How do attacks on data centres affect internet services?
Attacks on data centres cause outages mainly by knocking out power, cooling or connectivity, and the effects can spread beyond the facility.
Covers: This page explains how physical attacks, cyberattacks and sabotage on data centres can disrupt internet services, covering the main mechanisms (power, cooling, connectivity, routing) and evidence from documented incidents. It does not cover attacks on end-user devices or general cybercrime unrelated to data centre infrastructure.
Also answers: What happens to the internet when a data centre is attacked? · How do data centre attacks disrupt online services? · Impact of data centre attacks on internet connectivity · Can attacking a data centre take down the internet?
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The short answer
Interpretation AI-prepared starting mapAttacks on data centres disrupt internet services mainly by knocking out the physical systems a facility depends on — power, cooling and network connectivity — or by damaging the routing and edge connectivity that carries traffic to users. Documented cases show two distinct patterns: a fire at the SK C&C data centre in South Korea caused an extended outage for Kakao Corp, with ripple effects across the facility and a rise in cyber-security threats during the outage; and repeated strikes on Yandex's infrastructure in Russia forced the company to suspend operations. Research on edge networks shows that a sudden, temporary absence of requests from an address block is a usable signal that those users have lost internet connectivity.123
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Be the first to voteIn brief
Data centre attacks disrupt services mainly through power, cooling and connectivity failures, which can ripple across the facility rather than staying contained.1
Evidence-backedThe SK C&C fire caused an extended Kakao outage, raised cyber-security threats during the disruption, and pushed users to competitor apps.1
Evidence-backedRepeated strikes on Yandex infrastructure forced the company to suspend operations.2
Evidence-backedLoss of connectivity from an affected area is detectable as a sudden temporary absence of regular requests from its address blocks.3
Evidence-backedSimulations suggest consequences percolate through interdependent nodes and that social factors shape outcomes, but this has not been validated on a real data centre attack.4
Evidence-backed
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The evidence behind it
4 sources- Other studies and data3
- Background1
When it was published
Newest from 2026
| Source | Kind | Year |
|---|---|---|
| Ukraine strikes Yandex data centre in third attack on Russian tech giant | Background | 2026 |
| Modelling Cascading Failure in Complex CPSS to Inform Resilient Mission Assurance: An Intelligent Transport System Case Study. | Other studies and data | 2025 |
| Advancing the Art of Internet Edge Outage Detection | Other studies and data | 2018 |
| Did my app just crash? A case study of the Kakao superapp disruption event | Other studies and data | 2024 |
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What it means for you
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If you rely on a single messaging or superapp for daily communication
the Kakao case shows an extended data centre outage can cut off that app entirely, and users migrated to competitors during the disruption — having a fallback channel is the practical lesson.1
Evidence-backedIf you are responsible for continuity planning at a service that depends on one data centre
the SK C&C review attributes the severity of the Kakao outage to ineffective disaster readiness and an inadequate fire response, and recommends holistic risk management rather than treating the facility in isolation.1
Evidence-backedIf you are assessing exposure to state-linked or repeated attacks on infrastructure
the Yandex case shows a company can be targeted repeatedly within a single week and respond by suspending operations, so continuity plans should assume repeated rather than single strikes.2
Evidence-backedIf you want to detect whether an area has lost internet connectivity
a sudden temporary absence of the hourly requests that address blocks normally make to a CDN is a usable signal of a connectivity disruption.3
Evidence-backedIf you are modelling how far an outage will spread
simulation work suggests identifying critical nodes and including social as well as technical nodes, since consequences percolate through interdependent systems and human responses shape the outcome.4
Evidence-backedThe full story · 3 chapters
01
How an attack turns into an outage
AI summary:Outages start when attacks damage power, cooling or connectivity, and failures can ripple across the facility and show up as lost requests from affected address blocks.
Evidence-backed: A data centre keeps services online through interdependent physical systems: electrical power, cooling, and network connectivity. When an attack damages one of these, the failure can spread to the others — the SK C&C case describes how ineffective disaster readiness led to an inadequate fire response and serious ripple effects across the data centre, rather than a contained incident. This is the core mechanism by which a localised physical event becomes a broad service outage.1
Evidence-backed: On the network side, disruption shows up as loss of connectivity for the address blocks served through the affected facility. A major CDN's hourly request logs show that devices in many edge address blocks contact the CDN every hour over weeks and months; a sudden temporary absence of those requests indicates that those address blocks have lost internet connectivity. This gives a measurable signature of an outage as seen from the edge, even when the cause is upstream.3
Evidence-backed: Simulation work on complex cyber-physical-social systems argues that consequences can percolate through a network of interdependent nodes, and that identifying critical nodes matters for resilience. The same work stresses that social nodes — people and organisations — must be included in models for accurate simulation, because failures that involve cyber systems are shaped by how people and institutions respond. This is a modelling result from an intelligent transport scenario, not a measurement of a data centre attack, so it should be read as a framework for thinking about cascades rather than as evidence about any specific incident.4
02
Documented incidents
AI summary:A fire at SK C&C caused an extended Kakao outage, and repeated strikes on Yandex forced it to suspend operations.
