Discover how Darktrace uses AI and Google Packet Mirroring to enhance cloud security. Learn about their innovative immune system approach.
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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.
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26
Jan 2021
Google Packet Mirroring + Darktrace/Cloud
With Darktrace’s Self-Learning AI cloud cyber security and the visibility provided by Google’s Packet Mirroring, Darktrace/Cloud brings autonomous, cloud-native threat detection, investigation, and response to your Google Cloud.
Google’s Packet Mirroring service enables Darktrace’s Cyber AI to seamlessly deploy in the cloud and immediately form an understanding of what normal activity looks like for every user, container, application, and workload in a customer’s Google Cloud environment. This bespoke, real-time knowledge of an organization’s ‘pattern of life’ allows Darktrace/Cloud to identify the subtle behavioral deviations that point to a threat.
Darktrace/Cloud delivers the only cloud cyber security solution that learns ‘on the job’, adapts as your business evolves, and autonomously responds to the full range of threats in the cloud. The ability to evolve with an organization and continuously update its understanding of ‘normal’ is a particularly critical feature given the speed and scale of development in the cloud.
With the power of Cyber AI and Google Packet Mirroring, organizations can benefit from bespoke, context-based defense against even the most advanced threats that may emerge – from misconfigurations to compromised credentials.
Leveraging Google Packet Mirroring for Self-Learning Cyber AI
Darktrace/Cloud leverages Google Packet Mirroring to monitor all traffic in a customer’s Google Cloud environment, with no need to deploy agents. This allows Darktrace/Cloud self-learning AI to analyze the entire packet, including headers and payload, and build rich behavioral models for activity in Google Cloud.
With this deep understanding of context, Darktrace/Cloud can detect and correlate all the weak indicators of a threat that policy-based tools miss – even if the threat is highly sophisticated or novel.
Every threat surfaced in Google Cloud is automatically investigated by Cyber AI Analyst which triages, interprets, and reports on the full scope of security incidents, reducing triage time by up to 92%.
Darktrace/Cloud Security Module for Google Cloud provides additional visibility, ensuring full awareness of administrative activity and system events in Cloud Audit Log-Compatible services, with additional support for Data Access Logs for deeper visibility into specific component activity. The Security Module allows for coverage of Darktrace’s workload-focused use cases, identifying threats like data exfiltration and critical misconfigurations.
Because user access to Google Cloud is authenticated via the Google Workspace platform, customers can gain visibility of logins and other user activity with Darktrace’s Google Workspace Module. This Module allows for coverage of Darktrace’s workforce-focused use cases, identifying threats like compromised credentials and insider threat.
Darktrace can deliver total coverage across all your Google Cloud services, including:
BigQuery
Cloud Compute
Cloud CDN
Cloud Run
Cloud SQL
Cloud Storage*
Cloud Translate
Key Management
Resource Manager
*Please note cloud storage files are no longer audited by Google if made explicitly public.
Unified, AI-native platform for defense across the enterprise
Taking a fundamentally unique approach, Darktrace/Cloud can correlate behavior in Google Cloud with activity from SaaS, email, remote endpoints, and any range of on- or off-premise infrastructure across a customer’s enterprise.
This is a crucial benefit, as businesses and workforces today are increasingly complex and dynamic. With Darktrace’s unified security platform, Cyber AI can connect the dots between unusual behavior in disparate infrastructure areas and ensure cloud security is not siloed from the monitoring of the rest of the organizations. And because the AI technology learns ‘on the job’, Darktrace/Cloud provides the flexibility and scalability needed to evolve at the pace of your business.
Augmenting security teams and enabling digital transformation with AI cloud security
Darktrace/Cloud provides the industry’s only self-learning platform that correlates information from across the organization and adapts in real time – improving productivity across the security team and letting you accelerate digital innovation in your Google Cloud environment, and beyond.
Cyber AI can analyze data at a speed and scale impossible for humans, and surfaces actionable insights right when your team needs them. With Darktrace/Cloud, security analysts and business leaders alike can focus more on thoughtful decision-making, while the AI works in the background to ensure the business and workforce are always protected.
