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Email Security in 2021: Darktrace's Key Predictions

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13
Jan 2021
13
Jan 2021
Darktrace reveals the top five email security predictions for 2021, offering expert insights into protecting your digital communications.

In 2020, we saw cyber-criminals take advantage of collective uncertainty with ‘fearware’ phishing attacks, and continue to shrink the lifespan of their attack campaigns by purchasing cheap domains in their thousands and regularly updating their attack infrastructure. As organizations began to rely heavily on SaaS collaboration tools, we also saw a marked increase in account compromise and phishing from the inside.

What new tactics and techniques can we expect email attackers to deliver this year – and how will companies react? This blog presents five predictions for security teams to keep an eye on in 2021.

1. Supply chain fraud will overtake CEO fraud

Targeting the C-Suite is a well-known tactic that has brought attackers success due to both the sensitive and valuable data these executives are exposed to and the authority they hold within a company. But with special protections increasingly put in place, it can be hard for an attacker to get to these individuals. The alternative for attackers? Go after whoever an organization trusts.

When an attacker can take over the legitimate email account of a trusted third-party supplier, they can net a big return without ever interacting with a C-level executive. Because of the implicit trust between established contacts, it’s likely that suppliers and contractors with large client bases will become ever more tempting targets. Why work hard to compromise 500 companies separately, when you can compromise just one and send fraudulent invoices to a thousand?

There are signs already hinting in this direction. Research earlier this year found that spoofing attacks that target the C-suite were decreasing. Meanwhile, the high-profile SolarWinds hack has shown just how effective cyber-attacks that come through the supply chain can be.

2. Email security solutions and third-party gateways that deploy via MX Records will be phased out

This is not so much a threat from attackers themselves, but rather a risk posed by existing email security tools – in particular, how they are deployed. Many email security solutions and third-party gateways currently sit right within the mail flow, directing traffic by way of mail exchanger records (MX records), which specify the mail server responsible for accepting email messages.

The trouble with this method isn’t really a security problem: it’s an operational issue. If the security tool sits within the mail flow, it can become a potential obstacle. If something goes wrong with the security gateway – an outage, for example – it can disrupt, or block, the entire flow of mail.

This disruption to business inevitably results in heightened pressure on security teams. Even when fully functioning, this deployment method can introduce latency, which is becoming less tolerable as remote work becomes more prevalent.

For this reason, we’re likely to see security teams continue to shift away from this deployment method, to API-based solutions that don’t require configuration changes and alleviate the risk of downtime.

3. The email attack cycle will continue to shorten

Once upon a time, attack infrastructure lasted for weeks or months. Darktrace research found that the average lifespan of fraudulent email dropped from 2.1 days in March 2018 to just 12 hours in 2020. Attackers can easily purchase new email domains with just a few pennies, and a brand-new domain, with no malicious activity on its record, will pass most email security reputation checks with ease.

It’s a worrying trend for legacy security tools reliant on signatures and blacklisting. And this lifespan will continue to trend towards zero. In the near future, we can expect attackers to reach a stage where a new domain is created, a single targeted email is sent, and the attack infrastructure is then retired before the cycle repeats.

4. Phishing will become even more targeted

The overwhelming, rapid proliferation of ‘fearware’ this year has shown how effective targeted and topical phishing lures could be. The sheer availability of information online and across a plethora of social media platforms allows attackers to move from a ‘spray and pray’ approach to sending well-researched, tailored emails that have a considerably higher chance of succeeding. And as the technology becomes available to automate much of this reconnaissance, it is natural to assume attackers will take advantage of these tools.

5. Hackers will target identities rather than devices

For attackers going after businesses that have expanded remote working, targeted cloud services might be favorable to going after centralized, on-premise infrastructure. Email-borne fraudulent invoices could prove a quieter and more lucrative alternative for the money-minded cyber-criminal than ransomware. Successful impersonations of trusted suppliers frequently enable successful wire fraud attacks. And since these attacks involve ‘clean’ emails – containing no links or attachments – they usually skip past legacy security tools with ease.

