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November 17, 2019

An Education In Detecting Ransomware Without Any Signatures

Learn how to detect ransomware without any malware signatures. See how Darktrace is one of the leading fighters against ransomware and other cyber risks.
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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17
Nov 2019

Across Darktrace’s global customer base, ransomware is rapidly on the rise. And unlike the indiscriminate ransomware worms — like WannaCry and BadRabbit — that we’ve discussed in the past, the trend of today’s attacks is toward selective “big game hunting.” The Ryuk ransomware incident I blogged about last month demonstrates how criminals now seek to exploit the particular vulnerabilities of their strategic targets.

Despite the increasing sophistication of these attacks, however, detecting them is ultimately just a classification problem — albeit a highly complex and consequential one. To understand what makes this problem difficult, consider three ways of identifying ransomware. The first and most common way is to cross-reference new activity with the digital ‘signatures’ of known malware strains, catching attacks that the security community has already catalogued. Of course, such fixed signatures are blind to the novel malware variants that dominate the modern threat landscape.

The second level uses supervised machine learning, which entails training an AI on lots of historical examples of ransomware attacks in an attempt to find their commonalities. While this approach can, in theory, detect ransomware that isn’t identical to training data, the supervised learning approach is essentially just signatures on steroids, failing to flag malicious behavior that is fundamentally unlike anything seen before. Rather, addressing the ransomware epidemic once and for all requires unsupervised machine learning. By understanding how each particular employee and device functions while ‘on the job’ — without any signatures or training data — Cyber AI does just that.

An education in ransomware

When a world-leading education institution was hit with a strain of the Dharma ransomware family this past October, Darktrace Cyber AI immediately alerted on the attack using this learnt knowledge of the institution itself — rather than with signatures. The following timeline details each phase of the incident:

Figure 1: An overview of the attack.

In summary, the threat-actors brute-forced their way into the institution’s network by exploiting a server that lacked protection against such RDP brute-forcing — compromising an admin’s credentials. They then proceeded to scan the network until they located an open port 445, whereupon they moved laterally using the PsExec tool that allows for remote administration. The initially compromised server copied the ransomware, named “system.exe,” to hidden SMB shares on the other machines via the SMB protocol. Finally, that ransomware began encrypting data on all of these devices.

Cyber AI traced every step of the above attack by contrasting it with the institution’s normal online behavior. The graph below shows the infected server’s activity throughout the entire incident.

Figure 2: Every colored dot represents a high-confidence Darktrace alert indicating significantly anomalous activity.

Beyond just detecting the attack, however, Darktrace’s AI Autonomous Response tool, Antigena, would have taken targeted action to neutralize it within seconds. When hit with machine-speed threats like ransomware, human security teams need such AI tools to contain the damage, as Antigena would have done:

An alternate reality with Autonomous Response

The attack would have gone quite differently had it been met with Autonomous Response. To start with, Antigena would have blocked the threat-actor’s repeated login attempts over RDP, since these attempts originated from external IP addresses that had never communicated with the organization before. Antigena works by enforcing the normal ‘pattern of life’ for each impacted user and device, meaning that it would not have blocked IP addresses that regularly communicate with the RDP server. This ensures that activity necessary to daily operations isn’t interrupted during even serious threats.

Figure 3: Darktrace alerts on one of the multiple unusual IP addresses that attempted brute-forcing.

By this point, the threat would already have been neutralized by the blocked brute-forcing. But had the attackers somehow still managed to scan the network for open SMB services, Antigena would have intervened once again to surgically restrict that behavior, as Darktrace recognized that the infected server almost never scanned the internal network.

Figure 4: Darktrace alerts on the anomalous scanning behavior, which Antigena would have autonomously blocked.

Continuing on with the hypothetical, though, the server now employs PsExec to move laterally to other devices — activity that Darktrace identified as anomalous immediately. Antigena would have escalated its response at this point, stopping all outbound connections from the server for several hours. Ultimately, Autonomous Response would have completely disarmed the threat, as it has successfully demonstrated on millions of occasions already.

Uncovering the Unpredictable

It has never been easier for threat-actors to devise novel ransomware strains and to gain access to new command & control domains. Using fixed signatures, IP blacklists, and predefined assumptions is therefore insufficient, since no security tool can predict the next fundamentally unpredictable attack. Only Cyber AI — which learns what’s normal for each unique user and device it defends — is equipped for such a challenge.

Of course, detection alone won’t cut it. Modern ransomware is increasingly automated; in this particular case, the entire incident took less than two hours, from the initial brute-forcing to the concluding encryption. And although Darktrace alerted on the threat in real time, the security team was occupied with other tasks, leading to a compromise. That’s where Autonomous Response has become business-critical across every industry — it’s on guard 24/7, even when the security team can’t be.

