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Protecting global stadiums and events with Self-Learning AI

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02
Mar 2022
02
Mar 2022
This blog breaks down the challenges of securing high-profile events – including the ‘access paradox’, increasing IT and OT convergence, and the importance of a fast response – and explains how Self-Learning AI changes the game.

Stadium and large public venue operators are confronted with a unique set of cyber security challenges. Often described as a ‘honeypot’ for cyber-criminals, the entertainment industry is an attractive target for threat actors for three main reasons:

  • Hacktivism – as witnessed during the Rio and Tokyo Olympic Games;
  • The global stage of international events makes it a target for geopolitically motivated cyber-terrorism;
  • The large sums of money at stake make event organizers and associated parties a prime target for financially motivated cyber-crime like ransomware.

The potential ramifications of cyber disruption during a large-scale event cannot be overstated. A momentary lapse in access to power could bring TV broadcasts to a halt; disruption to access controls could restrict fans from entering the grounds; CCTV outages could increase the risk of criminal behavior and physical injuries. If data is not reliable and stadium machines are outputting the wrong metrics, a venue could become dangerously overcrowded. The barrier between the cyber and physical worlds has long dissolved – cyber-attacks threaten human safety.

In this blog, I explore the key challenges of stadium cyber security and explain the unique capabilities of Self-Learning AI that led me to adopt Darktrace as a head of ICT and cyber security for international venues and events.

The access paradox

The biggest challenge lies in the paradox of securing a site where various internal services are provided to a large number of unknown and uncontrolled users, suppliers and devices.

When it’s game time, or ‘D-Day’, you see a huge influx of thousands of people, each with their own devices, needing to connect to your network and your infrastructure. The floodgates are opened. But of course, certain parts of your digital environment need to remain protected: your sensitive employee and customer data, your critical OT systems. I liken this to opening the door to your home, and letting the entire town come in and wander around. But you still need to secure your master bedroom.

A multitude of different actors must be able to work on site to provide services or content during the event. Broadcasters, staff and suppliers need to have access to managing the show, and all of these people need to access or interact with the IT infrastructure. In many ways, these additional bodies are already inside the perimeter and could host unknown malicious threats.

Achieving this balance between accessibility and security requires a shift in mindset from perimeter-based security to one that can detect and respond to threats on the inside. The complexities involved requires technology that can identify malicious behavior in real time based on the wider context of an incident. A particular behavior or connection may be benign in one context and yet critically disruptive in another — tools and technology must be able to discern between the two.

This is why I considered Darktrace’s Self-Learning AI a suitable fit: rather than defending at the perimeter, it focuses on detecting and responding to malicious activity already inside. Because it learns the unique ‘patterns of life’ of its surroundings, it can detect subtle deviations that indicate a threat and initiate a targeted response – without relying on pre-programmed rules and playbooks.

IT/OT convergence

The second key challenge is the issue of IT and OT convergence. Typical stadiums and arenas consist of a wide range of Industrial Control Systems (ICS).

Figure 1: The interconnected IT/OT components of a stadium

This involves a complex and messy array of switches, cables, CCTV cameras, as well as devices and technologies being brought in by the media and the press, and all these IT and OT components are now interconnected, which means these technologies now have Internet Protocol (IP)-based threats to manage.

The same challenges that the corporate infrastructure for stadium management faces in cyber security are therefore also now an issue for ICS security.

This challenge cannot be addressed by viewing IT and OT security in isolation — these two environments are linked because of the analogue migration to IP. A unified approach is required to detect and respond to threats that start in IT before moving to industrial systems. In addition, cyber security technology must be able to deal with complexity.

Darktrace’s AI thrives in the most complex environments, with more data points adding more context to inform the AI’s decision making. It covers OT and IT with a single, unified AI engine, that can also detect and respond across cloud infrastructure, SaaS applications, email systems and endpoints. It is ready to adapt to the messy, interconnected systems that make up large stadiums’ digital infrastructure.

