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September 23, 2020

Detecting OT Threats: ICS Attack at International Airport

Learn how Darktrace's OT Threat Detection technology identified a sophisticated ICS attack on an international airport. Read more on Darktrace's blog.
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.
Written by
David Masson
VP, Field CISO
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23
Sep 2020

As Industrial Control Systems (ICS) and traditional IT networks converge, the number of cyber-attacks that start in the corporate network before spreading to operational technology has increased dramatically in the last 12 months. From North Korean hackers targeting a nuclear power plant in India to ransomware shutting down operations at a US gas facility, and across Honda’s manufacturing sites, 2020 has been the year OT attacks have become mainstream.

Darktrace recently detected a simulation of a state-of-the-art attack at an international airport, identifying ICS reconnaissance, lateral movement, vulnerability scanning and protocol fuzzing – a technique in which the attacker sends nonsensical commands over an ICS communication channel in order to confuse the target device, causing it to fail or reboot.

Darktrace’s Industrial Immune System detected every stage of the sophisticated attack, using AI-powered anomaly detection to identify ICS attack vectors without a list of known exploits, company assets, or firmware versions. The attacker leveraged tools at every stage of the ICS kill chain, including ICS-specific attack techniques.

Any unusual attempts to read or reprogram single coils, objects, or other data blocks were detected by Cyber AI, and Darktrace’s Cyber AI Analyst also automatically identified the activity and created summary reports detailing the key actions taken.

The attack spanned multiple days and targeted the Building Management System (BMS) and the Baggage Reclaim network, with attackers utilizing two common ICS protocols (BacNet and S7Comm) and leveraging legitimate tools (such as ICS reprogramming commands and connections through SMB service pipes) to evade traditional, signature-based security tools.

Attack details

Figure 1: Timeline of the attack

In the first stage of the attack, a new device was introduced to the network, using ARP spoofing to evade detection from traditional security tools. At 11.40am, the attacker scanned a target device and attempted to bruteforce open services. Once the target device had been hijacked, the attacker then sought to establish an external connection to the Internet. External connections should not be possible in ICS networks, but attackers often seek to bypass firewalls and network segregation rules in order to create a command and control (C2) channel.

Figure 2: Darktrace Threat Tray 15 minutes after the pentest commenced. High level model breaches have already alerted the analyst team to the attack device.

The hijacked device then began performing ICS reconnaissance using Discover and Read commands. Darktrace identified new objects and data blocks being targeted as part of this reconnaissance, and detected ICS devices targeted with unusual BacNet and Siemens S7Comm protocol commands.

Figure 3: Model alerts associated with ICS reconnaissance over BacNet. Machine learning at the ICS command level detected new and unusual BacNet objects being targeted by the attacker.

The attacker enumerated through multiple ICS devices in order to perform lateral movement throughout the ICS system. Once they had learned device settings and configurations, they used ICS Reprogram and Write commands to reconfigure machines. The attacker attempted to use known vulnerabilities to exploit the target devices, such as the use of SMB, SMBv1, HTTP, RDP, and ICS protocol fuzzing.

Figure 4: Visualization of the device enumeration performed by the attacker against multiple ICS controllers. The attacker used ICS Discover commands as part of the initial reconnaissance.

The attacker took deliberate actions to evade the airport’s cyber security stack, including making connections using ICS protocols commonly used on the network to devices which commonly use those protocols. While legacy security tools failed to pick up on this activity, Darktrace’s deep packet inspection was able to identify unusual commands used by the attacker within those ‘normal’ connections.

The attacker used ARP spoofing to slow any investigation using asset management-based security tools – including two other solutions being trialed by the airport at the time of the attack. They also used multiple devices throughout the intrusion to throw defense teams off the scent.

Darktrace’s AI technology also launched an automated investigation into the incident. The Cyber AI Analyst identified all of the attack devices and produced summary reports for each, showcasing its ability to not only save crucial time for security teams, but bridge the skills gap between IT teams and ICS engineers.

Figure 5: The Cyber AI Analyst threat tray at the end of day 1. Both devices used by the attacker have been identified.

The Cyber AI Analyst immediately began investigating after the first model breach, and continued to stitch together disparate events across the network to produce a natural language summary of the incident, including recommendations for action.

Figure 6: AIA incident summary at the end of day 2, detailing the use of SMB exploits as part of the attack chain against one of the ICS devices.