The next wave of email attacks

Cyber-criminals continue to find new ways to skirt by the traditional, legacy-based email security tools commonly relied on today. Organizations must prepare now for the next wave of email attacks by turning to a new email security approach capable of neutralizing novel and sophisticated attacks that gateways miss.

Hundreds of organizations have adopted a self-learning approach that doesn’t rely on hard-coded rules and signatures, but uses AI to spot unusual patterns in email communications indicative of a threat. As attackers continue to innovate, having an adaptive email security technology that continuously reassesses emails in light of new evidence will be crucial for security teams.

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.
AUTHOR
ABOUT ThE AUTHOR
Dan Fein
VP, Product

Based in New York, Dan joined Darktrace’s technical team in 2015, helping customers quickly achieve a complete and granular understanding of Darktrace’s product suite. Dan has a particular focus on Darktrace/Email, ensuring that it is effectively deployed in complex digital environments, and works closely with the development, marketing, sales, and technical teams. Dan holds a Bachelor’s degree in Computer Science from New York University.

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Thought Leadership

The State of AI in Cybersecurity: Understanding AI Technologies

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24
Jul 2024

About the State of AI Cybersecurity Report

Darktrace surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.

This blog continues the conversation from “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners”. This blog will focus on security professionals’ understanding of AI technologies in cybersecurity tools.

To access download the full report, click here.

How familiar are security professionals with supervised machine learning

Just 31% of security professionals report that they are “very familiar” with supervised machine learning.

Many participants admitted unfamiliarity with various AI types. Less than one-third felt "very familiar" with the technologies surveyed: only 31% with supervised machine learning and 28% with natural language processing (NLP).

Most participants were "somewhat" familiar, ranging from 46% for supervised machine learning to 36% for generative adversarial networks (GANs). Executives and those in larger organizations reported the highest familiarity.

Combining "very" and "somewhat" familiar responses, 77% had familiarity with supervised machine learning, 74% generative AI, and 73% NLP. With generative AI getting so much media attention, and NLP being the broader area of AI that encompasses generative AI, these results may indicate that stakeholders are understanding the topic on the basis of buzz, not hands-on work with the technologies.  

If defenders hope to get ahead of attackers, they will need to go beyond supervised learning algorithms trained on known attack patterns and generative AI. Instead, they’ll need to adopt a comprehensive toolkit comprised of multiple, varied AI approaches—including unsupervised algorithms that continuously learn from an organization’s specific data rather than relying on big data generalizations.  

Different types of AI

Different types of AI have different strengths and use cases in cyber security. It’s important to choose the right technique for what you’re trying to achieve.  

Supervised machine learning: Applied more often than any other type of AI in cyber security. Trained on human attack patterns and historical threat intelligence.  

Large language models (LLMs): Applies deep learning models trained on extremely large data sets to understand, summarize, and generate new content. Used in generative AI tools.  

Natural language processing (NLP): Applies computational techniques to process and understand human language.  

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies.  

What impact will generative AI have on the cybersecurity field?

More than half of security professionals (57%) believe that generative AI will have a bigger impact on their field over the next few years than other types of AI.

Chart showing the types of AI expected to impact security the most
Figure 1: Chart from Darktrace's State of AI in Cybersecurity Report

Security stakeholders are highly aware of generative AI and LLMs, viewing them as pivotal to the field's future. Generative AI excels at abstracting information, automating tasks, and facilitating human-computer interaction. However, LLMs can "hallucinate" due to training data errors and are vulnerable to prompt injection attacks. Despite improvements in securing LLMs, the best cyber defenses use a mix of AI types for enhanced accuracy and capability.

AI education is crucial as industry expectations for generative AI grow. Leaders and practitioners need to understand where and how to use AI while managing risks. As they learn more, there will be a shift from generative AI to broader AI applications.

Do security professionals fully understand the different types of AI in security products?

Only 26% of security professionals report a full understanding of the different types of AI in use within security products.

Confusion is prevalent in today’s marketplace. Our survey found that only 26% of respondents fully understand the AI types in their security stack, while 31% are unsure or confused by vendor claims. Nearly 65% believe generative AI is mainly used in cybersecurity, though it’s only useful for identifying phishing emails. This highlights a gap between user expectations and vendor delivery, with too much focus on generative AI.