The time factor

Finally, the nature of stadium events means that timing is critical and puts enormous pressure on the organizers and operators. ‘D-Day’ cannot be replayed or postponed, and so if cyber disruption occurs during the event, every minute is crucial.

There is consequently a strong emphasis on two key metrics that will be familiar to the wider audience: Mean Time To Know (MTTK) — how long it takes the security team need to be aware of an incident; and Mean Time To Restore (MTTR) — how quickly a team can act to contain the threat. It is perhaps more imperative in stadium event management than anywhere else that these two metrics be minimized.

This leads to the third criteria in assessing cyber security technology: does it help with response? And critically, can that response be nuanced and targeted, able to contain that threat without causing further disruption?

To this end, Darktrace’s Autonomous Response takes machine-speed action to contain cyber-attacks, when humans are too slow to react or aren’t around at all. It’s powered by Darktrace’s AI, so it has a nuanced and continuously updating understanding of what’s ‘normal’ across IT and OT systems. This means its response actions are targeted: designed to eliminate the threat, but not at the cost of disruption. Depending on the nature and severity of the threat, the technology can block specific malicious connections by enforcing the normal ‘pattern of life’ of a device or account. When every second counts, this is the speed and granularity that you need in a cyber security technology.

Plug and play

For stadiums and large venue operators, Darktrace’s trial period is typically extended for the AI to learn ‘normal’ over a longer period of time, capturing both ‘business as usual’, and ‘event time’. The sophistication of the AI enables it to factor event day into its understanding of ‘normal’.

When event day comes around, the technology has a nuanced understanding of how every user and device typically behaves, and can identify subtle deviations indicative of a threat.

It can be deployed across every area of the digital enterprise – including email, adding an invaluable layer of defense as any new event will entail thousands of email exchanges with new senders to prepare for the event, adding to the propagation risk of viruses or ransomware. It also covers cloud and SaaS environments with the same self-learning approach, stopping anomalous behaviors that point to account takeover and other cloud-based threats.

Wherever it is deployed, Darktrace allows the stadium operator to focus on the vital part of the game and offers real-time protection without any modification in the network topology or infrastructure.

An adaptive defense

Cyber-criminals are constantly developing their approach in an attempt to evade security tools trained to look for specific hallmarks of an attack. As they get creative and continuously experiment with new tactics and techniques, the human operators using these tools are forced into a constant state of catch up.

Figure 2: Cyber security is an evolving game of attack and defense

An AI-based approach that learns an organization from the ground up puts an end to this game of ‘cat and mouse’, shifting the balance in favor of the defenders and allowing them to stay ahead of the threat.

With a nuanced understanding of what’s ‘normal’ for the business, unified IT/OT coverage, and an Autonomous Response solution that takes immediate, targeted action, the playing field is levelled and large stadium and events operators can focus on delivering the best possible experience for attendees, digital viewers, partners and performers.

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
Karim Benslimane
VP, Cyber Intelligence

Karim Benslimane is Darktrace’s VP of Cyber Intelligence, working with clients in the public and private sector to analyse the most sophisticated cyber-threats today, and advising security professionals on the employment of artificial intelligence to strengthen their defensive strategy. Karim is a technical specialist in cyber and counter-terrorism exercises with over two decades of experience defending the sports and event industry from sophisticated threats. He has led major IT and cyber security projects for international arenas and events such as the Football World Cups, Rugby World Cups, World Athletics Championships and over 500 events.

Karim is also Lieutenant-Colonel (RC) at the Command of the Gendarmerie in Cyberspace, also known as ComCyber-MI, in charge with steering, leading and coordinating the French Gendarmerie Nationale's efforts to combat cyberthreats in the areas of prevention, monitoring of digital spaces and judicial investigation of cybercriminal organisations.

Karim is based in Middle East.

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Lost in Translation: Darktrace Blocks Non-English Phishing Campaign Concealing Hidden Payloads

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15
May 2024

Email – the vector of choice for threat actors

In times of unprecedented globalization and internationalization, the enormous number of emails sent and received by organizations every day has opened the door for threat actors looking to gain unauthorized access to target networks.