Potential ramifications

Had the attack been allowed to continue, the attackers – potentially activist groups, terrorist organizations, and organized criminals – could have caused significant operational disruption to the airport. For example, the BMS is likely to manage temperature settings, the sprinkler system, fire alarms and fire exits, lighting, and doors in and out of secure access areas. Meddling with any one of these could cause severe disruption at an airport, with significant financial and reputational effects. Similarly, access to baggage reclaim networks could be used by criminals seeking to smuggle illegal goods or steal valuable cargo.

This simulation showcases the possibilities for an advanced cyber-criminal looking to compromise integrated IT and OT networks. The majority of leading ICS ‘security’ vendors are signature-based, and fail to pick up on novel techniques and utilization of common protocols to pursue malicious ends – this is why ICS attacks have continued to hit the headlines this year.

The incident showcases the extent of Cyber AI’s detections in a real-world ICS environment, and the level of detail Darktrace can provide following an attack. As Industrial Control Systems become increasingly integrated with the wider IT network, the importance of securing these critical systems is paramount. Darktrace provides a unified security umbrella with visibility and detection across the entire digital environment.

Thanks to Darktrace analyst Oakley Cox for his insights on the above investigation.

Learn more about the Industrial Immune System

Darktrace model detections:

  • ICS / Unusual ICS Commands
  • ICS / Multiple New Reprograms
  • ICS / Multiple New Discover Commands
  • ICS / Rare External from OT Device
  • ICS / Uncommon ICS Protocol Warning
  • ICS / Multiple Failed Connections to ICS Device
  • ICS / Anomalous IT to ICS Connection
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.
Written by
David Masson
VP, Field CISO

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April 17, 2026

中国系サイバー作戦の進化 - それはサイバーリスクおよびレジリエンスにとって何を意味するか

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サイバーセキュリティにおいては、これまではインシデント、侵害、キャンペーン、そして脅威グループを中心にリスクを整理してきました。これらの要素は現在も重要です -しかし個別のインシデントにとらわれていては、エコシステム全体の形成を見逃してしまう危険があります。国家が支援する攻撃者グループは、個別の攻撃を実行したり短期的な目標を達成したりするためだけではなく、サイバー作戦を長期的な戦略上の影響力を構築するために使用するようになっています。  

当社の最新の調査レポート、Crimson Echoにおいてもこうした状況にあわせて視点を変えています。キャンペーンやマルウェアファミリー、あるいはアクターのラベルを個別のイベントとして分類するのではなく、ダークトレースの脅威調査チームは中国系グループのアクティビティを長期的に連続した行動として分析しました。このように視野を拡大することで、これらの攻撃者がさまざまな環境内でどのように存在しているか、すなわち、静かに、辛抱強く、持続的に、そして多くのケースにおいて識別可能な「インシデント」が発生するかなり前から下準備をしている様子が明らかになりました。  

中国系サイバー脅威のこれまでの変化

中国系サイバーアクティビティは過去20年間において4つのフェーズで進化してきたと言えます。初期の、ボリュームを重視したオペレーションは1990年代にから2000年代初めに見られ、それが2010年代にはより構造化された、戦略に沿った活動となり、そして現在の高度な適応性を備えた、アイデンティティを中心とした侵入へと進化しています。  

現在のフェーズの特徴は、大規模、攻撃の自制、そして永続化です。攻撃者はアクセスを確立し、その戦略的価値を評価し、維持します。これはより全体的な変化を反映したものです。つまりサイバー作戦は長期的な経済的および地政学的戦略に組み込まれる傾向が強まっているということです。デジタル環境へのアクセス、特に国家の重要インフラやサプライチェーン、先端テクノロジーにつながるものは、ある種の長期的な戦略的影響力と見られるようになりました。  

複雑な問題に対するダークトレースのビヘイビア分析アプローチ

国家が支援するサイバーアクティビティを分析する際、難しい問題の1つはアトリビューションです。従来のアプローチは多くの場合、特定の脅威グループ、マルウェアファミリー、あるいはインフラに判定を依存していました。しかしこれらは絶えず変化するものであり、さらに中国系オペレーションの場合、しばしば重複が見られます。

Crimson Echo は2022年7月から2025年9月の間の3年間にDarktrace運用環境で観測された異常なアクティビティを回顧的に分析した結果です。ビヘイビア検知、脅威ハンティング、オープンソースインテリジェンス、および構造化されたアトリビューションフレームワーク(Darktrace Cybersecurity Attribution Framework)を用いて、数十件の中~高確度の事例を特定し、繰り返し発生しているオペレーションのパターンを分析しました。  

この長期的視野を持ったビヘイビア中心型アプローチにより、ダークトレースは侵入がどのように展開していくかについての一定のパターンを特定することができ、動作のパターンが重要であることがあらためて確認されました。  

