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August 22, 2022

Emotet Resurgence: Cross-Industry Analysis

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Aug 2022
Technical insights on the Emotet resurgence in 2022 across various client environments, industries, and regions.

Introduction

Last year provided further evidence that the cyber threat landscape remains both complex and challenging to predict. Between uncertain attribution, novel exploits and rapid malware developments, it is becoming harder to know where to focus security efforts. One of the largest surprises of 2021 was the re-emergence of the infamous Emotet botnet. This is an example of a campaign that ignored industry verticals or regions and seemingly targeted companies indiscriminately. Only 10 months after the Emotet takedown by law enforcement agencies in January, new Emotet activities in November were discovered by security researchers. These continued into the first quarter of 2022, a period which this blog will explore through findings from the Darktrace Threat Intel Unit. 

Dating back to 2019, Emotet was known to deliver Trickbot payloads which ultimately deployed Ryuk ransomware strains on compromised devices. This interconnectivity highlighted the hydra-like nature of threat groups wherein eliminating one (even with full-scale law enforcement intervention) would not rule them out as a threat nor indicate that the threat landscape would be any more secure. 

When Emotet resurged, as expected, one of the initial infection vectors involved leveraging existing Trickbot infrastructure. However, unlike the original attacks, it featured a brand new phishing campaign.

Figure 1: Distribution of observed Emotet activities across Darktrace deployments

Although similar to the original Emotet infections, the new wave of infections has been classified into two categories: Epochs 4 and 5. These had several key differences compared to Epochs 1 to 3. Within Darktrace’s global deployments, Emotet compromises associated to Epoch 4 appeared to be the most prevalent. Affected customer environments were seen within a large range of countries (Figure 1) and industry verticals such as manufacturing and supply chain, hospitality and travel, public administration, technology and telecoms and healthcare. Company demographics and size did not appear to be a targeting factor as affected customers had varying employee counts ranging from less than 250, to over 5000.

Key differences between Epochs 1-3 vs 4-5

Based on wider security research into the innerworkings of the Emotet exploits, several key differences were identified between Epochs 4/5 and its predecessors. The newer epochs used:

·       A different Microsoft document format (OLE vs XML-based).

·       A different encryption algorithm for communication. The new epochs used Elliptic Curve Cryptograph (ECC) [1] with public encryption keys contained in the C2 configuration file [2]. This was different from the previous Rivest-Shamir-Adleman (RSA) key encryption method.

·       Control Flow Flattening was used as an obfuscation technique to make detection and reverse engineering more difficult. This is done by hiding a program’s control flow [3].

·       New C2 infrastructure was observed as C2 communications were directed to over 230 unique IPs all associated to the new Epochs 4 and 5.

In addition to the new Epoch 4 and 5 features, Darktrace detected unsurprising similarities in those deployments affected by the renewed campaign. This included self-signed SSL connections to Emotet’s new infrastructure as well as malware spam activities to multiple rare external endpoints. Preceding these outbound communications, devices across multiple deployments were detected downloading Emotet-associated payloads (algorithmically generated DLL files).

Emotet Resurgence Campaign

Figure 2: Darktrace’s Detection Timeline for Emotet Epoch 4 and 5 compromises

1. Initial Compromise

The initial point of entry for the resurgence activity was almost certainly via Trickbot infrastructure or a successful phishing attack (Figure 2). Following the initial intrusion, the malware strain begins to download payloads via macro-ladened files which are used to spawn PowerShell for subsequent malware downloads.

Following the downloads, malicious communication with Emotet’s C2 infrastructure was observed alongside activities from the spam module. Within Darktrace, key techniques were observed and documented below.

2. Establish Foothold: Binary Dynamic-link library (.dll) with algorithmically generated filenames 

Emotet payloads are polymorphic and contain algorithmically generated filenames . Within deployments, HTTP GET requests involving a suspicious hostname, www[.]arkpp[.]com, and Emotet related samples such as those seen below were observed:

·       hpixQfCoJb0fS1.dll (SHA256 hash: 859a41b911688b00e104e9c474fc7aaf7b1f2d6e885e8d7fbf11347bc2e21eaa)

·       M0uZ6kd8hnzVUt2BNbRzRFjRoz08WFYfPj2.dll (SHA256 hash: 9fbd590cf65cbfb2b842d46d82e886e3acb5bfecfdb82afc22a5f95bda7dd804)

·       TpipJHHy7P.dll (SHA256 hash: 40060259d583b8cf83336bc50cc7a7d9e0a4de22b9a04e62ddc6ca5dedd6754b)

These DLL files likely represent the distribution of Emotet loaders which depends on windows processes such as rundll32[.]exe and regsvr32[.]exe to execute. 

