February 11, 2025

Defending Against Living-off-the-Land Attacks: Anomaly Detection in Action

Discover how Darktrace detected and responded to cyberattacks using Living-off-the-Land (LOTL) tactics to exploit trusted services and tools on customer networks.

Written by

Alexandra Sentenac

Cyber Analyst

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

Alexandra Sentenac

Cyber Analyst

Feb 2025

What is living-off-the-land?

Threat actors employ a variety of techniques to compromise target networks, including exploiting unpatched vulnerabilities, abusing misconfigurations, deploying backdoors, and creating custom malware. However, these methods generate a lot of noise and are relatively easy for network and host-based monitoring tools to detect, especially once indicators of compromise (IoCs) and tactics, techniques, and procedures (TTPs) are published by the cybersecurity community.

Living-off-the-Land (LOTL) techniques, however, allow attacks to remain nearly invisible to Endpoint Detection and Response (EDR) tools – leveraging trusted protocols, applications and native systems to carry out malicious activity. While mitigations exist, they are often poorly implemented. The Cybersecurity and Infrastructure Security Agency (CISA) found that some organizations “lacked security baselines, allowing [Living-off-the-Land binaries (LOLBins)] to execute and leaving analysts unable to identify anomalous activity” and “organizations did not appropriately tune their detection tools to reduce alert noise, leading to an unmanageable level of alerts to sift through and action" [1].

Darktrace / NETWORK addresses this challenge across Information Technology (IT), Operational Technology (OT), and cloud environments by continuously analyzing network traffic and identifying deviations from normal behavior with its multi-layered AI – helping organizations detect and respond to LOTL attacks in real time.

Darktrace’s detection of LOTL attacks

This blog will review two separate attacks detected by Darktrace that leveraged LOTL techniques at several stages of the intrusion.

Case A

Reconnaissance

In September 2024, a malicious actor gained access to a customer network via their Virtual Private Network (VPN) from two desktop devices that had no prior connection history. Over two days, the attacker conducted multiple network scans, targeting ports associated with Remote Desktop Protocol (RDP) and NTLM authentication. Darktrace detected this unusual activity, triggering multiple alerts for scanning and enumeration activity.

Unusual NTLM authentication attempts using default accounts like “Guest” and “Administrator” were detected. Two days after the initial intrusion, suspicious DRSGetNCChanges requests were observed on multiple domain controllers (DCs), targeting the Directory Replication Service RPC interface (i.e., drsuapi) – a technique used to extract account hashes from DCs. This process can be automated using tools like Mimikatz's DcSync and DCShadow

Around the same time, attacker-controlled devices were seen presenting an admin credential and another credential potentially granting access to Cisco Firewall systems, suggesting successful privilege escalation. Due to the severity of this activity, Darktrace’s Autonomous Response was triggered to prevent the device from further deviation from its normal behavior. However, because Autonomous Response was configured in Human Confirmation mode, the response actions had to be manually applied by the customer.

Figure 1: Cyber AI Analyst Critical Incident showing the unusual DRSGetNCChanges requests following unusual scanning activity.

Lateral movement

Darktrace also detected anomalous RDP connections to domain controllers, originating from an attacker-controlled device using admin and service credentials. The attacker then successfully pivoted to a likely RDP server, leveraging the RDP protocol – one of the most commonly used for lateral movement in network compromises observed by Darktrace.

Tooling

Following an incoming RDP connection, one of the DCs made a successful GET request to the URI '/download/122.dll' on the 100% rare IP, 146.70.145[.]189. The request returned an executable file, which open-source intelligence (OSINT) suggests is likely a CobaltStrike C2 sever payload [2] [3]. Had Autonomous Response been enabled here, it would have blocked all outgoing traffic from the DC allowing the customer to investigate and remediate.

Additionally, Darktrace detected a suspicious CreateServiceW request to the Service Control (SVCCTL) RPC interface on a server. The request executed commands using ‘cmd.exe’ to perform the following actions

Used ‘tasklist’ to filter processes named ”lsass.exe” (Local Security Authority Subsystem Service) to find its specific process ID.
Used “rundll32.exe” to execute the MiniDump function from the “comsvcs.dll” library, creating a memory dump of the “lsass.exe” process.
Saved the output to a PNG file in a temporary folder,

Notably, “cmd.exe” was referenced as “CMd.EXE” within the script, likely an attempt to evade detection by security tools monitoring for specific keywords and patterns.