Now, increasingly global organizations not only need to safeguard their email environments against phishing campaigns targeting their employees in their own language, but they also need to be able to detect malicious emails sent in foreign languages too [1].

Why are non-English language phishing emails more popular?

Many traditional email security vendors rely on pre-trained English language models which, while function adequately against malicious emails composed in English, would struggle in the face of emails composed in other languages. It should, therefore, come as no surprise that this limitation is becoming increasingly taken advantage of by attackers.  

Darktrace/Email™, on the other hand, focuses on behavioral analysis and its Self-Learning AI understands what is considered ‘normal’ for every user within an organization’s email environment, bypassing any limitations that would come from relying on language-trained models [1].

In March 2024, Darktrace observed anomalous emails on a customer’s network that were sent from email addresses belonging to an international fast-food chain. Despite this seeming legitimacy, Darktrace promptly identified them as phishing emails that contained malicious payloads, preventing a potentially disruptive network compromise.

Attack Overview and Darktrace Coverage

On March 3, 2024, Darktrace observed one of the customer’s employees receiving an email which would turn out to be the first of more than 50 malicious emails sent by attackers over the course of three days.

The Sender

Darktrace/Email immediately understood that the sender never had any previous correspondence with the organization or its employees, and therefore treated the emails with caution from the onset. Not only was Darktrace able to detect this new sender, but it also identified that the emails had been sent from a domain located in China and contained an attachment with a Chinese file name.

The phishing emails detected by Darktrace sent from a domain in China and containing an attachment with a Chinese file name.
Figure 1: The phishing emails detected by Darktrace sent from a domain in China and containing an attachment with a Chinese file name.

Darktrace further detected that the phishing emails had been sent in a synchronized fashion between March 3 and March 5. Eight unique senders were observed sending a total of 55 emails to 55 separate recipients within the customer’s email environment. The format of the addresses used to send these suspicious emails was “12345@fastflavor-shack[.]cn”*. The domain “fastflavor-shack[.]cn” is the legitimate domain of the Chinese division of an international fast-food company, and the numerical username contained five numbers, with the final three digits changing which likely represented different stores.

*(To maintain anonymity, the pseudonym “Fast Flavor Shack” and its fictitious domain, “fastflavor-shack[.]cn”, have been used in this blog to represent the actual fast-food company and the domains identified by Darktrace throughout this incident.)

The use of legitimate domains for malicious activities become commonplace in recent years, with attackers attempting to leverage the trust endpoint users have for reputable organizations or services, in order to achieve their nefarious goals. One similar example was observed when Darktrace detected an attacker attempting to carry out a phishing attack using the cloud storage service Dropbox.

As these emails were sent from a legitimate domain associated with a trusted organization and seemed to be coming from the correct connection source, they were verified by Sender Policy Framework (SPF) and were able to evade the customer’s native email security measures. Darktrace/Email; however, recognized that these emails were actually sent from a user located in Singapore, not China.

Darktrace/Email identified that the email had been sent by a user who had logged in from Singapore, despite the connection source being in China.
Figure 2: Darktrace/Email identified that the email had been sent by a user who had logged in from Singapore, despite the connection source being in China.

The Emails

Darktrace/Email autonomously analyzed the suspicious emails and identified that they were likely phishing emails containing a malicious multistage payload.

Darktrace/Email identifying the presence of a malicious phishing link and a multistage payload.
Figure 3: Darktrace/Email identifying the presence of a malicious phishing link and a multistage payload.

There has been a significant increase in multistage payload attacks in recent years, whereby a malicious email attempts to elicit recipients to follow a series of steps, such as clicking a link or scanning a QR code, before delivering a malicious payload or attempting to harvest credentials [2].