Key threat detection use cases for Google Cloud environments include:
Data exfiltration and destruction: Detects anomalous device connections, and unusual resource deletion, modification, and movement
Critical misconfigurations: Catches unusual permission changes, and anomalous activity around compliance-related data or devices
Compromised credentials: Spots brute force attempts, unusual login source or time, and unusual user behavior including rule changes or password resets
Insider threat and admin abuse: Identifies the subtle signs of malicious insiders – including sensitive resource access, role changes, or adding/deleting users
Darktrace customers can learn more about leveraging Google Packet Mirroring on the Customer Portal
No items found.
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.
Rethinking Signature-Based Detection for Power Utility Cybersecurity
Lessons learned from OT cyber attacks
Over the past decade, some of the most disruptive attacks on power utilities have shown the limits of signature-based detection and reshaped how defenders think about OT security. Each incident reinforced that signatures are too narrow and reactive to serve as the foundation of defense.
2015: BlackEnergy 3 in Ukraine
According to CISA, on December 23, 2015, Ukrainian power companies experienced unscheduled power outages affecting a large number of customers — public reports indicate that the BlackEnergy malware was discovered on the companies’ computer networks.
2016: Industroyer/CrashOverride
CISA describes CrashOverride malwareas an “extensible platform” reported to have been used against critical infrastructure in Ukraine in 2016. It was capable of targeting industrial control systems using protocols such as IEC‑101, IEC‑104, and IEC‑61850, and fundamentally abused legitimate control system functionality to deliver destructive effects. CISA emphasizes that “traditional methods of detection may not be sufficient to detect infections prior to the malware execution” and recommends behavioral analysis techniques to identify precursor activity to CrashOverride.
2017: TRITON Malware
The U.S. Department of the Treasury reports that the Triton malware, also known as TRISIS or HatMan, was “designed specifically to target and manipulate industrial safety systems” in a petrochemical facility in the Middle East. The malware was engineered to control Safety Instrumented System (SIS) controllers responsible for emergency shutdown procedures. During the attack, several SIS controllers entered a failed‑safe state, which prevented the malware from fully executing.
The broader lessons
These events revealed three enduring truths:
Signatures have diminishing returns: BlackEnergy showed that while signatures can eventually identify adapted IT malware, they arrive too late to prevent OT disruption.
Behavioral monitoring is essential: CrashOverride demonstrated that adversaries abuse legitimate industrial protocols, making behavioral and anomaly detection more effective than traditional signature methods.
Critical safety systems are now targets: TRITON revealed that attackers are willing to compromise safety instrumented systems, elevating risks from operational disruption to potential physical harm.
The natural progression for utilities is clear. Static, file-based defenses are too fragile for the realities of OT.
These incidents showed that behavioral analytics and anomaly detection are far more effective at identifying suspicious activity across industrial systems, regardless of whether the malicious code has ever been seen before.
Strategic risks of overreliance on signatures
False sense of security: Believing signatures will block advanced threats can delay investment in more effective detection methods.
Resource drain: Constantly updating, tuning, and maintaining signature libraries consumes valuable staff resources without proportional benefit.
Adversary advantage: Nation-state and advanced actors understand the reactive nature of signature defenses and design attacks to circumvent them from the start.
Recommended Alternatives (with real-world OT examples)
Figure 1: Alternative strategies for detecting cyber attacks in OT
Behavioral and anomaly detection
Rather than relying on signatures, focusing on behavior enables detection of threats that have never been seen before—even trusted-looking devices.
Real-world insight:
In one OT setting, a vendor inadvertently left a Raspberry Pi on a customer’s ICS network. After deployment, Darktrace’s system flagged elastic anomalies in its HTTPS and DNS communication despite the absence of any known indicators of compromise. The alerting included sustained SSL increases, agent‑beacon activity, and DNS connections to unusual endpoints, revealing a possible supply‑chain or insider risk invisible to static tools.
