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: 2018-12-17)
    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
    1India267757819%China38.8%Togo31%State of Palestine3.52%Seychelles0.502
    2China13352719.48%Brazil13.8%Mayotte27%Mongolia3.02%Antigua and Barbuda0.287
    3Vietnam11332498.05%Russian Federation6.16%Anguilla26%Vietnam2.55%Albania0.199
    4Iran (Islamic Republic of)10730597.62%United States of America3.73%Yemen23%The former Yugoslav Republic of Macedonia2.21%Bulgaria0.143
    5Brazil7821075.56%Indonesia3.03%Laos13%Iran (Islamic Republic of)2.19%Samoa0.129
    6Thailand6838014.86%India2.89%Syrian Arab Republic11%Syrian Arab Republic2.06%China0.121
    7Indonesia4990613.54%Bangladesh1.91%Bhutan9.2%Tunisia2.04%Estonia0.12
    8Pakistan3641072.59%Ukraine1.76%Tajikistan8.1%Thailand1.87%Brazil0.114
    9Russian Federation3443262.45%United Kingdom of Great Britain and Northern Ireland1.63%Myanmar7%Algeria1.77%Montenegro0.104
    10Algeria3146602.23%Vietnam1.54%Mauritania6.7%Libya1.72%Czechia0.103
    11Egypt3131202.22%Poland1.51%State of Palestine6.6%Mauritius1.61%Serbia0.102
    12Mexico2776621.97%Colombia1.19%Mongolia6%Seychelles1.3%United States Virgin Islands0.0997
    13Venezuela (Bolivarian Republic of)2431011.73%Iran (Islamic Republic of)1.06%Albania5.5%Venezuela (Bolivarian Republic of)1.27%Slovenia0.0966
    14Turkey2144301.52%Argentina1.01%Iraq5.2%Albania1.26%Republic of Moldova0.0913
    15Philippines2078821.48%Japan0.866%India5.1%Maldives1.2%Kyrgyzstan0.0913
    16Saudi Arabia1585141.13%Spain0.826%Cambodia4.8%Pakistan1.19%Belize0.0893
    17Morocco1383070.982%Czechia0.816%Iran (Islamic Republic of)4.4%Antigua and Barbuda1.17%Latvia0.0835
    18Tunisia1305780.927%South Korea0.793%Sao Tome and Principe4.3%Bosnia and Herzegovina1.15%Malta0.0802
    19United States of America1305190.927%Turkey0.769%Venezuela (Bolivarian Republic of)4.2%Uruguay1.09%Armenia0.0784
    20Italy1217170.865%South Africa0.615%Afghanistan4.1%Oman0.984%Ukraine0.0783