summarize reporting, or emulate sophisticated phishing attacks for preventative security.

But, since this is semantic analysis, LLMs can struggle with the reasoning necessary for security analysis and detection consistently. If not applied responsibly, generative AI can cause confusion by “hallucinating,” meaning referencing invented data, without additional post-processing to decrease the impact or by providing conflicting responses due to confirmation bias in the prompts written by different security team members.

Combining techniques in a multi-layered AI approach

Each type of machine learning technique has its own set of strengths and weaknesses, so a multi-layered, multi-method approach is ideal to enhance functionality while overcoming the shortcomings of any one method.

Darktrace’s multi-layered AI engine is powered by multiple machine learning approaches, which operate in combination for cyber defense. This allows Darktrace to protect the entire digital estates of the organizations it secures, including corporate networks, cloud computing services, SaaS applications, IoT, Industrial Control Systems (ICS), and email systems.

Plugged into the organization’s infrastructure and services, our AI engine ingests and analyzes the raw data and its interactions within the environment and forms an understanding of the normal behavior, right down to the granular details of specific users and devices. The system continually revises its understanding about what is normal based on evolving evidence, continuously learning as opposed to baselining techniques.

This dynamic understanding of normal partnered with dozens of anomaly detection models means that the AI engine can identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign. Understanding anomalies through the lens of many models as well as autonomously fine-tuning the models’ performances gives us a higher understanding and confidence in anomaly detection.

The next layer provides event correlation and threat behavior context to understand the risk level of an anomalous event(s). Every anomalous event is investigated by Cyber AI Analyst that uses a combination of unsupervised machine learning models to analyze logs with supervised machine learning trained on how to investigate. This provides anomaly and risk context along with investigation outcomes with explainability.

The ability to identify activity that represents the first footprints of an attacker, without any prior knowledge or intelligence, lies at the heart of the AI system’s efficacy in keeping pace with threat actor innovations and changes in tactics and techniques. It helps the human team detect subtle indicators that can be hard to spot amid the immense noise of legitimate, day-to-day digital interactions. This enables advanced threat detection with full domain visibility.

Digging deeper into AI: Mapping specific machine learning techniques to cybersecurity functions

Visibility and control are vital for the practical adoption of AI solutions, as it builds trust between human security teams and their AI tools. That is why we want to share some specific applications of AI across our solutions, moving beyond hype and buzzwords to provide grounded, technical explanations.

Darktrace’s technology helps security teams cover every stage of the incident lifecycle with a range of comprehensive analysis and autonomous investigation and response capabilities.

  1. Behavioral prediction: Our AI understands your unique organization by learning normal patterns of life. It accomplishes this with multiple clustering algorithms, anomaly detection models, Bayesian meta-classifier for autonomous fine-tuning, graph theory, and more.
  2. Real-time threat detection: With a true understanding of normal, our AI engine connects anomalous events to risky behavior using probabilistic models. 
  3. Investigation: Darktrace performs in-depth analysis and investigation of anomalies, in particular automating Level 1 of a SOC team and augmenting the rest of the SOC team through prioritization for human-led investigations. Some of these methods include supervised and unsupervised machine learning models, semantic analysis models, and graph theory.
  4. Response: Darktrace calculates the proportional action to take in order to neutralize in-progress attacks at machine speed. As a result, organizations are protected 24/7, even when the human team is out of the office. Through understanding the normal pattern of life of an asset or peer group, the autonomous response engine can isolate the anomalous/risky behavior and surgically block. The autonomous response engine also has the capability to enforce the peer group’s pattern of life when rare and risky behavior continues.
  5. Customizable model editor: This layer of customizable logic models tailors our AI’s processing to give security teams more visibility as well as the opportunity to adapt outputs, therefore increasing explainability, interpretability, control, and the ability to modify the operationalization of the AI output with auditing.

See the complete AI architecture in the paper “The AI Arsenal: Understanding the Tools Shaping Cybersecurity.”

Figure 1. Alerts can be customized in the model editor in many ways like editing the thresholds for rarity and unusualness scores above.

Machine learning is the fundamental ally in cyber defense

Traditional security methods, even those that use a small subset of machine learning, are no longer sufficient, as these tools can neither keep up with all possible attack vectors nor respond fast enough to the variety of machine-speed attacks, given their complexity compared to known and expected patterns.

Security teams require advanced detection capabilities, using multiple machine learning techniques to understand the environment, filter the noise, and take action where threats are identified.

Darktrace’s multi-layered AI comes together to achieve behavioral prediction, real-time threat detection and response, and incident investigation, all while empowering your security team with visibility and control.

Download the full report

Discover specifically how Darktrace applies different types of AI to improve cybersecurity efficacy and operations in this technical paper.

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
Nicole Carignan
SVP, Security & AI Strategy, Field CISO

More in this series

No items found.

Blog

/

Email

/

May 29, 2025

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

Default blog imageDefault blog image

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.

[related-resource]

Continue reading
About the author
Carlos Gray
Senior Product Marketing Manager, Email

Blog

/

Network

/

May 30, 2025

PumaBot: Novel Botnet Targeting IoT Surveillance Devices

password login screen on computerDefault blog imageDefault blog image

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

}

Continue reading
About the author
Tara Gould
Threat Researcher
Your data. Our AI.
Elevate your network security with Darktrace AI