Darktrace’s AI-driven threat detection aligns with the zero-trust principle of assuming the risk of a breach. By leveraging AI that learns an organization’s specific patterns of life, Darktrace provides a tailored security approach ideal for organizations with complex supply chains.
Threat intelligence sharing & building toward zero-trust philosophy
Frameworks such as MITRE ATT&CK for ICS provide a common language to map activity against known adversary tactics, helping teams prioritize detections and response strategies. Similarly, information-sharing communities like E-ISAC and regional ISACs give utilities visibility into the latest tactics, techniques, and procedures (TTPs) observed across the sector. This level of intel can help shift the focus away from chasing individual signatures and toward building resilience against how adversaries actually operate.
Real-world insight:
Darktrace’s AI embodies zero‑trust by assuming breach potential and continually evaluating all device behavior, even those deemed trusted. This approach allowed the detection of an anomalous SharePoint phishing attempt coming from a trusted supplier, intercepted by spotting subtle patterns rather than predefined rules. If a cloud account is compromised, unauthorized access to sensitive information could lead to extortion and lateral movement into mission-critical systems for more damaging attacks on critical-national infrastructure.
This reinforces the need to monitor behavioral deviations across the supply chain, not just known bad artifacts.
Defense-in-Depth with OT context & unified visibility
OT environments demand visibility that spans IT, OT, and IoT layers, supported by risk-based prioritization.
Moreover, by integrating contextual risk scoring, considering real-world exploitability, device criticality, firewall misconfiguration, and legacy hardware exposure, utilities can focus on the vulnerabilities that genuinely threaten uptime and safety, rather than being overwhelmed by CVE noise.
Regulatory alignment and positive direction
Industry regulations are beginning to reflect this evolution in strategy. NERC CIP-015 requires internal network monitoring that detects anomalies, and the standard references anomalies 15 times. In contrast, signature-based detection is not mentioned once.
This regulatory direction shows that compliance bodies understand the limitations of static defenses and are encouraging utilities to invest in anomaly-based monitoring and analytics. Utilities that adopt these approaches will not only be strengthening their resilience but also positioning themselves for regulatory compliance and operational success.
Conclusion
Signature-based detection retains utility for common IT malware, but it cannot serve as the backbone of security for power utilities. History has shown that major OT attacks are rarely stopped by signatures, since each campaign targets specific systems with customized tools. The most dangerous adversaries, from insiders to nation-states, actively design their operations to avoid detection by signature-based tools.
A more effective strategy prioritizes behavioral analytics, anomaly detection, and community-driven intelligence sharing. These approaches not only catch known threats, but also uncover the subtle anomalies and novel attack techniques that characterize tomorrow’s incidents.
From VPS to Phishing: How Darktrace Uncovered SaaS Hijacks through Virtual Infrastructure Abuse
What is a VPS and how are they abused?
A Virtual Private Server (VPS) is a virtualized server that provides dedicated resources and control to users on a shared physical device. VPS providers, long used by developers and businesses, are increasingly misused by threat actors to launch stealthy, scalable attacks. While not a novel tactic, VPS abuse is has seen an increase in Software-as-a-Service (SaaS)-targeted campaigns as it enables attackers to bypass geolocation-based defenses by mimicking local traffic, evade IP reputation checks with clean, newly provisioned infrastructure, and blend into legitimate behavior [3].
VPS providers like Hyonix and Host Universal offer rapid setup and minimal open-source intelligence (OSINT) footprint, making detection difficult [1][2]. These services are not only fast to deploy but also affordable, making them attractive to attackers seeking anonymous, low-cost infrastructure for scalable campaigns. Such attacks tend to be targeted and persistent, often timed to coincide with legitimate user activity, a tactic that renders traditional security tools largely ineffective.
Darktrace’s investigation into Hyonix VPS abuse
In May 2025, Darktrace’s Threat Research team investigated a series of incidents across its customer base involving VPS-associated infrastructure. The investigation began with a fleet-wide review of alerts linked to Hyonix (ASN AS931), revealing a noticeable spike in anomalous behavior from this ASN in March 2025. The alerts included brute-force attempts, anomalous logins, and phishing campaign-related inbox rule creation.
