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September 19, 2021

Defending Tokyo Olympics: AI Neutralizes IoT Attack

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19
Sep 2021
Learn how Darktrace autonomously thwarted a cyber-attack on a national sporting body before the Tokyo Olympics in this detailed breakdown.

One of the greatest issues in security is how to deal with high-stress scenarios when there is a significant breach, and there is too much to do in too little time. The nightmare scenario for any CISO is when this happens during a critical moment for the organization: an important acquisition, a crucial news announcement, or in this case, a global sporting event attracting an audience of millions.

Threat actors often exploit the pressure of these events to cause disruption or extract hefty sums. Sporting occasions, especially Formula 1 races, the Super Bowl, and the Olympics, attract a great deal of criminal interest.

The games begin

There have been several recorded attacks and data breaches at the Olympics this year, including an incident when a volleyball commentator asked his colleague for his computer password – not realizing he was still on air.

In a more nefarious case discovered by Darktrace, a Raspberry Pi device was covertly implanted into a national sporting body directly involved in the Olympics, in an attempt to exfiltrate sensitive data. The events took place one week before the start of the Games, and a data breach at this time would have had significant ramifications for the reputation of the organization, the confidentiality of their plans, and potentially the safety of their athletes.

Darktrace AI recognized this activity as malicious given its evolving understanding of ‘self’ for the organization, and Antigena – Darktrace’s autonomous response capability – took action at machine speed to interrupt the threat, affording the human security team the critical time they needed to catch up and neutralize the attack.

In what follows, we break down the attack.

Figure 1: The overall dwell time was three days.

Breaking down the attack

July 15, 14:09 — Initial intrusion

An unauthorized Raspberry Pi device connected to the organization’s digital environment – disguised and named in a way which mimicked the corporate naming convention. As a small IoT device, Raspberry Pis can be easily hidden and are difficult to locate physically in large environments. They have been used in various high-profile hacks in the past including the 2018 NASA breach.

IoT devices – from printers to fish tanks – pose a serious risk to security, as they can be exploited to gather information, move laterally, and escalate privileges.

July 15, 15:25 — External VPN activity

Anomalous UDP connections were made to an external endpoint over port 1194 (Open VPN activity). URIs showed that the device downloaded data potentially associated with Open VPN configuration files. This could represent an attempt to establish a secure channel for malicious activity such as data exfiltration.

By establishing an outgoing VPN, the attacker obfuscated their activity and bypassed the organization’s signature-based security, which could not detect the encrypted traffic. Antigena immediately blocked the suspicious connectivity, regardless of the encryption, identifying that the activity was a deviation from the ‘pattern of life’ for new devices.

July 15, 16:04 — Possible C2 activity

The Raspberry Pi soon began making repeated HTTP connections to a new external endpoint and downloaded octet streams — arbitrary binary data. It seems the activity was initiated by a standalone software process as opposed to a web browser.

Darktrace revealed that the device was performing an unusual external data transfer to the same endpoint, uploading 7.5 MB which likely contained call home data about the new location and name of the device.

July 15, 16:41 — Internal reconnaissance

The device engaged in TCP scanning across three unique internal IP addresses over a wide range of ports. Although the network scan only targeted three internal servers, the activity was identified by Darktrace as a suspicious increase in internal connections and failed internal connections.

Antigena instantly stopped the Raspberry Pi from making internal connections over the ports involved in the scanning activity, as well as enforcing the device’s ‘pattern of life’.

Figure 2: Device event log showing the components which enable Darktrace to detect network scanning.

July 15, 18:14 — Multiple internal reconnaissance tactics

The Raspberry Pi then scanned a large number of devices on SMB port 445 and engaged in suspicious use of the outdated SMB version 1 protocol, suggesting more in-depth reconnaissance to find exploitable vulnerabilities.

Reacting to the scanning activity alongside the insecure protocol SMBv1, Antigena blocked connections from the source device to the destination IPs for one hour.