3. Establish Foothold: Outbound SSL connections to Emotet C2 servers 

A clear network indicator of compromise for Emotet’s C2 communication involved self-signed SSL using certificate issuers and subjects which matched ‘CN=example[.]com,OU=IT Department,O=Global Security,L=London,ST=London,C=GB’ , and a common JA3 client fingerprint (72a589da586844d7f0818ce684948eea). The primary C2 communications were seen involving infrastructures classified as Epoch 4 rather than 5. Despite encryption in the communication content, network contextual connection details were sufficient for the detection of the C2 activities (Figure 3).

Figure 3: UI Model Breach logs on download and outbound SSL activities.

Outbound SSL and SMTP connections on TCP ports 25, 465, 587 

An anomalous user agent such as, ‘Microsoft Outlook 15.0’, was observed being used for SMTP connections with some subject lines of the outbound emails containing Base64-encoded strings. In addition, this JA3 client fingerprint (37cdab6ff1bd1c195bacb776c5213bf2) was commonly seen from the SSL connections. Based on the set of malware spam hostnames observed across at least 10 deployments, the majority of the TLDs were .jp, .com, .net, .mx, with the Japanese TLD being the most common (Figure 4).

Figure 4: Malware Spam TLDs observed in outbound SSL and SMTP

 Plaintext spam content generated from the spam module were seen in PCAPs (Figure 5). Examples of clear phishing or spam indicators included 1) mismatched personal header and email headers, 2) unusual reply chain and recipient references in the subject line, and 3) suspicious compressed file attachments, e.g. Electronic form[.]zip.

Figure 5: Example of PCAP associated to SPAM Module

4. Accomplish Mission

 The Emotet resurgence also showed through secondary compromises involving anomalous SMB drive writes related to CobaltStrike. This consistently included the following JA3 hash (72a589da586844d7f0818ce684948eea) seen in SSL activities as well as SMB writes involving the svchost.exe file.

Darktrace Detection

 The key DETECT models used to identify Emotet Resurgence activities were focused on determining possible C2. These included:

·       Suspicious SSL Activity

·       Suspicious Self-Signed SSL

·       Rare External SSL Self-Signed

·       Possible Outbound Spam

File-focused models were also beneficial and included:

·       Zip or Gzip from Rare External Location

·       EXE from Rare External Location

Darktrace’s detection capabilities can also be shown through a sample of case studies identified during the Threat Research team’s investigations.

Case Studies 

Darktrace’s detection of Emotet activities was not limited by industry verticals or company sizing. Although there were many similar features seen across the new epoch, each incident displayed varying techniques from the campaign. This is shown in two client environments below:

When investigating a large customer environment within the public administration sector, 16 different devices were detected making 52,536 SSL connections with the example[.]com issuer. Devices associated with this issuer were mainly seen breaching the same Self-Signed and Spam DETECT models. Although anomalous incoming octet-streams were observed prior to this SSL, there was no clear relation between the downloads and the Emotet C2 connections. Despite the total affected devices occupying only a small portion of the total network, Darktrace analysts were able to filter against the much larger network ‘noise’ and locate detailed evidence of compromise to notify the customer.

Darktrace also identified new Emotet activities in much smaller customer environments. Looking at a company in the healthcare and pharmaceutical sector, from mid-March 2022 a single internal device was detected making an HTTP GET request to the host arkpp[.]com involving the algorithmically-generated DLL, TpipJHHy7P.dll with the SHA256 hash: 40060259d583b8cf83336bc50cc7a7d9e0a4de22b9a04e62ddc6ca5dedd6754b (Figure 6). 

Figure 6: A screenshot from VirusTotal, showing that the SHA256 hash has been flagged as malicious by other security vendors.