Darktrace identified suspicious activity across multiple customer environments around this time, but two networks stood out. In one instance, two internal devices exhibited mirrored patterns of compromise, including logins from rare endpoints, manipulation of inbox rules, and the deletion of emails likely used in phishing attacks. Darktrace traced the activity back to IP addresses associated with Hyonix, suggesting a deliberate use of VPS infrastructure to facilitate the attack.
On the second customer network, the attack was marked by coordinated logins from rare IPs linked to multiple VPS providers, including Hyonix. This was followed by the creation of inbox rules with obfuscated names and attempts to modify account recovery settings, indicating a broader campaign that leveraged shared infrastructure and techniques.
Darktrace’s Autonomous Response capability was not enabled in either customer environment during these attacks. As a result, no automated containment actions were triggered, allowing the attack to escalate without interruption. Had Autonomous Response been active, Darktrace would have automatically blocked connections from the unusual VPS endpoints upon detection, effectively halting the compromise in its early stages.
Case 1
Figure 1: Timeline of activity for Case 1 - Unusual VPS logins and deletion of phishing emails.
Initial Intrusion
On May 19, 2025, Darktrace observed two internal devices on one customer environment initiating logins from rare external IPs associated with VPS providers, namely Hyonix and Host Universal (via Proton VPN). Darktrace recognized that these logins had occurred within minutes of legitimate user activity from distant geolocations, indicating improbable travel and reinforcing the likelihood of session hijacking. This triggered Darktrace / IDENTITY model “Login From Rare Endpoint While User Is Active”, which highlights potential credential misuse when simultaneous logins occur from both familiar and rare sources.
Shortly after these logins, Darktrace observed the threat actor deleting emails referring to invoice documents from the user’s “Sent Items” folder, suggesting an attempt to hide phishing emails that had been sent from the now-compromised account. Though not directly observed, initial access in this case was likely achieved through a similar phishing or account hijacking method.
Figure 2: Darktrace / IDENTITY model "Login From Rare Endpoint While User Is Active", which detects simultaneous logins from both a common and a rare source to highlight potential credential misuse.
Case 2
Figure 3: Timeline of activity for Case 2 – Coordinated inbox rule creation and outbound phishing campaign.
In the second customer environment, Darktrace observed similar login activity originating from Hyonix, as well as other VPS providers like Mevspace and Hivelocity. Multiple users logged in from rare endpoints, with Multi-Factor Authentication (MFA) satisfied via token claims, further indicating session hijacking.
Establishing control and maintaining persistence
Following the initial access, Darktrace observed a series of suspicious SaaS activities, including the creation of new email rules. These rules were given minimal or obfuscated names, a tactic often used by attackers to avoid drawing attention during casual mailbox reviews by the SaaS account owner or automated audits. By keeping rule names vague or generic, attackers reduce the likelihood of detection while quietly redirecting or deleting incoming emails to maintain access and conceal their activity.
One of the newly created inbox rules targeted emails with subject lines referencing a document shared by a VIP at the customer’s organization. These emails would be automatically deleted, suggesting an attempt to conceal malicious mailbox activity from legitimate users.
Mirrored activity across environments
While no direct lateral movement was observed, mirrored activity across multiple user devices suggested a coordinated campaign. Notably, three users had near identical similar inbox rules created, while another user had a different rule related to fake invoices, reinforcing the likelihood of a shared infrastructure and technique set.
Privilege escalation and broader impact
On one account, Darktrace observed “User registered security info” activity was shortly after anomalous logins, indicating attempts to modify account recovery settings. On another, the user reset passwords or updated security information from rare external IPs. In both cases, the attacker’s actions—including creating inbox rules, deleting emails, and maintaining login persistence—suggested an intent to remain undetected while potentially setting the stage for data exfiltration or spam distribution.