Four minutes later, the device engaged in connections to the open-source vulnerability scanner, Nmap. Nmap can be used legitimately for vulnerability scanning and so often is not alerted to by traditional security tools. However, Darktrace’s AI detected that the use of the tool was highly anomalous, and so blocked all outgoing traffic for ten minutes.

July 15, 22:03 — Final reconnaissance

Three hours later, the Raspberry Pi initiated another network scan across six unique external IPs – this was in preparation for the final data exfiltration. Antigena responded with instant, specific blocks to the external IPs which the device was attempting to connect to – before any data could be exfiltrated.

After 30 minutes, Darktrace detected bruteforcing activity from the Raspberry Pi using the SMB and NTLM authentication protocols. The device made a large number of failed login attempts to a single internal device using over 100 unique user accounts. Antigena blocked the activity, successfully stopping another wave of attempted SMB lateral movement.

By this stage, Antigena had bought the security team enough time to respond. The team applied an Antigena quarantine rule (the most severe action Antigena can take) to the Raspberry Pi, until they were able to find the physical location of the device and unplug it from the network.

How AI Analyst stitched together the incident

Cyber AI Analyst autonomously reported on three key moments of the attack:

  • Unusual External Data Transfer
  • Possible HTTP Command and Control
  • TCP Scanning of Multiple Devices (the attempted data exfiltration)

It tied together activities over the span of multiple days, which could have been easily missed by human analysis. The AI provided crucial pieces of information, including the extent of the scanning activity. Such insights are time-consuming to calculate manually.

Figure 3: A screenshot from Cyber AI Analyst summarizing potential C2 activity.

Autonomous Response

Antigena took targeted action throughout to neutralize the suspicious behavior, while allowing normal business operations to continue unhindered.

Rather than widespread blocking, Antigena implemented a range of nuanced responses depending on the situation, always taking the smallest action necessary to deal with the threat.

Figure 4: Darktrace’s UI reveals the attempted network reconnaissance, and Antigena actions a targeted response. All IP addresses have been randomized.

Raspberry Pi: IoT threats

In an event involving 206 countries and 11,000 athletes, facing attacks from hacktivists, criminal groups, and nation states, with many broadcasters working remotely and millions watching from home, organizations involved in the Olympics needed a security solution which could rise to the occasion.

Even with the largest affairs, threats can come from the smallest places. The ability to detect unauthorized IoT devices and maintain visibility over all activity in your digital estate is essential.

Autonomous Response protects against the unexpected, stopping malicious activity at machine speed without any user input. This is necessary for rapid response and remediation, especially for resource-stretched internal security teams. When it comes to defending systems and outpacing attackers, AI always wins the race.

Thanks to Darktrace analysts Emma Foulger and Greg Chapman for their insights on the above threat find.

Learn how two rogue Raspberry Pi devices infected a healthcare provider

Darktrace model detections:

  • Compromise / Ransomware / Suspicious SMB Activity
  • Tags / New Raspberry Pi Device
  • Device / Network Scan
  • Unusual Activity / Unusual Raspberry Pi Activity
  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Suspicious Network Scan Activity
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Device / Suspicious SMB Scanning Activity
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Device / Attack and Recon Tools
  • Device / New Device with Attack Tools
  • Device / Anomalous Nmap Activity
  • Device / External Network Scan
  • Device / SMB Session Bruteforce
  • Antigena / Network / Manual / Block All Outgoing Connections
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
Oakley Cox
Director of Product

Oakley is a Product Manager within the Darktrace R&D team. He collaborates with global customers, including all critical infrastructure sectors and Government agencies, to ensure Darktrace/OT remains the first in class solution for OT Cyber Security. He draws on 7 years’ experience as a Cyber Security Consultant to organizations across EMEA, APAC and ANZ. His research into cyber-physical security has been published by Cyber Security journals and by CISA. Oakley has a Doctorate (PhD) from the University of Oxford.

