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
Keith Siepel
IT Manager, Hydrotech, Inc.
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25
Mar 2019
The following guest-authored blog post examines an advanced cyber-threat discovered by Darktrace on a customer’s network.
On the morning of Thursday, February 14, 2019 at 7:51 a.m. EST, a contract instructor turned on their computer as they had every morning that week to prepare for their upcoming class. They connected their laptop to our Wi-Fi network, just as they had each morning, and proceeded to check email.
Unfortunately, this morning was a little different, as while they were checking email, their computer was checking other things on our network:
18 seconds later...
Our Darktrace system notified us via email of a potential problem and by marking the device in yellow on their Threat Visualizer.
Notice that the time lapse from the initial scanning process until the offending system is marked as a problem is approximately 15 seconds.
Subsequently the Darktrace system upgraded the threat status to “red” and notified us a second time of a network scan in progress. Darktrace also marked the offending device with a dark red/orange color to indicate the severity of the threat. Note the time in the bottom left corner is exactly 11 seconds after the first warning.
By 7:53 a.m., the instructor’s laptop had attempted connections to every device on the network and prompted the following image on the Darktrace Threat Visualizer interface, leaving no doubt that there was a problem with the instructor’s laptop.
The offending laptop was removed from our network. We weren’t going to wait to see how long it might take for our other, signature-based systems to identify and remediate this problem.
Working with the instructor, I scanned the laptop with various tools in an attempt to identify and remove the malware that created this network scan. Unfortunately, we were not able to find malware running on this device using traditional tools. Subsequently, we determined that the source of this breach probably originated from a USB drive that was shared with the contract instructor by one of his students the night before.
Just as Darktrace detected a zero-day trojan on our network in January, I was able to identify and remove a threat from our network using Darktrace before I spent days, weeks, or even months waiting for our antivirus/malware signatures to hopefully identify it.
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.
Anomaly-based threat hunting: Darktrace's approach in action
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 relies on anomaly-based detection methods. 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) [1]. 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.
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)
Combatting the Top Three Sources of Risk in the Cloud
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.
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
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.
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.
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.
