Notes:

  • Population and Internet population numbers are generally as of 2014, and are sourced directly from the World Bank public access repositories. See, for example: Population Figures for more information.
  • The Regional Internet Registries (RIR) uses two letter codes for countries. These codes almost always agree with ISO3166-1-Alpha-2 (ISO 2 letter codes). However, RIR databases also contain some country codes that are not in ISO3166-1-Alpha-2 because they are obsolete (eg: SU), are sporadically used regional designations (eg: EU), alternates (eg: 'UK' versus 'GB') or are simply errors.
  • The country codes used (ISO3011-1-Alpha-2 for allocation-to-country mapping, ISO3166-1-Alpha-3 for population-to-country mapping) and for official country names are from ISO, obtained from here. Some minor stylistic changes have been made to a few country names to better align with more commonly recognized forms.
  • The per-capita numbers are calculated with the total internet population per-country. (The World Bank supplies the "internet population" as a percentage of the population)
  • The network sizes are calculated totalling up the sizes of network allocations assigned to each country as per RIR information .
  • The first grouping "By Infections" is the total number of infections in the corresponding country, and its percentage of the total number of infections the CBL knows about.
  • "By Spam Volume" is the total amount of spam we've observed coming from the corresponding country, and its percentage of the total. Please note that the very high volume associated with the United States is because of a very small number of a particular infection that "thinks" our spam sensors are their outbound SMTP relays. As such, instead of receiving a fraction of the spambot's spam, we receive all of it.
  • "By Network Infected", this is the percentage of the IP address netblocks assigned to the country that are infected. For example, 8% means that 8% of all IP addresses allocated to the country are infected with something we can detect.
  • "Per-Capita Infections" is the percentage of the population who have Internet access that are infected. As an example, a "12%" means that 12% of the Internet-connected population of that country has an infection that we can detect.
  • "Spam Per Capita" is the amount of spam we see from the country expressed in terms of the Internet-connected poopulation. for example, a "3" means that we've seen 3 spams for every Internet-connected user in the corresponding country. Note that the US figures are inflated for the same reason the "Spam Volume" figure is.
  • We now publish CSV format files that contain the basic infection/traffic numbers for all countries (including population), ASNs and domains.

    The top 20 worst countries (Prepared: 2019-08-20)
    Rank By Infections By Spam Volume By Network Infected Per-Capita Infections Spam Per-Capita
      Country Infections %of CBL Country %of traffic Country Rate Country Rate Country /capita
    1India201334116.2%Panama18.3%Martinique58%State of Palestine2.43%Panama9.89
    2China168364113.5%Russian Federation16.5%Guernsey36%Mongolia2.01%Ireland0.236
    3Vietnam9146057.35%United States of America8.36%Togo30%Vietnam1.93%Singapore0.202
    4Iran (Islamic Republic of)7793886.26%France4.88%Anguilla23%Curaçao1.9%Russian Federation0.196
    5Thailand5586344.49%Brazil4.46%Yemen18%Tunisia1.68%Belize0.196
    6Brazil4837423.89%China4.16%Laos17%Syrian Arab Republic1.62%Finland0.173
    7Egypt4651183.74%United Kingdom of Great Britain and Northern Ireland3.71%Mayotte8.2%The former Yugoslav Republic of Macedonia1.58%Costa Rica0.165
    8Indonesia4119063.31%India3.53%Syrian Arab Republic8.1%Iran (Islamic Republic of)1.58%Iceland0.141
    9Russian Federation2942142.36%Canada3.31%Tajikistan7.3%Thailand1.52%Antigua and Barbuda0.141
    10Pakistan2769032.22%Vietnam3.31%Falkland Islands (Malvinas)6.6%Ireland1.5%Mongolia0.137
    11Venezuela (Bolivarian Republic of)2501472.01%South Africa2.87%United States Virgin Islands6.6%Cayman Islands1.5%Bhutan0.135
    12Turkey2397481.93%South Korea1.75%Myanmar6.5%Bermuda1.47%Lithuania0.131
    13Algeria2344731.88%Netherlands1.19%Bhutan6.2%Venezuela (Bolivarian Republic of)1.35%Canada0.126
    14Mexico2019401.62%Ukraine1.19%Iraq6%Libya1.25%Bulgaria0.118
    15United States of America1983161.59%Indonesia1.15%Curaçao5.9%Grenada1.21%France0.118
    16Malaysia1723911.39%Argentina1.03%Barbados4.7%Aruba1.18%South Africa0.115
    17Philippines1416301.14%Germany0.938%Venezuela (Bolivarian Republic of)4.6%Algeria1.16%Cayman Islands0.113
    18Germany1183730.951%Australia0.785%State of Palestine4.6%Mauritius1.09%China0.0983
    19Argentina1130190.908%Ireland0.741%Mongolia4.3%Saint Lucia1.07%Netherlands0.0969
    20Tunisia1075240.864%Singapore0.735%Mauritania4.3%Egypt1.05%Saint Lucia0.096