In this case, the malicious actor had embedded a suspicious link into a QR code inside a Microsoft Word document which was then attached to the email in order to direct targets to a malicious domain. While this attempt to utilize a malicious QR code may have bypassed traditional email security tools that do not scan for QR codes, Darktrace was able to identify the presence of the QR code and scan its destination, revealing it to be a suspicious domain that had never previously been seen on the network, “sssafjeuihiolsw[.]bond”.

Suspicious link embedded in QR Code, which was detected and extracted by Darktrace.
Figure 4: Suspicious link embedded in QR Code, which was detected and extracted by Darktrace.

At the time of the attack, there was no open-source intelligence (OSINT) on the domain in question as it had only been registered earlier the same day. This is significant as newly registered domains are typically much more likely to bypass gateways until traditional security tools have enough intelligence to determine that these domains are malicious, by which point a malicious actor may likely have already gained access to internal systems [4]. Despite this, Darktrace’s Self-Learning AI enabled it to recognize the activity surrounding these unusual emails as suspicious and indicative of a malicious phishing campaign, without needing to rely on existing threat intelligence.

The most commonly used sender name line for the observed phishing emails was “财务部”, meaning “finance department”, and Darktrace observed subject lines including “The document has been delivered”, “Income Tax Return Notice” and “The file has been released”, all written in Chinese.  The emails also contained an attachment named “通知文件.docx” (“Notification document”), further indicating that they had been crafted to pass for emails related to financial transaction documents.

 Darktrace/Email took autonomous mitigative action against the suspicious emails by holding the message from recipient inboxes.
Figure 5: Darktrace/Email took autonomous mitigative action against the suspicious emails by holding the message from recipient inboxes.

Conclusion

Although this phishing attack was ultimately thwarted by Darktrace/Email, it serves to demonstrate the potential risks of relying on solely language-trained models to detect suspicious email activity. Darktrace’s behavioral and contextual learning-based detection ensures that any deviations in expected email activity, be that a new sender, unusual locations or unexpected attachments or link, are promptly identified and actioned to disrupt the attacks at the earliest opportunity.

In this example, attackers attempted to use non-English language phishing emails containing a multistage payload hidden behind a QR code. As traditional email security measures typically rely on pre-trained language models or the signature-based detection of blacklisted senders or known malicious endpoints, this multistage approach would likely bypass native protection.  

Darktrace/Email, meanwhile, is able to autonomously scan attachments and detect QR codes within them, whilst also identifying the embedded links. This ensured that the customer’s email environment was protected against this phishing threat, preventing potential financial and reputation damage.

Credit to: Rajendra Rushanth, Cyber Analyst, Steven Haworth, Head of Threat Modelling, Email

Appendices  

List of Indicators of Compromise (IoCs)  

IoC – Type – Description

sssafjeuihiolsw[.]bond – Domain Name – Suspicious Link Domain

通知文件.docx – File - Payload  

References

[1] https://darktrace.com/blog/stopping-phishing-attacks-in-enter-language  

[2] https://darktrace.com/blog/attacks-are-getting-personal

[3] https://darktrace.com/blog/phishing-with-qr-codes-how-darktrace-detected-and-blocked-the-bait

[4] https://darktrace.com/blog/the-domain-game-how-email-attackers-are-buying-their-way-into-inboxes

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Rajendra Rushanth
Cyber Analyst

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The State of AI in Cybersecurity: The Impact of AI on Cybersecurity Solutions

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13
May 2024

About the 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” which was an overview of the entire report. This blog will focus on one aspect of the overarching report, the impact of AI on cybersecurity solutions.

To access the full report, click here.

The effects of AI on cybersecurity solutions

Overwhelming alert volumes, high false positive rates, and endlessly innovative threat actors keep security teams scrambling. Defenders have been forced to take a reactive approach, struggling to keep pace with an ever-evolving threat landscape. It is hard to find time to address long-term objectives or revamp operational processes when you are always engaged in hand-to-hand combat.                  

The impact of AI on the threat landscape will soon make yesterday’s approaches untenable. Cybersecurity vendors are racing to capitalize on buyer interest in AI by supplying solutions that promise to meet the need. But not all AI is created equal, and not all these solutions live up to the widespread hype.  

