Qatar World Cup 2022 Protected by Darktrace AI Cybersecurity
Discover how Darktrace's AI technology safeguarded the Qatar World Cup 2022 from cyber threats. Learn more about cutting-edge cybersecurity measures today!
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.
Written by
Karim Benslimane
VP, Field CISO
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15
Jan 2023
Qatar World Cup 2022 was the fifth world cup (football and rugby) I have been closely involved in from the operation and cyber security standpoint. Over the last two decades, I have witnessed a dramatic shift in the cyber landscape.
A few years back, the main challenge was to mitigate technical issues due to failures or human error by increasing the resilience with high-availability and failover design. Today, with the increased complexity of the digital infrastructure underpinning global tournaments, and the sophistication and ferocity of the threat actors (ransomware gangs, hacktivists, APT groups) seeking to disrupt them, it is no surprise that cyber security has been pushed to the top of organizers’ agendas.
This football World Cup represented a challenge like no other. The tournament introduced the world’s first ‘connected stadium’ concept whereby all eight stadiums were managed by a single unified technology from the state-of-the-art Aspire Command Centre in Doha.
Figure 1: The connected stadium concept visualized
The centre – described as the most sophisticated setup ever seen at a sporting event – managed everything, from the lighting and access gates through to communications and IT. This unified integrated technology ecosystem offers the potential to drastically increase efficiency and gave the ability to seamlessly manage multiple matches at once. Each of the eight stadiums has a ‘digital twin’, allowing the cyber security experts to detect and mitigate issues as and when they arise.
Figure 2: The Aspire Command Centre
The organizer realized the importance of protecting a digital infrastructure of this scale and complexity from attempted cyber-attacks. A football World Cup draws in a global audience – an estimated 3.75 billion were said to have tuned in for the previous final. It is difficult to overstate the financial and reputational impacts of disruption to any game – whether that be to the turnstiles within the stadium or the broadcast of the game – due to a cyber incident. Hacktivists and other cyber-criminals are acutely aware of the global stage a tournament like this provides and so these events become an obvious target for threats such as Distributed-Denial-of-Service (DDoS) and ransomware attacks.
Furthermore, the interconnectivity between IT and OT systems means that the line between cyber security and physical safety is significantly blurred. For example, having your access control and CCTV malfunctioning may lead to overcrowding within parts of stadium and leave fans vulnerable to crushes and physical injuries.
Initially, the World Cup organizer was looking to improve OT visibility. They quickly recognized that Darktrace’s technology could take them a step further than any other solutions on the market. Darktrace AI is uniquely able to monitor and protect their OT and their IT, detect unusual behaviors, and mitigate cyber-threats, and present its findings in a single pane of glass.
The host country recognized that a best-in-class event needed best-in-class technology. The nature of international 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. Darktrace was selected not only because of its unified IT and OT coverage, but because of its ability to detect, investigate, and respond at machine speed.
In the end, Darktrace played a crucial role in protecting the tournament across all eight stadiums throughout the World Cup. Supplementing the value of the AI, our team was on the ground, working alongside the cyber security team to assist with investigations. The teamwork and collaboration were second-to-none and the energy in the Command Centre was palpable when Darktrace was able to spot events of interest that would have otherwise gone under the radar.
On game day, every second counts, so pairing people with the right technology is critical. Explainable AI really came into its own during the World Cup, rapidly synthesizing information about disparate events, and generating alerts in seconds about emerging threats. That meant the team had the information they needed at their fingertips in an easily-understand format.
Our AI technology, created in 2013 in our Cambridge AI Research Centre, has disrupted the cyber security industry, and is making a big impact in the real world: from financial services and education through to critical national infrastructure like utilities, energy suppliers, and healthcare. The Qatar World Cup 2022 provided a unique and high-profile challenge. Darktrace didn’t just successfully protect the World Cup against cyber-attackers; it protected the more than 1.4 million people entering the stadiums from physical risk arising from OT attacks.
In all likelihood, you probably watched this year’s World Cup engrossed in the games, without giving much of a thought to cyber security. That’s the funny thing about success in the cyber security world: if all goes well, the average person wouldn’t even know it.
We are incredibly proud to have helped defend the Qatar World Cup 2022. I would like to congratulate the organizer and all security team members involved for delivering a World Cup free from cyber disruption, allowing fans both on site and the billions watching at home to simply enjoy the action on the pitch.
Learn more about how Darktrace helped protect the World Cup: Watch the video.
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.
Beyond the Fridge: Exposed Industrial Control Systems
Following the cyber attack on supermarket refrigeration systems in France, Darktrace researchers looked at how many industrial devices are sitting exposed on the public internet. From fridge controllers to PLCs and remote gateways, many rely on weak or default credentials.
Botnet Behind the Camera: Mirai Katana Activity on a Video Recording Device
Darktrace identified Mirai Katana botnet activity on a camera device in a sports-sector network, showing how compromised IoT devices can facilitate malware delivery, botnet activity, and data exfiltration from inside trusted environments.