On a separate account, outbound spam was observed, featuring generic finance-related subject lines such as 'INV#. EMITTANCE-1'. At the network level, Darktrace / NETWORK detected DNS requests from a device to a suspicious domain, which began prior the observed email compromise. The domain showed signs of domain fluxing, a tactic involving frequent changes in IP resolution, commonly used by threat actors to maintain resilient infrastructure and evade static blocklists. Around the same time, Darktrace detected another device writing a file named 'SplashtopStreamer.exe', associated with the remote access tool Splashtop, to a domain controller. While typically used in IT support scenarios, its presence here may suggest that the attacker leveraged it to establish persistent remote access or facilitate lateral movement within the customer’s network.
Conclusion
This investigation highlights the growing abuse of VPS infrastructure in SaaS compromise campaigns. Threat actors are increasingly leveraging these affordable and anonymous hosting services to hijack accounts, launch phishing attacks, and manipulate mailbox configurations, often bypassing traditional security controls.
Despite the stealthy nature of this campaign, Darktrace detected the malicious activity early in the kill chain through its Self-Learning AI. By continuously learning what is normal for each user and device, Darktrace surfaced subtle anomalies, such as rare login sources, inbox rule manipulation, and concurrent session activity, that likely evade traditional static, rule-based systems.
As attackers continue to exploit trusted infrastructure and mimic legitimate user behavior, organizations should adopt behavioral-based detection and response strategies. Proactively monitoring for indicators such as improbable travel, unusual login sources, and mailbox rule changes, and responding swiftly with autonomous actions, is critical to staying ahead of evolving threats.
Credit to Rajendra Rushanth (Cyber Analyst), Jen Beckett (Cyber Analyst) and Ryan Traill (Analyst Content Lead)
• SaaS / Access / Unusual External Source for SaaS Credential Use
• SaaS / Compromise / High Priority Login From Rare Endpoint
• SaaS / Compromise / Login From Rare Endpoint While User Is Active
List of Indicators of Compromise (IoCs)
Format: IoC – Type – Description
• 38.240.42[.]160 – IP – Associated with Hyonix ASN (AS931)
• 103.75.11[.]134 – IP – Associated with Host Universal / Proton VPN
• 162.241.121[.]156 – IP – Rare IP associated with phishing
• 194.49.68[.]244 – IP – Associated with Hyonix ASN
• 193.32.248[.]242 – IP – Used in suspicious login activity / Mullvad VPN
• 50.229.155[.]2 – IP – Rare login IP / AS 7922 ( COMCAST-7922 )
• 104.168.194[.]248 – IP – Rare login IP / AS 54290 ( HOSTWINDS )
• 38.255.57[.]212 – IP – Hyonix IP used during MFA activity
• 103.131.131[.]44 – IP – Hyonix IP used in login and MFA activity
• 178.173.244[.]27 – IP – Hyonix IP
• 91.223.3[.]147 – IP – Mevspace Poland, used in multiple logins
• 2a02:748:4000:18:0:1:170b[:]2524 – IPv6 – Hivelocity VPS, used in multiple logins and MFA activity
• 51.36.233[.]224 – IP – Saudi ASN, used in suspicious login
• 103.211.53[.]84 – IP – Excitel Broadband India, used in security info update
MITRE ATT&CK Mapping
Tactic – Technique – Sub-Technique
• Initial Access – T1566 – Phishing
T1566.001 – Spearphishing Attachment
• Execution – T1078 – Valid Accounts
• Persistence – T1098 – Account Manipulation
T1098.002 – Exchange Email Rules
• Command and Control – T1071 – Application Layer Protocol
T1071.001 – Web Protocols
• Defense Evasion – T1036 – Masquerading
• Defense Evasion – T1562 – Impair Defenses
T1562.001 – Disable or Modify Tools
• Credential Access – T1556 – Modify Authentication Process
T1556.004 – MFA Bypass
• Discovery – T1087 – Account Discovery
• Impact – T1531 – Account Access Removal
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