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: 2019-10-22)
    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
    1India254860818.2%Russian Federation23.7%Togo70%Curaçao2.75%Panama11.4
    2China13624299.72%Panama21.7%Martinique64%State of Palestine2.74%Ireland0.325
    3Iran (Islamic Republic of)10102007.21%United States of America6.03%Yemen22%Mongolia2.36%Netherlands0.276
    4Vietnam9357416.68%China4.07%Laos17%Saint Vincent and the Grenadines2.35%Russian Federation0.257
    5Thailand5510363.93%Netherlands3.56%Mauritania16%Tunisia2.01%Antigua and Barbuda0.174
    6Egypt5212143.72%Brazil2.79%Tajikistan14%Cayman Islands1.85%Kazakhstan0.171
    7Brazil4536363.24%Vietnam2.68%Timor-Leste13%Syrian Arab Republic1.83%Kyrgyzstan0.145
    8United States of America4500273.21%South Africa2.09%Curaçao9.2%Iran (Islamic Republic of)1.76%Bulgaria0.141
    9Indonesia4097992.93%Canada2.03%Syrian Arab Republic9%The former Yugoslav Republic of Macedonia1.52%Uzbekistan0.12
    10Pakistan3956562.82%United Kingdom of Great Britain and Northern Ireland1.98%Barbados8.8%Sudan1.52%Armenia0.103
    11Algeria3428172.45%Kazakhstan1.95%Mayotte8.6%Bermuda1.47%Kiribati0.0963
    12Morocco3185272.27%France1.81%United States Virgin Islands7.5%Thailand1.4%Saint Vincent and the Grenadines0.0941
    13Russian Federation2964762.12%India1.68%Sudan6.4%Vietnam1.39%South Africa0.0814
    14Venezuela (Bolivarian Republic of)2401091.71%Uzbekistan1.63%Afghanistan6.4%Morocco1.36%Belize0.0797
    15Mexico2285361.63%Germany1.57%Iraq6.1%Algeria1.36%Lithuania0.0794
    16Turkey2255451.61%Ukraine1.2%Bhutan6%Antigua and Barbuda1.36%Cayman Islands0.0782
    17Sudan1957731.4%Indonesia1.08%Myanmar5.7%Uruguay1.36%Montenegro0.0781
    18Tunisia1492201.07%Japan1.06%El Salvador5.4%Seychelles1.32%Canada0.0761
    19Japan1479001.06%Ireland1.05%Guernsey5.4%Bahamas1.29%Republic of Moldova0.0756
    20Philippines1399650.999%South Korea0.986%Cambodia5.3%Libya1.27%Serbia0.0746