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
Wayne Racey
Manager of IT Operations, City of St Catharines (Guest Contributor)
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08
Aug 2022
The City of St. Catharines is the largest city in Ontario, Canada’s Niagara Region. We strive to meet the needs of our over 140,000 residents. Cyber disruption could stop our municipality from functioning, so having a strong security stack is critical to our mission.
Globally, 44% of ransomware attacks target municipalities. In Canada, smaller cities have had to deal with increased attempts by threat actors to access information, without significant increases in security staff or budgets.
Data breaches incur an average cost totaling $6.35 million CAD because of ransomware payments, fines for leaked personally identifiable information, or recovery costs. That number does not quantify the additional reputational damage, PR setbacks, and other repercussions. Instead of resigning ourselves to accepting a greater cyber-risk, we turned to Darktrace to protect our network, email, and Microsoft 365 Suite.
How Self-Learning AI buys back time
I’m sure we as a municipality are grappling with the same issues that other cities of a similar size face from a budgetary standpoint. We do not have enough boots on the ground and our IT team is stretched thin. Investigating cyber security incidents takes a lot of time. We must find correlations between several old systems and manually go through security event logs to determine which incidents require follow-up. These factors greatly increased our response time.
When we first implemented Darktrace, we immediately saw that it does all the heavy lifting for us when it comes to the analysis of breach events. The Cyber AI Analyst shows a granular breakdown of the digital traffic coming into and out of the City, all on a single screen. This helps us separate the meaningful data from the noise.
I now start all my investigations with the Cyber AI Analyst. It sets me up with actionable insights that ensure I focus my time and energy in the most productive ways.
Darktrace also saves my team time and labor when it comes to responding to incidents. When it does detect attacks, it autonomously responds in seconds to contain them without interfering with any normal business operations.
We have been able to configure Darktrace’s settings to further streamline our workload. We’ve made several adjustments that reduce the number of helpdesk tickets my team receives, which ensures we’re spending our time on high-value work.
Darktrace not only makes up for the limited resources of our IT team, but also augments us. By simplifying our investigations and autonomously stopping attacks, Darktrace gives us more time to work on our other IT responsibilities without worrying about our security.
Darktrace/Network brings visibility and defense
Before Darktrace, we didn’t have visibility into the east-west traffic on our network. Once installed, it provided a view of traffic we had never anticipated, and we saw connections that we never even knew existed.
Darktrace/Network has insight into every laptop, server, phone, and user. The Self-Learning AI learns the “pattern of life” of our organization, so that it can recognize unusual activity that indicates a cyber-attack. In the case of a serious emerging attack, Darktrace RESPOND can take precise actions to stop it while otherwise allowing normal digital operations.
Darktrace/Network maps connections made within our network, whether between users and servers or between devices. It sorts users into groups that behave similarly, making it more obvious if one acts unusually. Darktrace/Email and Darktrace/Apps extend this coverage to our email and Microsoft 365 Suite, respectively. In this way, Darktrace allows us to see comprehensively into end-user traffic.
Darktrace can stop attempts to download malicious software, move malware laterally, upload private data, and everything in between. This means we are protected from attacks that are notoriously difficult to find, such as stealth attacks, machine speed ransomwares, insider threats, and zero-days.
Darktrace brings peace of mind
The Self-Learning AI has transformed my skepticism of AI into enthusiasm. I now see the possibilities with AI are limited only by one’s imagination, and the Darktrace team has harnessed it to create a great security tool.
Darktrace has proven to be the addition we needed to keep our digital landscape secure while contending with the limitations of budget and staffing during a time of increasingly frequent attacks targeting municipalities. My team’s support for Darktrace has been outstanding, and we have no regrets.
Darktrace gives us the assurance that no matter what rules we put in place regarding the flow of traffic on our network, it will always be present to reconfigure our defenses and safeguard our digital assets should an attack occur. It works 24/7, at machine speed, and augments our IT team. That defines peace of mind!
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
Wayne Racey
Manager of IT Operations, City of St Catharines (Guest Contributor)
From Exploit to Escalation: Tracking and Containing a Real-World Fortinet SSL-VPN Attack
This blog outlines a real-world attack where threat actors exploited Fortinet SSL-VPN vulnerabilities to infiltrate a network. It highlights how Darktrace identified threat and took immediate actions to contain it, demonstrating the importance of proactive detection and rapid intervention in minimizing risk and disruption.
Ivanti Under Siege: Investigating the Ivanti Endpoint Manager Mobile Vulnerabilities (CVE-2025-4427 & CVE-2025-4428)
Darktrace investigates active exploitation of Ivanti EPMM vulnerabilities CVE-2025-4427 and CVE-2025-4428. Threat actors can leverage these CVEs for unauthenticated remote code execution, delivering malware like KrustyLoader. This blog explores evolving post-exploitation tactics and emphasizes the need for continuous visibility and machine-speed response across enterprise network environments.
Explore key cyber threat trends observed across Darktrace’s customer base in the first half of 2025. As threat actors increasingly adopt AI and diversify their techniques and tooling, anomaly-based detection continues to prove vital in defending against evolving attacks.
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
The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.
Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.
Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.
The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.