After the sample was downloaded, the device contacted a large number of endpoints that had never been contacted by devices on the network. The endpoints were contacted over ports 443, 8080, and 7080 involving Emotet related IOCs and the same SSL certificate mentioned previously. Malware spam activities were also observed during a similar timeframe.

 The Emotet case studies above demonstrate how autonomous detection of an anomalous sequence of activities - without depending on conventional rules and signatures - can reveal significant threat activities. Though possible staged payloads were only seen in a proportion of the affected environments, the following outbound C2 and malware spam activities involving many endpoints and ports were sufficient for the detection of Emotet.

 If present, in both instances Darktrace’s Autonomous Response technology, RESPOND, would recommend or implement surgical actions to precisely target activities associated with the staged payload downloads, outgoing C2 communications, and malware spam activities. Additionally, restriction to the devices’ normal pattern of life will prevent simultaneously occurring malicious activities while enabling the continuity of normal business operations.

 Conclusion 

·       The technical differences between past and present Emotet strains emphasizes the versatility of malicious threat actors and the need for a security solution that is not reliant on signatures.

·       Darktrace’s visibility and unique behavioral detection continues to provide visibility to network activities related to the novel Emotet strain without reliance on rules and signatures. Key examples include the C2 connections to new Emotet infrastructure.

·       Looking ahead, detection of C2 establishment using suspicious DLLs will prevent further propagation of the Emotet strains across networks.

·       Darktrace’s AI detection and response will outpace conventional post compromise research involving the analysis of Emotet strains through static and dynamic code analysis, followed by the implementation of rules and signatures.

Thanks to Paul Jennings and Hanah Darley for their contributions to this blog.

Appendices

Model breaches

·       Anomalous Connection / Anomalous SSL without SNI to New External 

·       Anomalous Connection / Application Protocol on Uncommon Port 

·       Anomalous Connection / Multiple Connections to New External TCP Port 

·       Anomalous Connection / Multiple Failed Connections to Rare Endpoint 

·       Anomalous Connection / Multiple HTTP POSTs to Rare Hostname 

·       Anomalous Connection / Possible Outbound Spam 

·       Anomalous Connection / Rare External SSL Self-Signed 

·       Anomalous Connection / Repeated Rare External SSL Self-Signed      

·       Anomalous Connection / Suspicious Expired SSL 

·       Anomalous Connection / Suspicious Self-Signed SSL

·       Anomalous File / Anomalous Octet Stream (No User Agent) 

·       Anomalous File / Zip or Gzip from Rare External Location 

·       Anomalous File / EXE from Rare External Location

·       Compromise / Agent Beacon to New Endpoint 

·       Compromise / Beacon to Young Endpoint 

·       Compromise / Beaconing Activity To External Rare 

·       Compromise / New or Repeated to Unusual SSL Port 

·       Compromise / Repeating Connections Over 4 Days 

·       Compromise / Slow Beaconing Activity To External Rare 

·       Compromise / SSL Beaconing to Rare Destination 

·       Compromise / Suspicious Beaconing Behaviour 

·       Compromise / Suspicious Spam Activity 

·       Compromise / Suspicious SSL Activity 

·       Compromise / Sustained SSL or HTTP Increase 

·       Device / Initial Breach Chain Compromise 

·       Device / Large Number of Connections to New Endpoints 

·       Device / Long Agent Connection to New Endpoint 

·       Device / New User Agent 

·       Device / New User Agent and New IP 

·       Device / SMB Session Bruteforce 

·       Device / Suspicious Domain 

·       Device / Suspicious SMB Scanning Activity 

For Darktrace customers who want to know more about using Darktrace to triage Emotet, refer here for an exclusive supplement to this blog.

References

[1] https://blog.lumen.com/emotet-redux/

[2] https://blogs.vmware.com/security/2022/03/emotet-c2-configuration-extraction-and-analysis.html

[3] https://news.sophos.com/en-us/2022/05/04/attacking-emotets-control-flow-flattening/

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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March 28, 2025

Darktrace Recognized as the Only Visionary in the 2025 Gartner® Magic Quadrant™ for CPS Protection Platforms

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We are thrilled to announce that Darktrace has been named the only Visionary in the inaugural Gartner® Magic Quadrant™ for Cyber-Physical Systems (CPS) Protection Platforms. We feel This recognition highlights Darktrace’s AI-driven approach to securing industrial environments, where conventional security solutions struggle to keep pace with increasing cyber threats.

