How Community Housing LTD Enhanced Incident Response
04
Mar 2024
04
Mar 2024
Jamie Woodland - Head of Technology at Community Housing LTD - describes how Darktrace provides enhanced incident response for their security team.
About Community Housing Limited
Community Housing Limited is a non-profit organization based in Australia that focuses on providing affordable, long-term housing and creating employment opportunities where possible. We give people the security of having a home so that they can focus on other essential pathways. As such, we are responsible for sensitive information on our clients.
As part of our commitment to strengthening our cyber security, we sought to simplify and unify our incident response plans and equip our engineers and desktop support teams with all the information we need at our fingertips.
Why Community Housing Limited chose Darktrace
Our team hoped to achieve a response procedure that allowed us to have oversight over any potential security risks, even cases that don’t overtly seem like a security risk. For example, an incident could start as a payroll issue and end up in the hands of HR, instead of surfacing as a security problem. In this case, our security team has no way of knowing the real number of events or how the threat had actually started and played out, making incident response and mitigation even more challenging.
AI-generated playbooks save time during incident response
I wanted to reduce the time and resources it took our security team to appropriately respond to a threat. Darktrace automates several steps of the recovery process to accelerate the rate of incident response by using AI that learns the granular details of the specific organization, building a dynamic understanding of the devices, connections, and user behaviors that make up the normal “pattern of life.”
The AI then uses this understanding to create bespoke, AI-generated incident response playbooks that leverage an evolving understanding of our organization to determine recovery steps that are tailored not only to the specific incident but also to our unique environment.
For my security team, this means having access to all the information we need to respond to a threat. When running through an incident, rather than going to different places to synthesize relevant information, which takes up valuable resources and time, we can speed up its remediation with Darktrace.
The playbooks created by Darktrace help lower the technical skills required to respond to incidents by elevating the workload of the staff, tripling our capacity for incident response.
Realistic attack simulations upskill teams while saving resources
We have differing levels of experience on the team which means some members know exactly what to do during incident response while others are slower and need more guidance. Thus, we have to either outsource skilled security professionals or add a security solution that could lower the technical skills bar.
You don’t want to be second guessing and searching for the right move – it’s urgent – there should be certainty. Our goal with running attack simulations is to test and train our team's response capabilities in a “realistic” scenario. But this takes considerable time to plan and execute or can be expensive if outsourced, which can be a challenge for organizations short on resources.
Darktrace provides AI-assisted incident response and cyber-attack simulation using AI that understands the organization to run simulations that effectively map onto the real digital environment and the assets within it, providing training for actual incidents.
It is one thing to sit together in a meeting and discuss various outcomes of a cyber-attack, talking through the best response strategies. It is a huge benefit being able to run attack simulations that emulate real-world scenarios.
Our team can now see how an incident would play out over several days to resemble a real-world scenario or it can play through the simulation quickly to ascertain outcomes immediately. It then uses these insights to strengthen its technology, processes, and training.
AI-Powered Incident Response
Darktrace helps my security team save resources and upskill staff using AI to generate bespoke playbooks and run realistic simulations. Its real-time understanding of our business ensures incident preparedness and incident response are tailored to not only the specific threat in question, but also to the contextual infrastructure of the organization.
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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
Jamie Woodland
Head of Technology at Community Housing Limited
Jamie Woodland is the Head of Technology at Community Housing Limited, a non-profit organization based in Australia that focuses on providing affordable, long-term housing and creating employment opportunities where possible.
The State of AI in Cybersecurity: Understanding AI Technologies
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.
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.
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.
Jupyter Ascending: Darktrace’s Investigation of the Adaptive Jupyter Information Stealer
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].
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.
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.
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.
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.
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)
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![Device Event Log indicating several connections from the source device to the rare external IP address 146.70.71[.]135 over port 80.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/669973708ae3bd007b761051_Screenshot%202024-07-18%20at%2012.55.55%20PM.png)
![PCAP of a HTTP POST request showing streams of data being sent to the endpoint, 146.70.71[.]135.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/669973c9c7e48724389cdff4_Screenshot%202024-07-18%20at%2012.57.50%20PM.png)