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September 26, 2024

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Inside the SOC

Thread Hijacking: How Attackers Exploit Trusted Conversations to Infiltrate Networks

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What is Thread Hijacking?

Cyberattacks are becoming increasingly stealthy and targeted, with malicious actors focusing on high-value individuals to gain privileged access to their organizations’ digital environments. One technique that has gained prominence in recent years is thread hijacking. This method allows attackers to infiltrate ongoing conversations, exploiting the trust within these threads to access sensitive systems.

Thread hijacking typically involves attackers gaining access to a user’s email account, monitoring ongoing conversations, and then inserting themselves into these threads. By replying to existing emails, they can send malicious links, request sensitive information, or manipulate the conversation to achieve their goals, such as redirecting payments or stealing credentials. Because such emails appear to come from a trusted source, they often bypass human security teams and traditional security filters.

How does threat hijacking work?

  1. Initial Compromise: Attackers first gain access to a user’s email account, often through phishing, malware, or exploiting weak passwords.
  2. Monitoring: Once inside, they monitor the user’s email threads, looking for ongoing conversations that can be exploited.
  3. Infiltration: The attacker then inserts themselves into these conversations, often replying to existing emails. Because the email appears to come from a trusted source within an ongoing thread, it bypasses many traditional security filters and raises less suspicion.
  4. Exploitation: Using the trust established in the conversation, attackers can send malicious links, request sensitive information, or manipulate the conversation to achieve their goals, such as redirecting payments or stealing credentials.

A recent incident involving a Darktrace customer saw a malicious actor attempt to manipulate trusted email communications, potentially exposing critical data. The attacker created a new mailbox rule to forward specific emails to an archive folder, making it harder for the customer to notice the malicious activity. This highlights the need for advanced detection and robust preventive tools.

Darktrace’s Self-Learning AI is able to recognize subtle deviations in normal behavior, whether in a device or a Software-as-a-Service (SaaS) user. This capability enables it to detect emerging attacks in their early stages. In this post, we’ll delve into the attacker’s tactics and illustrate how Darktrace / IDENTITY™ successfully identified and mitigated a thread hijacking attempt, preventing escalation and potential disruption to the customer’s network.

Threat hijacking attack overview & Darktrace coverage

On August 8, 2024, Darktrace detected an unusual email received by a SaaS account on a customer’s network. The email appeared to be a reply to a previous chain discussing tax and payment details, likely related to a transaction between the customer and one of their business partners.

Headers of the suspicious email received.
Figure 1: Headers of the suspicious email received.

A few hours later, Darktrace detected the same SaaS account creating a new mailbox rule named “.”, a tactic commonly used by malicious actors to evade detection when setting up new email rules [2]. This rule was designed to forward all emails containing a specific word to the user’s “Archives” folder. This evasion technique is typically used to move any malicious emails or responses to a rarely opened folder, ensuring that the genuine account holder does not see replies to phishing emails or other malicious messages sent by attackers [3].

Darktrace recognized the newly created email rule as suspicious after identifying the following parameters:

  • AlwaysDeleteOutlookRulesBlob: False
  • Force: False
  • MoveToFolder: Archive
  • Name: “.”
  • FromAddressContainsWords: [Redacted]
  • MarkAsRead: True
  • StopProcessingRules: True

Darktrace also noted that the user attempting to create this new email rule had logged into the SaaS environment from an unusual IP address. Although the IP was located in the same country as the customer and the ASN used by the malicious actor was typical for the customer’s network, the rare IP, coupled with the anomalous behavior, raised suspicions.

Figure 2: Hijacked SaaS account creating the new mailbox rule.

Given the suspicious nature of this activity, Darktrace’s Security Operations Centre (SOC) investigated the incident and alerted the customer’s security team of this incident.

Due to a public holiday in the customer's location (likely an intentional choice by the threat actor), their security team did not immediately notice or respond to the notification. Fortunately, the customer had Darktrace's Autonomous Response capability enabled, which allowed it to take action against the suspicious SaaS activity without human intervention.