A milestone for CPS security

It's our opinion that the first-ever Gartner Magic Quadrant for CPS Protection Platforms reflects a growing industry shift toward purpose-built security solutions for critical infrastructure. As organizations integrate IT, OT, and cloud-connected systems, the cyber risk landscape continues to expand. Gartner evaluated 17 vendors based on their Ability to Execute and Completeness of Vision, establishing a benchmark for security leaders looking to enhance cyber resilience in industrial environments.

We believe the Gartner recognition of Darktrace as the only Visionary reaffirms the platform’s ability to proactively defend against cyber risks through AI-driven anomaly detection, autonomous response, and risk-based security strategies. With increasingly sophisticated attacks targeting industrial control systems, organizations need a solution that continuously evolves to defend against both known and unknown threats.

AI-driven security for CPS environments

Securing CPS environments requires an approach that adapts to the dynamic nature of industrial operations. Traditional security tools rely on static signatures and predefined rules, leaving gaps in protection against novel and sophisticated threats. Darktrace / OT takes a different approach, leveraging Self-Learning AI to detect and neutralize threats in real time, even in air-gapped or highly regulated environments.

Darktrace / OT continuously analyzes network behaviors to establish a deep understanding of what is “normal” for each industrial environment. This enables it to autonomously identify deviations that signal potential cyber threats, providing early warning and proactive defense before attacks can disrupt operations. Unlike rule-based security models that require constant manual updates, Darktrace / OT improves with the environment, ensuring long-term resilience against emerging cyber risks.

Bridging the IT-OT security gap

A major challenge for organizations protecting CPS environments is the disconnect between IT and OT security. While IT security has traditionally focused on data

protection and compliance, OT security is driven by operational uptime and safety, leading to siloed security programs that leave critical gaps in visibility and response.

Darktrace / OT eliminates these silos by providing unified visibility across IT, OT, and IoT assets, ensuring that security teams have a complete picture of their attack surface. Its AI-driven approach enables cross-domain threat detection, recognizing risks that move laterally between IT and OT environments. By seamlessly integrating with existing security architectures, Darktrace / OT helps organizations close security gaps without disrupting industrial processes.

Proactive OT risk management and resilience

Beyond detection and response, Darktrace / OT strengthens organizations’ ability to manage cyber risk proactively. By mapping vulnerabilities to real-world attack paths, it prioritizes remediation actions based on actual exploitability and business impact, rather than relying on isolated CVE scores. This risk-based approach enables security teams to focus resources where they matter most, reducing overall exposure to cyber threats.

With autonomous threat response capabilities, Darktrace / OT not only identifies risks but also contains them in real time, preventing attackers from escalating intrusions. Whether mitigating ransomware, insider threats, or sophisticated nation-state attacks, Darktrace / OT ensures that industrial environments remain secure, operational, and resilient, no matter how threats evolve.

AI-powered incident response and SOC automation

Security teams are facing an overwhelming volume of alerts, making it difficult to prioritize threats and respond effectively. Darktrace / OT’s Cyber AI Analyst acts as a force multiplier for security teams by automating threat investigation, alert triage, and response actions. By mimicking the workflow of a human SOC analyst, Cyber AI Analyst provides contextual insights that accelerate incident response and reduce the manual workload on security teams.

With 24/7 autonomous monitoring, Darktrace / OT ensures that threats are continuously detected and investigated in real time. Whether facing ransomware, insider threats, or sophisticated nation-state attacks, organizations can rely on AI-driven security to contain threats before they disrupt operations.

Trusted by customers: Darktrace / OT recognized in Gartner Peer Insights

Source: Gartner Peer Insights (Oct 28th)

Beyond our recognition in the Gartner Magic Quadrant, we feel Darktrace / OT is one of the highest-rated CPS security solutions on Gartner Peer Insights, reflecting strong customer trust and validation. With a 4.9/5 overall rating and the highest "Willingness to Recommend" score among CPS vendors, organizations across critical infrastructure and industrial sectors recognize the impact of our AI-driven security approach. Source: Gartner Peer Insights (Oct 28th)

This strong customer endorsement underscores why leading enterprises trust Darktrace / OT to secure their CPS environments today and in the future.