To learn more about how Autonomous Response neutralizes ransomware without relying on signatures, check out our white paper: The Evolution of Autonomous Response: Fighting Back in a New Era of Cyber-Threat.

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 7, 2025

Anomaly-based threat hunting: Darktrace's approach in action

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What is threat hunting?

Threat hunting in cybersecurity involves proactively and iteratively searching through networks and datasets to detect threats that evade existing automated security solutions. It is an important component of a strong cybersecurity posture.

There are several frameworks that Darktrace analysts use to guide how threat hunting is carried out, some of which are:

  • MITRE Attack
  • Tactics, Techniques, Procedures (TTPs)
  • Diamond Model for Intrusion Analysis
  • Adversary, Infrastructure, Victims, Capabilities
  • Threat Hunt Model – Six Steps
  • Purpose, Scope, Equip, Plan, Execute, Feedback
  • Pyramid of Pain

These frameworks are important in baselining how to run a threat hunt. There are also a combination of different methods that allow defenders diversity– regardless of whether it is a proactive or reactive threat hunt. Some of these are:

  • Hypothesis-based threat hunting
  • Analytics-driven threat hunting
  • Automated/machine learning hunting
  • Indicator of Compromise (IoC) hunting
  • Victim-based threat hunting

Threat hunting with Darktrace

At its core, Darktrace is an anomaly-based detection tool. It combines various machine learning types that allows it to characterize what constitutes ‘normal’, based on the analysis of many different measures of a device or actor’s behavior. Those types of learning are then curated into what are called models.

Darktrace models leverage anomaly detection and integrate outputs from Darktrace Deep Packet Inspection, telemetry inputs, and additional modules, creating tailored activity detection.

This dynamic understanding allows Darktrace to identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign.  On top of machine learning models for detection, there is also the ability to change and create models showcasing the tool’s diversity. The Model Editor allows security teams to specify values, priorities, thresholds, and actions they want to detect. That means a team can create custom detection models based on specific use cases or business requirements. Teams can also increase the priority of existing detections based on their own risk assessments to their environment.

This level of dexterity is particularly useful when conducting a threat hunt. As described above, and in previous ‘Inside the SOC’ blogs such a threat hunt can be on a specific threat actor, specific sector, or a  hypothesis-based threat hunt combined with ‘experimenting’ with some of Darktrace’s models.

Conducting a threat hunt in the energy sector with experimental models

In Darktrace’s recent Threat Research report “AI & Cybersecurity: The state of cyber in UK and US energy sectors” Darktrace’s Threat Research team crafted hypothesis-driven threat hunts, building experimental models and investigating existing models to test them and detect malicious activity across Darktrace customers in the energy sector.

For one of the hunts, which hypothesised utilization of PerfectData software and multi-factor authentication (MFA) bypass to compromise user accounts and destruct data, an experimental model was created to detect a Software-as-a-Service (SaaS) user performing activity relating to 'PerfectData Software’, known to allow a threat actor to exfiltrate whole mailboxes as a PST file. Experimental model alerts caused by this anomalous activity were analyzed, in conjunction with existing SaaS and email-related models that would indicate a multi-stage attack in line with the hypothesis.

Whilst hunting, Darktrace researchers found multiple model alerts for this experimental model associated with PerfectData software usage, within energy sector customers, including an oil and gas investment company, as well as other sectors. Upon further investigation, it was also found that in June 2024, a malicious actor had targeted a renewable energy infrastructure provider via a PerfectData Software attack and demonstrated intent to conduct an Operational Technology (OT) attack.

The actor  logged into Azure AD from a rare US IP address. They then granted Consent to ‘eM Client’ from the same IP. Shortly after, the actor granted ‘AddServicePrincipal’ via Azure  to PerfectData Software. Two days later, the actor created a  new email rule from a London IP to move emails to an RSS Feed Folder, stop processing rules, and mark emails as read. They then accessed mail items in the “\Sent” folder from a malicious IP belonging to anonymization network,  Private Internet Access Virtual Private Network (PIA VPN). The actor then conducted mass email deletions , deleting multiple instances of emails with subject “[Name] shared "[Company Name] Proposal" With You” from the  “\Sent folder”. The emails’ subject suggests the email likely contains a link to file storage for phishing purposes. The mass deletion likely represented an attempt to obfuscate a potential outbound phishing email campaign.

The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.
Figure 1: The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.

A month later, the same user was observed downloading mass mLog CSV files related to proprietary and Operational Technology information. In September, three months after the initial attack, another mass download of operational files occurred by this actor, pertaining to operating instructions and measurements, The observed patience and specific file downloads seemingly demonstrated an intent to conduct or research possible OT attack vectors. An attack on OT could have significant impacts including operational downtime, reputational damage, and harm to everyday operations. Darktrace alerted the impacted customer once findings were verified, and subsequent actions were taken by the internal security team to prevent further malicious activity.