In this instance, Darktrace swiftly disabled the seemingly compromised SaaS user for 24 hours. This action halted the spread of the compromise to other accounts on the customer’s SaaS platform and prevented any sensitive data exfiltration. Additionally, it provided the security team with ample time to investigate the threat and remove the user from their environment. The customer also received detailed incident reports and support through Darktrace’s Security Operations Support service, enabling direct communication with Darktrace’s expert Analyst team.

Conclusion

Ultimately, Darktrace’s anomaly-based detection allowed it to identify the subtle deviations from the user’s expected behavior, indicating a potential compromise on the customer’s SaaS platform. In this case, Darktrace detected a login to a SaaS platform from an unusual IP address, despite the attacker’s efforts to conceal their activity by using a known ASN and logging in from the expected country.

Despite the attempted SaaS hijack occurring on a public holiday when the customer’s security team was likely off-duty, Darktrace autonomously detected the suspicious login and the creation of a new email rule. It swiftly blocked the compromised SaaS account, preventing further malicious activity and safeguarding the organization from data exfiltration or escalation of the compromise.

This highlights the growing need for AI-driven security capable of responding to malicious activity in the absence of human security teams and detect subtle behavioral changes that traditional security tools.

Credit to: Ryan Traill, Threat Content Lead for his contribution to this blog

Appendices

Darktrace Model Detections

SaaS / Compliance / Anomalous New Email Rule

Experimental / Antigena Enhanced Monitoring from SaaS Client Block

Antigena / SaaS / Antigena Suspicious SaaS Activity Block

Antigena / SaaS / Antigena Email Rule Block

References

[1] https://blog.knowbe4.com/whats-the-best-name-threadjacking-or-man-in-the-inbox-attacks

[2] https://darktrace.com/blog/detecting-attacks-across-email-saas-and-network-environments-with-darktraces-combined-ai-approach

[3] https://learn.microsoft.com/en-us/defender-xdr/alert-grading-playbook-inbox-manipulation-rules

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About the author
Maria Geronikolou
Cyber Analyst

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September 26, 2024

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How AI can help CISOs navigate the global cyber talent shortage

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The global picture

4 million cybersecurity professionals are needed worldwide to protect and defend the digital world – twice the number currently in the workforce.1

Innovative technologies are transforming business operations, enabling access to new markets, personalized customer experiences, and increased efficiency. However, this digital transformation also challenges Security Operations Centers (SOCs) with managing and protecting a complex digital environment without additional resources or advanced skills.

At the same time, the cybersecurity industry is suffering a severe global skills shortage, leaving many SOCs understaffed and under-skilled. With a 72% increase in data breaches from 2021-20232, SOCs are dealing with overwhelming alert volumes from diverse security tools. Nearly 60% of cybersecurity professionals report burnout3, leading to high turnover rates. Consequently, only a fraction of alerts are thoroughly investigated, increasing the risk of undetected breaches. More than half of organizations that experienced breaches in 2024 admitted to having short-staffed SOCs.4

How AI can help organizations do more with less

Cyber defense needs to evolve at the same pace as cyber-attacks, but the global skills shortage is making that difficult. As threat actors increasingly abuse AI for malicious purposes, using defensive AI to enable innovation and optimization at scale is reshaping how organizations approach cybersecurity.

The value of AI isn’t in replacing humans, but in augmenting their efforts and enabling them to scale their defense capabilities and their value to the organization. With AI, cybersecurity professionals can operate at digital speed, analyzing vast data sets, identifying more vulnerabilities with higher accuracy, responding and triaging faster, reducing risks, and implementing proactive measures—all without additional staff.

Research indicates that organizations leveraging AI and automation extensively in security functions—such as prevention, detection, investigation, or response—reduced their average mean time to identify (MTTI) and mean time to contain (MTTC) data breaches by 33% and 43%, respectively. These organizations also managed to contain breaches nearly 100 days faster on average compared to those not using AI and automation.5

First, you've got to apply the right AI to the right security challenge. We dig into how different AI technologies can bridge specific skills gaps in the CISO’s Guide to Navigating the Cybersecurity Skills Shortage.