データが示していること

分析からいくつかの明確な傾向が浮かび上がりました:

  • 標的は戦略的に重要なセクターに集中していたのです。データセット全体で、侵入の88%は重要インフラと分類される、輸送、重要製造業、政府、医療、ITサービスを含む組織で発生しています。   
  • 戦略的に重要な西側経済圏が主な焦点です。米国だけで、観測されたケースの22.5%を占めており、ドイツ、イタリア、スペイン、および英国を含めた主要なヨーロッパの経済圏と合わせると侵入の半数以上(55%)がこれらの地域に集中しています。  
  • 侵入の63%近くがインターネットに接続されたシステムのエクスプロイトから始まっており、外部に露出したインフラの持続的リスクがあらためて浮き彫りになりました。  

サイバー作戦の2つのモデル

データセット全体で、中国系のアクティビティは2つの作戦モデルに従っていることが確認されました。  

1つ目は“スマッシュアンドグラブ”(強奪)型と表現することができます。これらはスピードのために最適化された短期型の侵入です。攻撃者はすばやく動き  – しばしば48時間以内にデータを抜き出し  – ステルス性よりも規模を重視します。これらの侵害の期間の中央値は10日ほどです。検知の危険を冒しても短期的利益を得ようとしていることが明らかです。  

2つ目は“ローアンドスロー”(低速)型です。これらのオペレーションはデータセット内ではあまり多くありませんでしたが、潜在的影響はより重大です。ここでは攻撃者は持続性を重視し、アイデンティティシステムや正規の管理ツールを通じて永続的なアクセスを確立し、数か月間、場合によっては数年にわたって検知されないままアクセスを維持しようとします。1つの注目すべきケースでは、脅威アクターは環境に完全に侵入して永続性を確立し、600日以上経ってからようやく再浮上した例もありました。このようなオペレーションの一時停止は侵入の深さと脅威アクターの長期的な戦略的意図の両方を表しています。このことはサイバーアクセスが長期にわたって保有し活用するべき戦略的資産であることを示しており、これは最も戦略的に重要なセクターにおいて最もよく見られたパターンです。  

同じ作戦エコシステムにおいて両方のモデルを並行して利用し、標的の価値、緊急性、意図するアクセスに基づいて適切なモデルを選択することも可能だという点に注意することも重要です。“スマッシュアンドグラブ” モデルが見られたからといって諜報活動が失敗したとのみ解釈すべきではなく、むしろ目標に沿った作戦上の選択かもしれないと見るべきでしょう。“ローアンドスロー” 型は粘り強い活動のために最適化され、“スマッシュアンドグラブ” 型はスピードのために最適化されています。どちらも意図的な作戦上の選択と見られ、必ずしも能力を表していません。  

サイバーリスクを再考する

多くの組織にとって、サイバーリスクはいまだに一連の個別のイベントとして位置づけられています。何かが発生し、検知され、封じ込められ、組織はそれを乗り越えて前に進みます。しかし永続的アクセスは、特にクラウド、アイデンティティベースのSaaSやエージェント型システム、そして複雑なサプライチェーンネットワークが相互接続された環境では、重大な持続的露出リスクを作り出します。システムの中断やデータの流出が発生していなくても、そのアクセスによって業務や依存関係、そして戦略的意思決定についての情報を得られるかもしれません。サイバーリスクはますます長期的な競合情報収集に似てきています。

その影響はSOCだけの問題ではありません。組織はガバナンス、可視性、レジリエンスについての考え方を見直し、サイバー露出をインシデント対応の問題ではなく構造的なビジネスリスクとして扱う必要があります。  

次の目標

この調査の目的は、これらの脅威の仕組みについてより明確な理解を提供することにより、防御者がより早期にこれらを識別しより効果的に対応できるようにすることです。これには、インジケーターの追跡からビヘイビアの理解にシフトすること、アイデンティティプロバイダーを重要インフラリスクとして扱うこと、サプライヤーの監視を拡大すること、迅速な封じ込めのための能力に投資すること、などが含まれます。  

ダークトレースの最新調査、”Crimson Echo: ビヘイビア分析を通じて中国系サイバー諜報技術を理解する” についてより詳しく知るには、ビジネスリーダー、CISO、SOCアナリストに向けたCrimson Echoレポートのエグゼクティブサマリーを ここからダウンロードしてください。 

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

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How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.  

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.  

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.  

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.  

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.  

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.  

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.  

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

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Ed Jennings
President and CEO
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