Figure 3: Model Alert Log showing the unusual SVCCTL create request.

Over the course of three days, this activity triggered around 125 Darktrace / NETWORK alerts across 11 internal devices. In addition, Cyber AI Analyst launched an autonomous investigation into the activity, analyzing and connecting 16 separate events spanning multiple stages of the cyber kill chain - from initial reconnaissance to payload retrieval and lateral movement.

Darktrace’s comprehensive detection enabled the customer’s security team to remediate the compromise before any further escalation was observed.

Case B

Between late 2023 and early 2024, Darktrace identified a widespread attack that combined insider and external threats, leveraging multiple LOTL tools for reconnaissance and lateral movement within a customer's network.

Reconnaissance

Initially, Darktrace detected the use of a new administrative credential by a device, which then made unusual RDP connections to multiple internal systems, including a 30-minute connection to a DC. Throughout the attack, multiple unusual RDP connections using the new administrative credential “%admin!!!” were observed, indicating that this protocol was leveraged for lateral movement.

The next day, a Microsoft Defender Security Integration alert was triggered on the device due to suspicious Windows Local Security Authority Subsystem Service (LSASS) credential dump behavior. Since the LSASS process memory can store operating system and domain admin credentials, obtaining this sensitive information can greatly facilitate lateral movement within a network using legitimate tools such as PsExec or Windows Management Instrumentation (WMI) [4]. Security integrations with other security vendors like this one can provide insights into host-based processes, which are typically outside of Darktrace’s coverage. Darktrace’s anomaly detection and network activity monitoring help prioritize the investigation of these alerts.

Three days later, the attacker was observed logging into the DC and querying tickets for the Lightweight Directory Access Protocol (LDAP) service using the default credential “Administrator.” This activity, considered new by Darktrace, triggered an Autonomous Response action that blocked further connections on Kerberos port 88 to the DC. LDAP provides a central location to access and manage data about computers, servers, users, groups, and policies within a network. LDAP enumeration can provide valuable Active Directory (AD) object information to an attacker, which can be used to identify critical attack paths or accounts with high privileges.

Lateral movement

Following the incoming RDP connection, the DC began scanning activities, including RDP and Server Block Message (SMB) services, suggesting the attacker was using remote access for additional reconnaissance. Outgoing RDP connection attempts to over 100 internal devices were observed, with around 5% being successful, highlighting the importance of this protocol for the threat actor’s lateral movement.

Around the same time, the DC made WMI, PsExec, and service control connections to two other DCs, indicating further lateral movement using native administrative protocols and tools. These functions can be leveraged by attackers to query system information, run malicious code, and maintain persistent access to compromised devices while avoiding traditional security tool alarms. In this case, requested services included the IWbemServices (used to access WMI services) and IWbemFetchSmartEnum (used to retrieve a network-optimized enumerator interface) interfaces, with ExecQuery operations detected for the former. This method returns an enumerable collection of IWbemClassObject interface objects based on a query.

Additionally, unusual Windows Remote Management (WinRM) connections to another domain controller were observed. WinRM is a Microsoft protocol that allows systems to exchange and access management information over HTTP(S) across a network, such as running executables or modifying the registry and services.

Figure 4: Cyber AI Analyst Incident showing unusual WMI activity between the two DCs.

The DC was also detected writing the file “PSEXESVC.exe” to the “ADMIN$” share of another internal device over the SMB file transfer network protocol. This activity was flagged as highly unusual by Darktrace, as these two devices had not previously engaged in this type of SMB connectivity.

It is rare for an attacker to immediately find the information or systems they are after, making it likely they will need to move around the network before achieving their objectives. Tools such as PsExec enable attackers to do this while largely remaining under the radar. With PsExec, attackers who gain access to a single system can connect to and execute commands remotely on other internal systems, access sensitive information, and spread their attack further into the environment.

Figure 5. Model Alert Event Log showing the new write of the file “PSEXESVC.exe” by one of the compromised devices over an SMB connection initiated at an unusual time.

Darktrace further observed the DC connecting to the SVCCTL endpoint on a remote device and performing the CreateServiceW operation, which was flagged as highly unusual based on previous behavior patterns between the two devices. Additionally, new ChangeServiceConfigW operations were observed from another device.