Key findings include:

  • Executives and managers report higher understanding than practitioners.
  • Larger organizations have better understanding due to greater specialization.

As AI evolves, vendors are rapidly introducing new solutions faster than practitioners can learn to use them. There's a strong need for greater vendor transparency and more education for users to maximize the technology's value.

To help ease confusion around AI technologies in cybersecurity, Darktrace has released the CISO’s Guide to Cyber AI. A comprehensive white paper that categorizes the different applications of AI in cybersecurity. Download the White Paper here.  

Do security professionals believe generative AI alone is enough to stop zero-day threats?

No! 86% of survey participants believe generative AI alone is NOT enough to stop zero-day threats

This consensus spans all geographies, organization sizes, and roles, though executives are slightly less likely to agree. Asia-Pacific participants agree more, while U.S. participants agree less.

Despite expecting generative AI to have the most impact, respondents recognize its limited security use cases and its need to work alongside other AI types. This highlights the necessity for vendor transparency and varied AI approaches for effective security across threat prevention, detection, and response.

Stakeholders must understand how AI solutions work to ensure they offer advanced, rather than outdated, threat detection methods. The survey shows awareness that old methods are insufficient.

To access the full report, click here.

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Inside the SOC

Jupyter Ascending: Darktrace’s Investigation of the Adaptive Jupyter Information Stealer

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18
Jul 2024

What is Malware as a Service (MaaS)?

Malware as a Service (MaaS) is a model where cybercriminals develop and sell or lease malware to other attackers.

This approach allows individuals or groups with limited technical skills to launch sophisticated cyberattacks by purchasing or renting malware tools and services. MaaS is often provided through online marketplaces on the dark web, where sellers offer various types of malware, including ransomware, spyware, and trojans, along with support services such as updates and customer support.

The Growing MaaS Marketplace

The Malware-as-a-Service (MaaS) marketplace is rapidly expanding, with new strains of malware being regularly introduced and attracting waves of new and previous attackers. The low barrier for entry, combined with the subscription-like accessibility and lucrative business model, has made MaaS a prevalent tool for cybercriminals. As a result, MaaS has become a significant concern for organizations and their security teams, necessitating heightened vigilance and advanced defense strategies.

Examples of Malware as a Service

  • Ransomware as a Service (RaaS): Providers offer ransomware kits that allow users to launch ransomware attacks and share the ransom payments with the service provider.
  • Phishing as a Service: Services that provide phishing kits, including templates and email lists, to facilitate phishing campaigns.
  • Botnet as a Service: Renting out botnets to perform distributed denial-of-service (DDoS) attacks or other malicious activities.
  • Information Stealer: Information stealers are a type of malware specifically designed to collect sensitive data from infected systems, such as login credentials, credit card numbers, personal identification information, and other valuable data.

How does information stealer malware work?

Information stealers are an often-discussed type MaaS tool used to harvest personal and proprietary information such as administrative credentials, banking information, and cryptocurrency wallet details. This information is then exfiltrated from target networks via command-and-control (C2) communication, allowing threat actors to monetize the data. Information stealers have also increasingly been used as an initial access vector for high impact breaches including ransomware attacks, employing both double and triple extortion tactics.

After investigating several prominent information stealers in recent years, the Darktrace Threat Research team launched an investigation into indicators of compromise (IoCs) associated with another variant in late 2023, namely the Jupyter information stealer.

What is Jupyter information stealer and how does it work?

The Jupyter information stealer (also known as Yellow Cockatoo, SolarMarker, and Polazert) was first observed in the wild in late 2020. Multiple variants have since become part of the wider threat landscape, however, towards the end of 2023 a new variant was observed. This latest variant achieved greater stealth and updated its delivery method, targeting browser extensions such as Edge, Firefox, and Chrome via search engine optimization (SEO) poisoning and malvertising. This then redirects users to download malicious files that typically impersonate legitimate software, and finally initiates the infection and the attack chain for Jupyter [3][4]. In recently noted cases, users download malicious executables for Jupyter via installer packages created using InnoSetup – an open-source compiler used to create installation packages in the Windows OS.