Evidence-backed: South Korea, SK C&C data centre fire: a fire at the SK C&C data centre caused an extended outage for Kakao Corp, one of the country's leading tech companies, and disrupted KakaoTalk, its most widely used messaging application. The review found that ineffective disaster readiness produced an inadequate fire response, with serious ripple effects across the data centre. During the outage, cyber-security threats rose. As a result of the disruption, Kakao users turned to competitor apps, changing market dynamics — an illustration of the unforeseen costs and socio-economic effects of such interruptions.1
Evidence-backed: Russia, Yandex: Ukraine struck a Yandex data centre, the third attack on the Russian tech giant that week, forcing the company to suspend its operations. The reporting confirms the strike, the repetition within a single week, and the operational suspension; it does not state how many users were affected, which services went down, or how long the suspension lasted.2
03
What tends to follow an outage
AI summary:Documented cases show security threats rising during the outage and users switching to competitor apps.
Evidence-backed: Two downstream effects recur in the documented cases. First, security risk can rise during the disruption itself: the Kakao review notes that cyber-security threats increased while the outage was ongoing. Second, users move: Kakao users turned to competitor apps during the extended outage, shifting market dynamics in a way the review frames as an unforeseen cost of the interruption. Both effects suggest that the impact of a data centre attack extends beyond the hours of downtime.1
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Data centre attacks disrupt services mainly through power, cooling and connectivity failures, which can ripple across the facility rather than staying contained.
The SK C&C fire caused an extended Kakao outage, raised cyber-security threats during the disruption, and pushed users to competitor apps.
Repeated strikes on Yandex infrastructure forced the company to suspend operations.
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- 1Did my app just crash? A case study of the Kakao superapp disruption eventJournal of Business Continuity & Emergency Planning (Hefley et al.)Published Mar 1, 2024Checked Oct 12, 2026
“Superapps (ie apps that integrate the features of multiple applications for a more convenient user experience) have become pervasive among Internet users. This case study examines a recent disruption to one such application: KakaoTalk - the most widely used messaging application in South Korea. Specifically, the case study examines a fire incident at the SK C&C data centre, which caused an extended outage for one of South Korea's leading tech companies - Kakao Corp. The review of this event reveals how ineffective disaster readiness resulted in inadequate fire response, leading to serious ripple effects across the data centre. During the outage, cyber-security threats rose. As a result of these disruptions, Kakao users turned to competitor apps, resulting in changing market dynamics. This case study highlights the unforeseen costs and socio-economic influences caused by such interruptions, highlighting the importance of holistic risk management strategies.”
- 2Ukraine strikes Yandex data centre in third attack on Russian tech giantBBC NewsPublished Oct 11, 2026Checked Oct 12, 2026
“It is the third time this week that Ukraine has targeted Russian tech giant Yandex, forcing the company to suspend its operations.”
- 3Advancing the Art of Internet Edge Outage DetectionInternet Measurement Conference (Richter et al.)Published Oct 31, 2018Checked Oct 12, 2026
“Measuring reliability of edge networks in the Internet is difficult due to the size and heterogeneity of networks, the rarity of outages, and the difficulty of finding vantage points that can accurately capture such events at scale. In this paper, we use logs from a major CDN, detailing hourly request counts from address blocks. We discovered that in many edge address blocks, devices, collectively, contact the CDN every hour over weeks and months. We establish that a sudden temporary absence of these requests indicates a loss of Internet connectivity of those address blocks, events we call disruptions.”
- 4Modelling Cascading Failure in Complex CPSS to Inform Resilient Mission Assurance: An Intelligent Transport System Case Study.Entropy (Basel, Switzerland) (Sobb & Turnbull)Published Jul 25, 2025Checked Oct 12, 2026
“They represent unique opportunities but also have potential threats to system operation and correctness. The emergent behaviour in Complex Cyber-Physical-Social Systems (C-CPSSs), caused by events such as cyber-attacks and network outages, have the potential to have devastating effects to critical services across society. It is therefore imperative that the risk of cascading failure is minimised through the fortifying of these systems of systems to achieve resilient mission assurance. This work designs and implements a programmatic model to validate the value of cascading failure simulation and analysis, which is then tested against a C-CPSS intelligent transport system scenario. Results from the model and its implementations highlight the value in identifying both critical nodes and percolation of consequences during a cyber failure, in addition to the importance of including social nodes in models for accurate simulation results. Understanding the relationships between cyber, physical, and social nodes is key to understanding systems' failures that occur because of or that involve cyber systems, in order to achieve cyber and system resilience.”
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“What tends to follow an outage” rests on one independent source
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Open questions
How many users and which services are affected by a given data centre attack, and for how long? The available incidents are described qualitatively, without figures on reach or duration.
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How far do cascading failures actually propagate from a damaged data centre in practice, as opposed to in simulation? The modelling work identifies critical nodes and percolation but has not been tested on a real data centre attack.
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Can edge-outage detection methods reliably distinguish an attack-caused data centre outage from ordinary connectivity loss, and how quickly?
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Which readiness measures — fire response, redundancy, routing diversity — measurably shorten outages after an attack? The Kakao review points to disaster readiness as decisive but does not compare specific measures.
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