Aside from IWbemServices requests seen on multiple devices, Darktrace also detected multiple internal devices connecting to the ITaskSchedulerService interface over DCE-RPC and performing new SchRpcRegisterTask operations, which register a task on the destination system. Attackers can exploit the task scheduler to facilitate the initial or recurring execution of malicious code by a trusted system process, often with elevated permissions. The creation of these tasks was considered new or highly unusual and triggered several anomalous ITaskScheduler activity alerts.

Conclusion

As pointed out by CISA, threat actors frequently exploit the lack of implemented controls on their target networks, as demonstrated in the incidents discussed here. In the first case, VPN access was granted to all domain users, providing the attacker with a point of entry. In the second case, there were no restrictions on the use of RDP within the targeted network segment, allowing the attackers to pivot from device to device.

Darktrace assists security teams in monitoring for unusual use of LOTL tools and protocols that can be leveraged by threat actors to achieve a wide range of objectives. Darktrace’s Self-Learning AI sifts through the network traffic noise generated by these trusted tools, which are essential to administrators and developers in their daily tasks, and highlights any anomalous and potentially unexpected use.

Credit to Alexandra Sentenac (Senior Cyber Analyst) and Ryan Traill (Analyst Content Lead)

References

[1] https://www.cisa.gov/sites/default/files/2024-02/Joint-Guidance-Identifying-and-Mitigating-LOTL_V3508c.pdf

[2] https://www.virustotal.com/gui/ip-address/146.70.145.189/community

[3] https://www.virustotal.com/gui/file/cc9a670b549d84084618267fdeea13f196e43ae5df0d88e2e18bf5aa91b97318

[4]https://www.microsoft.com/en-us/security/blog/2022/10/05/detecting-and-preventing-lsass-credential-dumping-attacks

MITRE Mapping

INITIAL ACCESS - External Remote Services

DISCOVERY - Remote System Discovery

DISCOVERY - Network Service Discovery

DISCOVERY - File and Directory Discovery

CREDENTIAL ACCESS – OS Credential Dumping: LSASS Memory

LATERAL MOVEMENT - Remote Services: Remote Desktop Protocol

LATERAL MOVEMENT - Remote Services: SMB/Windows Admin Shares

EXECUTION - System Services: Service Execution

PERSISTENCE - Scheduled Task

COMMAND AND CONTROL - Ingress Tool Transfer

Darktrace Model Detections

Case A

Device / Suspicious Network Scan Activity

Device / Network Scan

Device / ICMP Address Scan

Device / Reverse DNS Sweep

Device / Suspicious SMB Scanning Activity

Device / Possible SMB/NTLM Reconnaissance

Anomalous Connection / Unusual Admin SMB Session

Device / SMB Session Brute Force (Admin)

Device / Possible SMB/NTLM Brute Force

Device / SMB Lateral Movement

Device / Anomalous NTLM Brute Force

Anomalous Connection / SMB Enumeration

Device / SMB Session Brute Force (Non-Admin)

Device / Anomalous SMB Followed By Multiple Model Breaches

Anomalous Connection / Possible Share Enumeration Activity

Device / RDP Scan

Device / Anomalous RDP Followed By Multiple Model Breaches

Anomalous Connection / Unusual Admin RDP Session

Anomalous Connection / Active Remote Desktop Tunnel

Anomalous Connection / Anomalous DRSGetNCChanges Operation

Anomalous Connection / High Priority DRSGetNCChanges

Compliance / Default Credential Usage

User / New Admin Credentials on Client

User / New Admin Credentials on Server

Device / Large Number of Model Breaches from Critical Network Device

User / New Admin Credential Ticket Request

Compromise / Unusual SVCCTL Activity

Anomalous Connection / New or Uncommon Service Control

Anomalous File / Script from Rare External Location

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous File / EXE from Rare External Location

Anomalous File / Numeric File Download

Device / Initial Breach Chain Compromise

Device / Multiple Lateral Movement Model Breaches

Device / Large Number of Model Breaches

Compromise / Multiple Kill Chain Indicators

Case B

User / New Admin Credentials on Client

Compliance / Default Credential Usage

Anomalous Connection / SMB Enumeration

Device / Suspicious SMB Scanning Activity

Device / RDP Scan

Device / New or Uncommon WMI Activity

Device / Anomaly Indicators / New or Uncommon WMI Activity Indicator

Device / New or Unusual Remote Command Execution

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / Active Remote Desktop Tunnel