The latest release of Jupyter reportedly takes advantage of signed digital certificates to add credibility to downloaded executables, further supplementing its already existing tactics, techniques and procedures (TTPs) for detection evasion and sophistication [4]. Jupyter does this while still maintaining features observed in other iterations, such as dropping files into the %TEMP% folder of a system and using PowerShell to decrypt and load content into memory [4]. Another reported feature includes backdoor functionality such as:

  • C2 infrastructure
  • Ability to download and execute malware
  • Execution of PowerShell scripts and commands
  • Injecting shellcode into legitimate windows applications

Darktrace Coverage of Jupyter information stealer

In September 2023, Darktrace’s Threat Research team first investigated Jupyter and discovered multiple IoCs and TTPs associated with the info-stealer across the customer base. Across most investigated networks during this time, Darktrace observed the following activity:

  • HTTP POST requests over destination port 80 to rare external IP addresses (some of these connections were also made via port 8089 and 8090 with no prior hostname lookup).
  • HTTP POST requests specifically to the root directory of a rare external endpoint.
  • Data streams being sent to unusual external endpoints
  • Anomalous PowerShell execution was observed on numerous affected networks.

Taking a further look at the activity patterns detected, Darktrace identified a series of HTTP POST requests within one customer’s environment on December 7, 2023. The HTTP POST requests were made to the root directory of an external IP address, namely 146.70.71[.]135, which had never previously been observed on the network. This IP address was later reported to be malicious and associated with Jupyter (SolarMarker) by open-source intelligence (OSINT) [5].

Device Event Log indicating several connections from the source device to the rare external IP address 146.70.71[.]135 over port 80.
Figure 1: Device Event Log indicating several connections from the source device to the rare external IP address 146.70.71[.]135 over port 80.

This activity triggered the Darktrace / NETWORK model, ‘Anomalous Connection / Posting HTTP to IP Without Hostname’. This model alerts for devices that have been seen posting data out of the network to rare external endpoints without a hostname. Further investigation into the offending device revealed a significant increase in external data transfers around the time Darktrace alerted the activity.

This External Data Transfer graph demonstrates a spike in external data transfer from the internal device indicated at the top of the graph on December 7, 2023, with a time lapse shown of one week prior.
Figure 2: This External Data Transfer graph demonstrates a spike in external data transfer from the internal device indicated at the top of the graph on December 7, 2023, with a time lapse shown of one week prior.

Packet capture (PCAP) analysis of this activity also demonstrates possible external data transfer, with the device observed making a POST request to the root directory of the malicious endpoint, 146.70.71[.]135.

PCAP of a HTTP POST request showing streams of data being sent to the endpoint, 146.70.71[.]135.
Figure 3: PCAP of a HTTP POST request showing streams of data being sent to the endpoint, 146.70.71[.]135.

In other cases investigated by the Darktrace Threat Research team, connections to the rare external endpoint 67.43.235[.]218 were detected on port 8089 and 8090. This endpoint was also linked to Jupyter information stealer by OSINT sources [6].

Darktrace recognized that such suspicious connections represented unusual activity and raised several model alerts on multiple customer environments, including ‘Compromise / Large Number of Suspicious Successful Connections’ and ‘Anomalous Connection / Multiple Connections to New External TCP Port’.

In one instance, a device that was observed performing many suspicious connections to 67.43.235[.]218 was later observed making suspicious HTTP POST connections to other malicious IP addresses. This included 2.58.14[.]246, 91.206.178[.]109, and 78.135.73[.]176, all of which had been linked to Jupyter information stealer by OSINT sources [7] [8] [9].

Darktrace further observed activity likely indicative of data streams being exfiltrated to Jupyter information stealer C2 endpoints.

Graph displaying the significant increase in the number of HTTP POST requests with No Get made by an affected device, likely indicative of Jupyter information stealer C2 activity.
Figure 4: Graph displaying the significant increase in the number of HTTP POST requests with No Get made by an affected device, likely indicative of Jupyter information stealer C2 activity.