A stadium during a major sporting event is simultaneously a city, a broadcast hub, a transport node, a public safety space, and a national symbol. That makes them attractive not just to criminals, but to politically motivated and state‑aligned threat actors willing to plan years ahead. With the 2026 FIFA World Cup spanning three nations and dozens of host cities, those challenges – and the AI now amplifying them – have never carried higher stakes.
From One Alert to a Wider Campaign: Uncovering Novel Threats Through Behavioral Analysis
Key Findings
Darktrace’s Threat Research team is exploring a new method to identify malicious campaigns by correlating similar anomalous activity across its customer fleet.
The method uses unsupervised clustering to identify shared behavioral patterns across multiple customer deployments without relying on prior indicators of compromise (IoCs).
The approach identified shared infrastructure across dozens of customers, with activity linked to the likely POPA botnet.
A second cluster identified anomalous activity associated with Monero cryptocurrency mining.
How does Darktrace conduct threat research?
The Darktrace Threat Research team aims to identify malicious campaign activity affecting multiple customers. By their nature, these campaigns will lead to shared behavioral patterns across multiple victims.
In this piece, we describe a method for identifying malicious campaign activity that uses this simple observation as a guiding principle. We begin by outlining a few key facts about the modern threat landscape and why behavioral detection is necessary to meet the challenges it presents. We then explore how fleet-wide behavioral detections can be leveraged using the principle above to systematically identify multi-deployment campaigns impacting the Darktrace customer fleet. Finally, we demonstrate that this research is not purely theoretical, using real examples of campaign activity detected through this novel approach.
Shifting to a behavioral-focused approach
The modern era of cyber-defense necessitates detection capabilities beyond traditional signature-based methods. Technologies such as antivirus signatures, hash-based malware identification, and IP blocklists have historically been successful in defending against known threats. However, they are easily circumvented by attackers and inherently limited in defending against the novel attack techniques that today's threat landscape is producing.
Recent breakthroughs in AI represent a transformational risk multiplier that cannot be ignored. AI systems do not merely execute existing attacks at machine speed; there are signs they can also synthesize novel tradecraft [1], increasing the likelihood that defenders will encounter attack patterns that have never previously been observed. Meanwhile vulnerability disclosures continue to accelerate. More than 48,000 CVEs were published in 2025, and published statistics to date indicate that 2026 is already on track to significantly exceed that figure [2]. While multiple factors may contribute to vulnerability discovery, AI is seen as a key factor in this rapid growth. Organizations should expect a growing proportion of malicious activity to fall outside the scope of detections that rely solely on prior knowledge of known threats.
By design, Darktrace’s behavioral and anomaly-focused detection capabilities are well-suited to this testing landscape. With a focus on detecting unknown and unexpected activity that deviates from the baseline of “normal” behavior, detection of CVE exploitation without prior knowledge of the vulnerability and before public disclosure, for example, is achievable [3][4].
Darktrace’s diverse customer base benefits from a further layer of protection: working with only deidentified alert metadata, our threat researchers hunt proactively across the fleet. Beyond the level of the individual customer, there is a second, larger vantage to be leveraged.
Darktrace data provides unique insight into the unexpected behavioral patterns that occur across its customer fleet. What can we see if we look at this fleet-wide telemetry and hunt for systematic behavioral campaigns that impact our customer base?
Correlating across the customer base
At the deployment level, Darktrace focuses on whether activity is unusual in the context of an organization's normal behavior, but as we zoom out and look at these alerts at the fleet level, the problem fundamentally changes . We are no longer seeking anomalies in largely normal data; we are seeking systematic patterns within data already known to be anomalous. An isolated behavior anomaly may be notable for a single customer; the same fingerprint across dozens is a signal of a structured campaign. Hunting for such campaigns and sharing our findings are key areas of focus for Darktrace’s Threat Research team. In this spirit, Darktrace is actively exploring a a new research approach that leverages deidentified alert metadata to increase campaign detection ability.
This approach is based on the premise that an expert human analyst presented with a set of alerts will identify likely related activity by assessing whether the metadata fields indicate a relationship. For example, two connections to a rare endpoint that exhibits naming structure that is likely from a single algorithm are a strong indicator, as is the use of an unusual port carrying a given protocol (e.g. port 6000 for SSL). Darktrace’s approach to campaign detection encodes this expert human judgement process through deterministic embedding functions that obtain reproducible vector representations of alert data. Unsupervised clustering is then applied to these vector representations to obtain groups of alerts that describe semantically similar alert data across multiple customers. These clusters therefore represent similar anomalous activity that has occurred across multiple customers. In other words, these clusters are candidates for malicious campaign activity.
Figure 1: Layers of AI-based analysis provided through Darktrace products and cross-customer correlation.
Case study 1 – unusual connections to shared underlying infrastructure
In one recent case, the clustering produced several tight groups of alerts spanning dozens of customers. Each group centered on a single domain whose subdomains were algorithmically generated (e.g. s01699, s1736, s91). Examined side by side, these clusters shared an unmistakable behavioral shape: each involved a domain with a clear naming pattern (e.g. s016841786.net-echo[.]com, s1670.pixellog[.]io, s217.pixellog[.]io) , with connections over SSL on the unusual port 6000 to servers hosted by non-mainstream providers (e.g. OVH, Hetzner, GTHost, Leaseweb), rather than generally expected content-delivery networks.