Compliance / SMB Drive Write

Anomalous Connection / Anomalous DRSGetNCChanges Operation

Device / Multiple Lateral Movement Model Breaches

Device / Anomalous ITaskScheduler Activity

Anomalous Connection / Unusual Admin RDP Session

Device / Large Number of Model Breaches from Critical Network Device

Compliance / Default Credential Usage

IOC - Type - Description/Probability

146.70.145[.]189 - IP Address - Likely C2 Infrastructure

Written by

Alexandra Sentenac

Cyber Analyst

Inside the SOC

Written by

Alexandra Sentenac

Cyber Analyst

Product Marketing Manager, Network Detection & Response

Watch the NIS2 Webinar

Blog

Network

November 12, 2025

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

What is vo1d APK malware?

Vo1d malware first appeared in the wild in September 2024 and has since evolved into one of the most widespread Android botnets ever observed. This large-scale Android malware primarily targets smart TVs and low-cost Android TV boxes. Initially, Vo1d was identified as a malicious backdoor capable of installing additional third-party software [1]. Its functionality soon expanded beyond the initial infection to include deploying further malicious payloads, running proxy services, and conducting ad fraud operations. By early 2025, it was estimated that Vo1d had infected 1.3 to 1.6 million devices worldwide [2].

From a technical perspective, Vo1d embeds components into system storage to enable itself to download and execute new modules at any time. External researchers further discovered that Vo1d uses Domain Generation Algorithms (DGAs) to create new command-and-control (C2) domains, ensuring that regardless of existing servers being taken down, the malware can quickly reconnect to new ones. Previous published analysis identified dozens of C2 domains and hundreds of DGA seeds, along with new downloader families. Over time, Vo1d has grown increasingly sophisticated with clear signs of stronger obfuscation and encryption methods designed to evade detection [2].

Darktrace’s coverage

Earlier this year, Darktrace observed a surge in Vo1d-related activity across customer environments, with the majority of affected customers based in South Africa. Devices that had been quietly operating as expected began exhibiting unusual network behavior, including excessive DNS lookups. Open-source intelligence (OSINT) has long highlighted South Africa as one of the countries most impacted by Vo1d infections [2].

What makes the recent activity particularly interesting is that the surge observed by Darktrace appears to be concentrated specifically in South African environments. This localized spike suggests that a significant number of devices may have been compromised, potentially due to vulnerable software, outdated firmware, or even preloaded malware. Regions with high prevalence of low-cost, often unpatched devices are especially susceptible, as these everyday consumer electronics can be quietly recruited into the botnet’s network. This specifically appears to be the case with South Africa, where public reporting has documented widespread use of low-cost boxes, such as non-Google-certified Android TV sticks, that frequently ship with outdated firmware [3].

The initial triage highlighted the core mechanism Vo1d uses to remain resilient: its use of DGA. A DGA deterministically creates a large list of pseudo-random domain names on a predictable schedule. This enables the malware to compute hundreds of candidate domains using the same algorithm, instead of using a hard-coded single C2 hostname that defenders could easily block or take down. To ensure reproducible from the infected device’s perspective, Vo1d utilizes DGA seeds. These seeds might be a static string, a numeric value, or a combination of underlying techniques that enable infected devices to generate the same list of candidate domains for a time window, provided the same DGA code, seed, and date are used.

Interestingly, Vo1d’s DGA seeds do not appear to be entirely unpredictable, and the generated domains lack fully random-looking endings. As observed in Figure 1, there is a clear pattern in the names generated. In this case, researchers identified that while the first five characters would change to create the desired list of domain names, the trailing portion remained consistent as part of the seed: 60b33d7929a, which OSINT sources have linked to the Vo1d botnet. [2]. Darktrace’s Threat Research team also identified a potential second DGA seed, with devices in some cases also engaging in activity involving hostnames matching the regular expression /[a-z]{5}fc975904fc9\.(com|top|net). This second seed has not been reported by any OSINT vendors at the time of writing.

Another recurring characteristic observed across multiple cases was the choice of top-level domains (TLDs), which included .com, .net, and .top.