In several cases, Darktrace was able to leverage customer integrations with other security vendors to add additional context to its own model alerts. For example, numerous customers who had integrated Darktrace with Microsoft Defender received security integration alerts that enriched Darktrace’s model alerts with additional intelligence, linking suspicious activity to Jupyter information stealer actors.

The security integration model alerts ‘Security Integration / Low Severity Integration Detection’ and (right image) ‘Security Integration / High Severity Integration Detection’, linking suspicious activity observed by Darktrace with Jupyter information stealer (SolarMarker).
Figure 5: The security integration model alerts ‘Security Integration / Low Severity Integration Detection’ and (right image) ‘Security Integration / High Severity Integration Detection’, linking suspicious activity observed by Darktrace with Jupyter information stealer (SolarMarker).

Conclusion

The MaaS ecosystems continue to dominate the current threat landscape and the increasing sophistication of MaaS variants, featuring advanced defense evasion techniques, poses significant risks once deployed on target networks.

Leveraging anomaly-based detections is crucial for staying ahead of evolving MaaS threats like Jupyter information stealer. By adopting AI-driven security tools like Darktrace / NETWORK, organizations can more quickly identify and effectively detect and respond to potential threats as soon as they emerge. This is especially crucial given the rise of stealthy information stealing malware strains like Jupyter which cannot only harvest and steal sensitive data, but also serve as a gateway to potentially disruptive ransomware attacks.

Credit to Nahisha Nobregas (Senior Cyber Analyst), Vivek Rajan (Cyber Analyst)

References

1.     https://www.paloaltonetworks.com/cyberpedia/what-is-multi-extortion-ransomware

2.     https://flashpoint.io/blog/evolution-stealer-malware/

3.     https://blogs.vmware.com/security/2023/11/jupyter-rising-an-update-on-jupyter-infostealer.html

4.     https://www.morphisec.com/hubfs/eBooks_and_Whitepapers/Jupyter%20Infostealer%20WEB.pdf

5.     https://www.virustotal.com/gui/ip-address/146.70.71.135

6.     https://www.virustotal.com/gui/ip-address/67.43.235.218/community

7.     https://www.virustotal.com/gui/ip-address/2.58.14.246/community

8.     https://www.virustotal.com/gui/ip-address/91.206.178.109/community

9.     https://www.virustotal.com/gui/ip-address/78.135.73.176/community

Appendices

Darktrace Model Detections

  • Anomalous Connection / Posting HTTP to IP Without Hostname
  • Compromise / HTTP Beaconing to Rare Destination
  • Unusual Activity / Unusual External Data to New Endpoints
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / Large Number of Suspicious Successful Connections
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Excessive Posts to Root
  • Compromise / Sustained SSL or HTTP Increase
  • Security Integration / High Severity Integration Detection
  • Security Integration / Low Severity Integration Detection
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Unusual Activity / Unusual External Data Transfer

AI Analyst Incidents:

  • Unusual Repeated Connections
  • Possible HTTP Command and Control to Multiple Endpoints
  • Possible HTTP Command and Control

List of IoCs

Indicators – Type – Description

146.70.71[.]135

IP Address

Jupyter info-stealer C2 Endpoint

91.206.178[.]109

IP Address

Jupyter info-stealer C2 Endpoint

146.70.92[.]153

IP Address

Jupyter info-stealer C2 Endpoint

2.58.14[.]246

IP Address

Jupyter info-stealer C2 Endpoint

78.135.73[.]176

IP Address

Jupyter info-stealer C2 Endpoint

217.138.215[.]105

IP Address

Jupyter info-stealer C2 Endpoint

185.243.115[.]88

IP Address

Jupyter info-stealer C2 Endpoint

146.70.80[.]66

IP Address

Jupyter info-stealer C2 Endpoint

23.29.115[.]186

IP Address

Jupyter info-stealer C2 Endpoint

67.43.235[.]218

IP Address

Jupyter info-stealer C2 Endpoint

217.138.215[.]85

IP Address

Jupyter info-stealer C2 Endpoint

193.29.104[.]25

IP Address

Jupyter info-stealer C2 Endpoint

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About the author
Nahisha Nobregas
SOC Analyst
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