Cases in point: AI as a human force multiplier

Let’s take a look at just some of the cybersecurity challenges to which AI can be applied to scale defense efforts and relieve the burden on the SOC. We go further into real-life examples in our white paper.

Automated threat detection and response

AI enables 24/7 autonomous response, eliminating the need for after-hours SOC shifts and providing security leaders with peace of mind. AI can scale response efforts by analyzing vast amounts of data in real time, identifying anomalies, and initiating precise autonomous actions to contain incidents, which buys teams time for investigation and remediation.  

Triage and investigation

AI enhances the triage process by automatically categorizing and prioritizing security alerts, allowing cybersecurity professionals to focus on the most critical threats. It creates a comprehensive picture of an attack, helps identify its root cause, and generates detailed reports with key findings and recommended actions.  

Automation also significantly reduces overwhelming alert volumes and high false positive rates, enabling analysts to concentrate on high-priority threats and engage in more proactive and strategic initiatives.

Eliminating silos and improving visibility across the enterprise

Security and IT teams are overwhelmed by the technological complexity of operating multiple tools, resulting in manual work and excessive alerts. AI can correlate threats across the entire organization, enhancing visibility and eliminating silos, thereby saving resources and reducing complexity.

With 88% of organizations favoring a platform approach over standalone solutions, many are consolidating their tech stacks in this direction. This consolidation provides native visibility across clouds, devices, communications, locations, applications, people, and third-party security tools and intelligence.

Upskilling your existing talent in AI

As revealed in the State of AI Cybersecurity Survey 2024, only 26% of cybersecurity professionals say they have a full understanding of the different types of AI in use within security products.6

Understanding AI can upskill your existing staff, enhancing their expertise and optimizing business outcomes. Human expertise is crucial for the effective and ethical integration of AI. To enable true AI-human collaboration, cybersecurity professionals need specific training on using, understanding, and managing AI systems. To make this easier, the Darktrace ActiveAI Security Platform is designed to enable collaboration and reduce the learning curve – lowering the barrier to entry for junior or less skilled analysts.  

However, to bridge the immediate expertise gap in managing AI tools, organizations can consider expert managed services that take the day-to-day management out of the SOC’s hands, allowing them to focus on training and proactive initiatives.

Conclusion

Experts predict the cybersecurity skills gap will continue to grow, increasing operational and financial risks for organizations. AI for cybersecurity is crucial for CISOs to augment their teams and scale defense capabilities with speed, scalability, and predictive insights, while human expertise remains vital for providing the intuition and problem-solving needed for responsible and efficient AI integration.

If you’re thinking about implementing AI to solve your own cyber skills gap, consider the following:

  • Select an AI cybersecurity solution tailored to your specific business needs
  • Review and streamline existing workflows and tools – consider a platform-based approach to eliminate inefficiencies
  • Make use of managed services to outsource AI expertise
  • Upskill and reskill existing talent through training and education
  • Foster a knowledge-sharing culture with access to knowledge bases and collaboration tools

Interested in how AI could augment your SOC to increase efficiency and save resources? Read our longer CISO’s Guide to Navigating the Cybersecurity Skills Shortage.

And to better understand cybersecurity practitioners' attitudes towards AI, check out Darktrace’s State of AI Cybersecurity 2024 report.

References

  1. https://www.isc2.org/research  
  2. https://www.forbes.com/advisor/education/it-and-tech/cybersecurity-statistics/  
  3. https://www.informationweek.com/cyber-resilience/the-psychology-of-cybersecurity-burnout  
  4. https://www.ibm.com/downloads/cas/1KZ3XE9D  
  5. https://www.ibm.com/downloads/cas/1KZ3XE9D  
  6. https://darktrace.com/resources/state-of-ai-cyber-security-2024
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