Figure 1: Advanced Search results showing DNS lookups, providing a glimpse on the DGA seed utilized.

The activity was detected by multiple models in Darktrace / NETWORK, which triggered on devices making an unusually large volume of DNS requests for domains uncommon across the network.

During the network investigation, Darktrace analysts traced Vo1d’s infrastructure and uncovered an interesting pattern related to responder ASNs. A significant number of connections pointed to AS16509 (AMAZON-02). By hosting redirectors or C2 nodes inside major cloud environments, Vo1d is able to gain access to highly available and geographically diverse infrastructure. When one node is taken down or reported, operators can quickly enable a new node under a different IP within the same ASN. Another feature of cloud infrastructure that hardens Vo1d’s resilience is the fact that many organizations allow outbound connections to cloud IP ranges by default, assuming they are legitimate. Despite this, Darktrace was able to identify the rarity of these endpoints, identifying the unusualness of the activity.

Analysts further observed that once a generated domain successfully resolved, infected devices consistently began establishing outbound connections to ephemeral port ranges like TCP ports 55520 and 55521. These destination ports are atypical for standard web or DNS traffic. Even though the choice of high-numbered ports appears random, it is likely far from not accidental. Commonly used ports such as port 80 (HTTP) or 443 (HTTPS) are often subject to more scrutiny and deeper inspection or content filtering, making them riskier for attackers. On the other hand, unregistered ports like 55520 and 55521 are less likely to be blocked, providing a more covert channel that blends with outbound TCP traffic. This tactic helps evade firewall rules that focus on common service ports. Regardless, Darktrace was able to identify external connections on uncommon ports to locations that the network does not normally visit.

The continuation of the described activity was identified by Darktrace’s Cyber AI Analyst, which correlated individual events into a broader interconnected incident. It began with the multiple DNS requests for the algorithmically generated domains, followed by repeated connections to rare endpoints later confirmed as attacker-controlled infrastructure. Cyber AI Analyst’s investigation further enabled it to categorize the events as part of the “established foothold” phase of the attack.

Figure 2: Cyber AI Analyst incident illustrating the transition from DNS requests for DGA domains to connections with resolved attacker-controlled infrastructure.

Conclusion

The observations highlighted in this blog highlight the precision and scale of Vo1d’s operations, ranging from its DGA-generated domains to its covert use of high-numbered ports. The surge in affected South African environments illustrate how regions with many low-cost, often unpatched devices can become major hubs for botnet activity. This serves as a reminder that even everyday consumer electronics can play a role in cybercrime, emphasizing the need for vigilance and proactive security measures.

Credit to Christina Kreza (Cyber Analyst & Team Lead) and Eugene Chua (Principal Cyber Analyst & Team Lead)

Edited by Ryan Traill (Analyst Content Lead)

‍

Appendices

Darktrace Model Detections

Anomalous Connection / Devices Beaconing to New Rare IP
Anomalous Connection / Multiple Connections to New External TCP Port
Anomalous Connection / Multiple Failed Connections to Rare Endpoint
Compromise / DGA Beacon
Compromise / Domain Fluxing
Compromise / Fast Beaconing to DGA
Unusual Activity / Unusual External Activity

List of Indicators of Compromise (IoCs)

3.132.75[.]97 – IP address – Likely Vo1d C2 infrastructure
g[.]sxim[.]me – Hostname – Likely Vo1d C2 infrastructure
snakeers[.]com – Hostname – Likely Vo1d C2 infrastructure

Selected DGA IoCs

semhz60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
ggqrb60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
eusji60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
uacfc60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
qilqxfc975904fc9[.]top – Hostname – Possible Vo1d C2 DGA endpoint

MITRE ATT&CK Mapping

T1071.004 – Command and Control – DNS
T1568.002 – Command and Control – Domain Generation Algorithms
T1568.001 – Command and Control – Fast Flux DNS
T1571 – Command and Control – Non-Standard Port

[1] https://news.drweb.com/show/?lng=en&i=14900

[2] https://blog.xlab.qianxin.com/long-live-the-vo1d_botnet/

[3] https://mybroadband.co.za/news/broadcasting/596007-warning-for-south-africans-using-specific-types-of-tv-sticks.html

‍

About the author

Christina Kreza

Cyber Analyst

Blog

Network

November 6, 2025

Darktrace Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response

Darktrace: The only Customers’ Choice for NDR in 2025

In a year defined by rapid change across the threat landscape, recognition from those who use and rely on security technology every day means the most.