Conclusion

Harnessing the power of different tools in a security stack is a key element to cyber defense. The above hypothesis-based threat hunt and custom demonstrated intent to conduct an experimental model creation demonstrates different threat hunting approaches, how Darktrace’s approach can be operationalized, and that proactive threat hunting can be a valuable complement to traditional security controls and is essential for organizations facing increasingly complex threat landscapes.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO at Darktrace) and Zoe Tilsiter (EMEA Consultancy Lead)

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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May 6, 2025

Combatting the Top Three Sources of Risk in the Cloud

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With cloud computing, organizations are storing data like intellectual property, trade secrets, Personally Identifiable Information (PII), proprietary code and statistics, and other sensitive information in the cloud. If this data were to be accessed by malicious actors, it could incur financial loss, reputational damage, legal liabilities, and business disruption.

Last year data breaches in solely public cloud deployments were the most expensive type of data breach, with an average of $5.17 million USD, a 13.1% increase from the year before.

So, as cloud usage continues to grow, the teams in charge of protecting these deployments must understand the associated cybersecurity risks.

What are cloud risks?

Cloud threats come in many forms, with one of the key types consisting of cloud risks. These arise from challenges in implementing and maintaining cloud infrastructure, which can expose the organization to potential damage, loss, and attacks.

There are three major types of cloud risks:

1. Misconfigurations

As organizations struggle with complex cloud environments, misconfiguration is one of the leading causes of cloud security incidents. These risks occur when cloud settings leave gaps between cloud security solutions and expose data and services to unauthorized access. If discovered by a threat actor, a misconfiguration can be exploited to allow infiltration, lateral movement, escalation, and damage.

With the scale and dynamism of cloud infrastructure and the complexity of hybrid and multi-cloud deployments, security teams face a major challenge in exerting the required visibility and control to identify misconfigurations before they are exploited.

Common causes of misconfiguration come from skill shortages, outdated practices, and manual workflows. For example, potential misconfigurations can occur around firewall zones, isolated file systems, and mount systems, which all require specialized skill to set up and diligent monitoring to maintain

2. Identity and Access Management (IAM) failures

IAM has only increased in importance with the rise of cloud computing and remote working. It allows security teams to control which users can and cannot access sensitive data, applications, and other resources.

Cybersecurity professionals ranked IAM skills as the second most important security skill to have, just behind general cloud and application security.

There are four parts to IAM: authentication, authorization, administration, and auditing and reporting. Within these, there are a lot of subcomponents as well, including but not limited to Single Sign-On (SSO), Two-Factor Authentication (2FA), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

Security teams are faced with the challenge of allowing enough access for employees, contractors, vendors, and partners to complete their jobs while restricting enough to maintain security. They may struggle to track what users are doing across the cloud, apps, and on-premises servers.

When IAM is misconfigured, it increases the attack surface and can leave accounts with access to resources they do not need to perform their intended roles. This type of risk creates the possibility for threat actors or compromised accounts to gain access to sensitive company data and escalate privileges in cloud environments. It can also allow malicious insiders and users who accidentally violate data protection regulations to cause greater damage.

3. Cross-domain threats

The complexity of hybrid and cloud environments can be exploited by attacks that cross multiple domains, such as traditional network environments, identity systems, SaaS platforms, and cloud environments. These attacks are difficult to detect and mitigate, especially when a security posture is siloed or fragmented.  

Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.  

Challenges in securing against cross-domain threats often come from a lack of unified visibility. If a security team does not have unified visibility across the organization’s domains, gaps between various infrastructures and the teams that manage them can leave organizations vulnerable.

Adopting AI cybersecurity tools to reduce cloud risk

For security teams to defend against misconfigurations, IAM failures, and insecure APIs, they require a combination of enhanced visibility into cloud assets and architectures, better automation, and more advanced analytics. These capabilities can be achieved with AI-powered cybersecurity tools.

Such tools use AI and automation to help teams maintain a clear view of all their assets and activities and consistently enforce security policies.

Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that makes cloud security accessible to all security teams and SOCs by using AI to identify and correct misconfigurations and other cloud risks in public, hybrid, and multi-cloud environments.

It provides real-time, dynamic architectural modeling, which gives SecOps and DevOps teams a unified view of cloud infrastructures to enhance collaboration and reveal possible misconfigurations and other cloud risks. It continuously evaluates architecture changes and monitors real-time activity, providing audit-ready traceability and proactive risk management.

Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.
Figure 1: Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.

Darktrace / CLOUD also offers attack path modeling for the cloud. It can identify exposed assets and highlight internal attack paths to get a dynamic view of the riskiest paths across cloud environments, network environments, and between – enabling security teams to prioritize based on unique business risk and address gaps to prevent future attacks.  

Darktrace’s Self-Learning AI ensures continuous cloud resilience, helping teams move from reactive to proactive defense.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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