Redefining the future of CPS security

It's our view that Darktrace’s recognition as the only Visionary in the Gartner Magic Quadrant for CPS Protection Platforms validates its leadership in next-generation industrial security. As cyber threats targeting critical infrastructure continue to rise, organizations must adopt AI-driven security solutions that can adapt, respond, and mitigate risks in real time.

We believe this recognition reinforces our commitment to innovation and our mission to secure the world’s most essential systems. This recognition reinforces our commitment to innovation and our mission to secure the world’s most essential systems.

® Download the full Gartner Magic Quadrant for CPS Protection Platforms

® Request a demo to see Darktrace OT in action.

Gartner, Magic Quadrant for CPS Protection Platforms , Katell Thielemann, Wam Voster, Ruggero Contu 12 February 2025

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant and Peer Insights are a registered trademark, of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, 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.

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Pallavi Singh
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March 28, 2025

Survey Findings: AI Cybersecurity Priorities and Objectives in 2025

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AI is changing the cybersecurity field, both on the offensive and defensive sides. We surveyed over 1,500 cybersecurity professionals from around the world to uncover their attitudes, understanding, and priorities when it comes to AI cybersecurity in 2025. Our full report, unearthing some telling trends, is available now.  

Download the full report to explore these findings in depth

It is clear that security professionals know their field is changing fast, and that AI will continue to influence those changes. Our survey results show that they are aware that the rise of AI will require them to adopt new tools and learn to use them effectively. Still, they aren’t always certain about how to plan for the future, or what to invest in.

The top priorities of security stakeholders for improving their defenses against AI-powered threats include augmenting their existing tool stacks with AI-powered solutions and improving integration among their security tools.

Figure 1: Year-over-year changes to the priorities of securitystakeholders.

Increasing cybersecurity staff

As was also the case last year, security stakeholders are less interested in hiring additional staff than in adding new AI-powered tools onto their existing security stacks, with only with 11% (and only 8% of executives) planning to increase cybersecurity staff in 2025.

This suggests that leaders are looking for new methods to overcome talent resource shortages.

Adding AI-powered security tools to supplement existing solutions

Executives are particularly enthusiastic about adopting AI-driven tools. Within that goal, there is consensus about the qualities cyber professionals are looking for when purchasing new security capabilities or replacing existing products.

  • 87% of survey respondents prefer solutions that are part of a broader platform over individual point products

These results are similar to last year’s, where again, almost nine out of ten agreed that a platform-oriented security solution was more effective at stopping cyber threats than a collection of individual products.

  • 88% of survey respondents agree that the use of AI within the security stack is critical to freeing up time for security teams to become more proactive, compared to reactive

AI itself can contribute to this shift from reactive to proactive security, improving risk prioritization and automating preventative strategies like Attack Surface Management (ASM) and proactive exposure management.

  • 84% of survey respondents prefer defensive AI solutions that do not require the organization’s data to be shared externally

This preference may reflect increasing attention to the data privacy and security risks posed by generative AI (gen AI) adoption. It may also reflect growing awareness of data residency requirements and other restrictions that regulators are imposing.

Improving cybersecurity awareness training for end users

Based on the survey results, practitioners in SecOps are more interested in improving security awareness training.

This goal is not necessarily mutually exclusive from the addition of AI tools. For example, teams can leverage AI to build more effective security awareness training programs, and as gen AI tools are adopted, users will need to be taught about data privacy and associated security risks.

Looking towards the future

One conclusion we can draw from the attitudinal shifts from last year’s survey to this year’s: while hiring more security staff might be a nice-to-have, implementing AI-powered tools so that existing employees can work smarter is increasingly viewed as a must-have.

However, trending goals are not just about managing resources, whether headcount or AI investments, to keep up with workloads. Existing end users must also be trained to follow safe practices while using established and newly adopted tools.

Security professionals, including executives, SecOps, and every role in between, continue to shift their identified challenges and priorities as they gear up for the coming year in the Era of AI.

State of AI report

Download the full report to explore these findings in depth

The full report for Darktrace’s State of AI Cybersecurity is out now. Download the paper to dig deeper into these trends, and see how results differ by industry, region, organization size, and job title.  

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