That’s why we’re proud to share that Darktrace has been named the only Customers’ Choice in the 2025 Gartner® Peer Insights™ Voice of the Customer for Network Detection and Response (NDR).

Out of 11 leading NDR vendors evaluated, Darktrace stood alone as the sole Customers’ Choice, a recognition that we feel reflects not just our innovation, but the trust and satisfaction of the customers who secure their networks with Darktrace every day.

What the Gartner® Peer Insights™ Voice of the Customer means

“Voice of the Customer” is a document that synthesizes Gartner Peer Insights reviews into insights for buyers of technology and services. This aggregated peer perspective, along with the individual detailed reviews, is complementary to Gartner expert research and can play a key role in your buying process. Peers are verified reviewers of a technology product or service, who not only rate the offering, but also provide valuable feedback to consider before making a purchase decision. Vendors placed in the upper-right “Customers’ Choice” quadrant of the “Voice of the Customer” have scores that meet or exceed the market average for both axes (User Interest and Adoption, and Overall Experience).It’s not just a rating. We feel it’s a reflection of genuine customer sentiment and success in the field.

In our view, Customers consistently highlight Darktrace’s ability to:

Detect and respond to unknown threats in real time
Deliver unmatched visibility across IT, OT, and cloud environments
Automate investigations and responses through AI-driven insights

We believe this recognition reinforces what our customers already know: that Darktrace helps them see, understand, and stop attacks others miss.

A rare double: recognized by customers and analysts alike

This distinction follows another major recogniton. Darktrace’s placement as a Leader in the Gartner® Magic Quadrant™ for Network Detection and Response earlier this year.

That makes Darktrace the only vendor to achieve both:

A Leader status in the Gartner Magic Quadrant for NDR, and
A Customers’ Choice in Gartner Peer Insights 2025

It’s a rare double that we feel reflects both industry leadership and customer trust, two perspectives that, together, define what great cybersecurity looks like.

A Customers’ Choice across the network and the inbox

To us, this recognition also builds on Darktrace’s momentum across multiple domains. Earlier this year, Darktrace was also named a Customers’ Choice for Email Security Platforms in the Gartner® Peer Insights™ report.

With more than 1,000 verified reviews across Network Detection and Response, Email Security Platforms, and Cyber Physical Systems (CPS), we at Darktrace are proud to be trusted across the full attack surface, from the inbox to the industrial network.

Thank you to our customers

We’re deeply grateful to every customer who shared their experience with Darktrace on Gartner Peer Insights. Your insights drive our innovation and continue to shape how we protect complex, dynamic environments across the world.

Discover why customers choose Darktrace for network and email security.

^{Gartner® Peer Insights™ content consists of the opinions of individual end users based on their own experiences, and should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.}

^{Gartner, Voice of the Customer for Network Detection and Response, By Peer Community Contributor, 30 October 2025}

About the author

Mikey Anderson

Product Marketing Manager, Network Detection & Response

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Check out this article by Darktrace: Defending Against Living-off-the-Land Attacks: Anomaly Detection in Action

Defending Against Living-off-the-Land Attacks: Anomaly Detection in Action

What is living-off-the-land?

Darktrace’s detection of LOTL attacks

Case A

Reconnaissance

Lateral movement

Tooling

Case B

Reconnaissance

Lateral movement

Conclusion

References

MITRE Mapping

Darktrace Model Detections

Case A

Case B

IOC - Type - Description/Probability

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

Darktrace Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response

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Unmasking Vo1d: Inside Darktrace’s Botnet Detection

Darktrace Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response

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Blog

Network

November 12, 2025

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

What is vo1d APK malware?

Darktrace’s coverage

Conclusion

Appendices

Darktrace Model Detections

List of Indicators of Compromise (IoCs)

Selected DGA IoCs

MITRE ATT&CK Mapping

Blog

Network

November 6, 2025

Darktrace Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response

Darktrace: The only Customers’ Choice for NDR in 2025

What the Gartner® Peer Insights™ Voice of the Customer means

A Customers’ Choice across the network and the inbox

Thank you to our customers