This project delves into analyzing ransomware infections using data extracted from the Shodan API. By analyzing real-time data on internet-connected devices, we explore ransomware trends across various countries and cities. Through data visualizations and statistical analysis, we aim to identify geographic hotspots of ransomware activity, comprehend infection patterns, and provide valuable insights for cybersecurity professionals. The project underscores the importance of monitoring and comprehending ransomware incidents to enhance global cyber defenses.
The Shodan API, a powerful tool for searching and retrieving data on internet-connected devices, provides information about devices’ locations, services, vulnerabilities, and more. In this project, the API is used to analyze global trends and patterns of ransomware infections.
This section analyzes ransomware infections. It starts with a summary of affected countries and reported incidents. A statistical analysis presents key metrics on infection distribution. The section concludes with a table detailing ransomware incidents by country and city, revealing geographic trends and high-infection areas.
According to the Shodan dataset, a total of 133 ransomware infections have been reported worldwide, impacting 42 countries. Russian Federation has the highest number of ransomware infections, reporting 16 incidents.
The city with the most ransomware infections is Moscow, with 7 incidents.
This comprehensive table offers a detailed breakdown of ransomware infection rates across various countries and cities. It presents country and city names alongside the corresponding number of ransomware incidents, making it easy to compare regions. This table serves as a crucial reference point for understanding global ransomware trends and identifying areas where cyber defenses may need reinforcement.
| Country | City | Number of Infections |
|---|---|---|
| Russian Federation | Moscow | 7 |
| Germany | Frankfurt am Main | 5 |
| Turkey | Istanbul | 5 |
| Brazil | Manaus | 3 |
| China | Shanghai | 3 |
| Egypt | Cairo | 2 |
| Germany | Falkenstein | 2 |
| China | Guiyang | 2 |
| Ukraine | Kyiv | 2 |
| Spain | Madrid | 2 |
| Czechia | Ostrava | 2 |
| Mexico | Santiago de Querétaro | 2 |
| Brazil | São Vicente | 2 |
| Korea, Republic of | Seoul | 2 |
| Bulgaria | Sofia | 2 |
| United States | St. Louis | 2 |
| China | Tianjin | 2 |
| Hong Kong | Tseung Kwan O | 2 |
| Germany | Aachen | 1 |
| Kazakhstan | Almaty | 1 |
| Netherlands | Amsterdam | 1 |
| Panama | Arco Iris | 1 |
| India | Baharampur | 1 |
| Spain | Barcelona | 1 |
| China | Beijing | 1 |
| Germany | Berlin | 1 |
| Poland | Biała Podlaska | 1 |
| Romania | Bucharest | 1 |
| Hungary | Budapest | 1 |
| Canada | Calgary | 1 |
| Argentina | Castelar | 1 |
| China | Chengdu | 1 |
| United States | Chicago | 1 |
| India | Delhi | 1 |
| United States | Des Moines | 1 |
| Bangladesh | Dhaka | 1 |
| China | Dongjie | 1 |
| United Arab Emirates | Dubai | 1 |
| France | Dunkerque | 1 |
| Germany | Düsseldorf | 1 |
| Poland | Dźwirzyno | 1 |
| Egypt | Giza | 1 |
| China | Guangzhou | 1 |
| China | Hubei | 1 |
| Mexico | Irapuato | 1 |
| Indonesia | Jakarta | 1 |
| Indonesia | Jember | 1 |
| Russian Federation | Kaliningrad | 1 |
| Russian Federation | Kaluga | 1 |
| Russian Federation | Kamensk-Ural’skiy | 1 |
| Russian Federation | Khimki | 1 |
| Japan | Kobe | 1 |
| India | Kolkata | 1 |
| Poland | Kraków | 1 |
| Netherlands | Lelystad | 1 |
| Peru | Lima | 1 |
| United Kingdom | London | 1 |
| Brazil | Marechal Cândido Rondon | 1 |
| Argentina | Mendoza | 1 |
| Mexico | Mexico City | 1 |
| Somalia | Mogadishu | 1 |
| Mexico | Monterrey | 1 |
| Canada | Montréal | 1 |
| Russian Federation | Nizhniy Novgorod | 1 |
| Russian Federation | Novorossiysk | 1 |
| Japan | Ōi | 1 |
| Japan | Osaka | 1 |
| Burkina Faso | Ouagadougou | 1 |
| United States | Phoenix | 1 |
| Brazil | Porto Alegre | 1 |
| United Kingdom | Portsmouth | 1 |
| India | Pune | 1 |
| United States | Rancho Santa Margarita | 1 |
| Italy | Rome | 1 |
| Russian Federation | Saint Petersburg | 1 |
| United States | San Diego | 1 |
| United States | San Jose | 1 |
| United States | Santa Monica | 1 |
| Brazil | São Paulo | 1 |
| United States | SeaTac | 1 |
| Malaysia | Shah Alam | 1 |
| Russian Federation | Shchyolkovo | 1 |
| China | Shenzhen | 1 |
| Lithuania | Šiauliai | 1 |
| Singapore | Singapore | 1 |
| Germany | Stuttgart | 1 |
| Taiwan | Taipei | 1 |
| Uzbekistan | Tashkent | 1 |
| Greece | Thessaloníki | 1 |
| Japan | Tokyo | 1 |
| Italy | Tortona | 1 |
| Libya | Tripoli | 1 |
| Hong Kong | Tsuen Wan | 1 |
| Russian Federation | Ufa | 1 |
| Finland | Vaala | 1 |
| Denmark | Varde | 1 |
| Lithuania | Vilnius | 1 |
| Canada | Winnipeg | 1 |
| China | Wuhan | 1 |
| China | Xi’an | 1 |
| Netherlands | Zaandam | 1 |
| Mexico | Zapopan | 1 |
This section visualizes ransomware infection patterns globally. It maps incidents at country and city levels using Shodan API data, highlighting affected regions and trends. An interactive map lets users zoom in and examine infection details, making it useful for cybersecurity professionals and researchers.
This data visualization explores the global distribution of ransomware infections, focusing on the geographical hotspots by country and city. Using data from the Shodan API, the map highlights areas with the highest concentrations of ransomware incidents, shedding light on trends and patterns in cyberattacks. By mapping ransomware infections based on real-time data, the visualization provides insights into which regions are most affected and allows for a better understanding of the geographic spread of these cyber threats. The interactive map enables users to zoom in on specific locations and view detailed information on the number of incidents, cities, and countries impacted, offering valuable insights for cybersecurity professionals and researchers.