1 · Selection
Focus on interpretable dimensions
The interface emphasizes variables that are easy to compare and explain: time, country exposure, incident type, and repeated attacker–target flows.
Cyber incidents · global patterns
Understanding global cyber incident patterns
This website presents a curated view of cyber incident data through four analytical dimensions: time, target geography, incident types, and attacker–target relationships. The goal is not to reproduce a complex repository, but to surface the most interpretable structures in the data.
Editorial approach
Built for clear visual storytelling
The structure follows a simple visual logic: overview first, then ranked comparisons, then recurring geopolitical relationships.
2 · Interaction
Use coordinated views, not isolated charts
A country selected in the ranking chart directly filters the relationships view, turning separate charts into a connected exploratory system.
3 · Accessibility
Readable for non-specialists
Ranked bars, short explanatory text, visible filter states, and stable labels help the website stay understandable for a broad audience.
Time window
2000–2025
Selected country
All countries
Section 1
How have cyber incidents evolved over time?
Overview first: the timeline sets the global temporal context.
A strong increase appears in the most recent years
This opening chart provides a broad temporal view of the dataset. It shows that cyber incidents are not distributed evenly across time: the number of recorded incidents rises sharply in recent years, suggesting an intensification in reporting, activity, or both.
Key takeaway: The timeline establishes the macro-pattern of the website.
It works as the overview layer from which the rest of the exploration begins.
Evolution of cyber incidents over time
Brush the overview chart to zoom into a time window.
Section 2
Which countries are targeted most often?
Position and length make concentration immediately visible.
Targeting is concentrated on a limited set of countries
The distribution of targeted countries is highly uneven. A small number of countries appear far more often than others, suggesting that cyber incidents concentrate around major geopolitical, economic, and strategic actors.
Interaction: Click a country bar to filter the relationships chart and
focus only on the pairings involving that country.
Top targeted countries
Hover for exact values. Click a country to filter the relationships chart.
Country focus
Click a country bar to keep it selected and update the relationships chart.
Section 3
What types of cyber incidents are most common?
A ranked comparison clarifies which forms of activity dominate.
Some incident types dominate the overall picture
Not all types of cyber incidents appear with the same frequency. This chart highlights which forms of activity dominate the dataset, helping distinguish between disruption, theft, ransomware, hijacking, and other recurring forms of cyber aggression.
Key takeaway: This view complements the timeline and geography views by
showing what kinds of incidents structure the dataset most strongly.
Top incident types
Hover for exact values. This view complements the time and country perspectives.
Section 4
Which attacker–target relationships stand out?
Details on demand: focus the strongest repeated country-to-country pairs.
Recurring pairs reveal the strongest geopolitical patterns
Looking at individual countries is useful, but examining repeated initiator–target relationships reveals more structured patterns. This chart shows the most frequent pairs in the dataset and makes persistent rivalries and asymmetric targeting more visible.
Interaction: Click a country in Section 2 to focus this view, then click a
pair here to inspect it more closely.
Most frequent cyber incident relationships
Click a country in Section 2 to focus this chart, or click a pair here for more detail.
Relationship detail
Click a relationship bar to display the currently selected pair.
Section 5
Where do cyber attacks flow geographically?
Hover a country to reveal its top attacker and primary target as directional arrows.
Attack flows trace the geopolitical fault lines
Each country is shaded by the number of cyber incidents it received. Hovering a country draws two arrows: one incoming from its top attacker, one outgoing to the country it most frequently targeted.
Interaction: Hover any shaded country to see its dominant
attacker–target relationship. The time window from Section 1 applies here too.
Incoming attack
Outgoing attack
Global cyber attack flow map
Shading = incidents received · Hover a country for attack flow arrows
Conclusion
What these visualizations reveal
Taken together, the charts provide a focused reading of the dataset.
What becomes visible
The website shows four core structures: a strong recent increase in recorded incidents, a concentration of targets in a limited number of countries, the dominance of a few incident types, and the recurrence of specific attacker–target pairs.
Why this matters
By selecting only the most informative variables and pairing each chart with a short explanation, the website turns a large dataset into a more accessible and interpretable representation of global cyber incident patterns.