Figure 2: An example model alert triggering on unusual outbound connections and data transfer to s1670.pixellog[.]io.
Closer inspection revealed that the same subdomain label appeared under different domains (e.g. s1786.net-echo[.]com and s1786.pixellog[.]io). Following this lead, Darktrace’s threat researchers pivoted across the customer base and found numerous distinct subdomain labels reused under more than one registered domain. In several cases, the same label resolved to the same IP across different domains (e.g. s1704 under both pixellog[.]io and net-echo[.]com → 148.113.222[.]244; s01684 → 15.235.67[.]95), suggesting the use of ephemeral subdomain infrastructure in an attempt to avoid signature-based defense techniques.
This example demonstrates how multi-deployment data can uncover shared infrastructure that may be difficult to identify at the individual deployment level alone. In particular, the presented method facilitated this discovery by producing coherent, tight clusters of geometrically similar vector embeddings of malicious activity from large-scale fleet data, which could then be presented to a researcher for further investigation. This activity highlighted across the Darktrace customer base was ultimately linked to likely POPA botnet activity through use of open-source intelligence (OSINT) [5].
Case Study 2 – anomalous user agent and txt URI
A second example shows the method surfacing a single, self-contained cluster. The clustering was not given any indicators to look for; instead, it grouped devices that shared an unusual combination of features. On inspection, that shared consisted of:
- unusual user agent (tes)
- specific request path (/md5_GIP.txt)
- uncommon destination port (83)
- hostname impersonating Google (matching regular expression ^www\.google\.com\.[a-zA-Z0-9]+\.fast-dns-host\.com$)
Treating these shared features as IoCs, Darktrace’s threat researchers then pivoted to an exploration across the customer base to investigate the full breadth of the campaign. This search identified further commonalities; additional domains which shared the same IP as the domain family highlighted in the cluster, as well as evidence of cryptocurrency mining on affected devices. Additional OSINT research also aligned with these findings, linking the discovered payloads and infrastructure to Monero cryptocurrency mining [6].
Conclusion
The method outlined in this blog is under active development, with ongoing technical refinement providing opportunities to further enhance its capabilities. While the capability continues to mature, this approach to threat hunting leverages the powerful vantage point of fleetwide telemetry across a diverse customer base to help protect Darktrace customers. This method allows campaigns that at the deployment level appear as isolated alerts to be seen at their true scale and spread. It enables proactive fleet-wide analysis without prior IoCs to identify campaigns impacting Darktrace customers.
This is possible because of what the method is built on: the atomic unit is the Darktrace alert, a signal that encodes years of cyber expertise, while the multi-deployment nature of the method allows organizations to indirectly benefit from findings from other networks through shared intelligence by Darktrace’s Threat Research team.
Credit to Daniel Levy (Threat Hunting Data Scientist), Paul Jennings (Principal Cyber Analyst), Emma Foulger (Global Head, Threat Operations)
“I wonder if running that tool influenced the connectivity in some way.”
“I might need to target down or reboot, but first, I'll investigate the network using ARP. I wonder if ARP is responding properly. If I see "ip neigh stale," that could indicate a problem. I think arping might help in this situation, but I’ll need to keep an eye on how the network is behaving. It's all about figuring out where the issue lies!”
図10：エージェントの異常なLDAPアクティビティに対してトリガーされたこのLinuxサーバーに対するモデル検知は、Cyber AI Analystによって自律的に調査され、その調査結果にはエージェントの異常なSMBセッションアクティビティに関する内容が含まれ、Cyber AIAnalystインシデントに記録されました。
DarktraceのCyber AI Analystは、エージェントのアクティビティによってトリガーされたモデル検知を自律的に調査し、その調査結果を1つの重要なインシデントレポートにまとめました。インシデント報告書の内容は、エージェントによるネットワークスキャンから始まりコーディング演習内容のSMBベースの上書きで終わる、エージェントの行動の各段階を詳しくカバーしています。
図20：Cyber AI Analystは、モデル検知に対する自律的な調査を通じて得られた所見をつなぎ合わせ、エージェントの進行状況のタイムラインを作成し、アクティビティを視覚的に表したグラフとともに提供しました。
Cyber Verification Program下のAnthropic Opus 4.6モデルなど、他のフロンティアモデルを使用したエージェントも、Darktraceのシミュレートされた環境で同じ設定とタスクを使ってテストされました。反復の度に、エージェントは演習の内容または採点プログラムを変更して不正を行おうとしました。基盤となるモデルに関係なく、エージェントは自分が置かれた環境を侵害することで、100％のスコアを達成する方法を見つけることに成功していました。これらのエージェントによるハッキングは、アクティビティの内容こそ違っていましたが、すべてその環境のベースラインの振る舞いから大きく逸脱した動作を引き起こしていました。これらの逸脱はDarktraceプラットフォームに十分な検知の機会を提供し、エージェントの逃げ場はありませんでした。