Do security professionals believe AI will impact their security operations?

Yes! 95% of cybersecurity professionals agree that AI-powered solutions will level up their organization’s defenses.                                                                

Not only is there strong agreement about the ability of AI-powered cybersecurity solutions to improve the speed and efficiency of prevention, detection, response, and recovery, but that agreement is nearly universal, with more than 95% alignment.

This AI-powered future is about much more than generative AI. While generative AI can help accelerate the data retrieval process within threat detection, create quick incident summaries, automate low-level tasks in security operations, and simulate phishing emails and other attack tactics, most of these use cases were ranked lower in their impact to security operations by survey participants.

There are many other types of AI, which can be applied to many other use cases:

Supervised machine learning: Applied more often than any other type of AI in cybersecurity. Trained on attack patterns and historical threat intelligence to recognize known attacks.

Natural language processing (NLP): Applies computational techniques to process and understand human language. It can be used in threat intelligence, incident investigation, and summarization.

Large language models (LLMs): Used in generative AI tools, this type of AI applies deep learning models trained on massively large data sets to understand, summarize, and generate new content. The integrity of the output depends upon the quality of the data on which the AI was trained.

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies. With the correct models, this AI can use anomaly-based detections to identify all kinds of cyber-attacks, including entirely unknown and novel ones.

What are the areas of cybersecurity AI will impact the most?

Improving threat detection is the #1 area within cybersecurity where AI is expected to have an impact.                                                                                  

The most frequent response to this question, improving threat detection capabilities in general, was top ranked by slightly more than half (57%) of respondents. This suggests security professionals hope that AI will rapidly analyze enormous numbers of validated threats within huge volumes of fast-flowing events and signals. And that it will ultimately prove a boon to front-line security analysts. They are not wrong.

Identifying exploitable vulnerabilities (mentioned by 50% of respondents) is also important. Strengthening vulnerability management by applying AI to continuously monitor the exposed attack surface for risks and high-impact vulnerabilities can give defenders an edge. If it prevents threats from ever reaching the network, AI will have a major downstream impact on incident prevalence and breach risk.

Where will defensive AI have the greatest impact on cybersecurity?

Cloud security (61%), data security (50%), and network security (46%) are the domains where defensive AI is expected to have the greatest impact.        

Respondents selected broader domains over specific technologies. In particular, they chose the areas experiencing a renaissance. Cloud is the future for most organizations,
and the effects of cloud adoption on data and networks are intertwined. All three domains are increasingly central to business operations, impacting everything everywhere.

Responses were remarkably consistent across demographics, geographies, and organization sizes, suggesting that nearly all survey participants are thinking about this similarly—that AI will likely have far-reaching applications across the broadest fields, as well as fewer, more specific applications within narrower categories.

Going forward, it will be paramount for organizations to augment their cloud and SaaS security with AI-powered anomaly detection, as threat actors sharpen their focus on these targets.

How will security teams stop AI-powered threats?            

Most security stakeholders (71%) are confident that AI-powered security solutions are better able to block AI-powered threats than traditional tools.

There is strong agreement that AI-powered solutions will be better at stopping AI-powered threats (71% of respondents are confident in this), and there’s also agreement (66%) that AI-powered solutions will be able to do so automatically. This implies significant faith in the ability of AI to detect threats both precisely and accurately, and also orchestrate the correct response actions.

There is also a high degree of confidence in the ability of security teams to implement and operate AI-powered solutions, with only 30% of respondents expressing doubt. This bodes well for the acceptance of AI-powered solutions, with stakeholders saying they’re prepared for the shift.

On the one hand, it is positive that cybersecurity stakeholders are beginning to understand the terms of this contest—that is, that only AI can be used to fight AI. On the other hand, there are persistent misunderstandings about what AI is, what it can do, and why choosing the right type of AI is so important. Only when those popular misconceptions have become far less widespread can our industry advance its effectiveness.  

To access the full report, click here.

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