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: 2020-01-27)
    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
    1India187099114%Russian Federation42.1%Martinique1.3e+02%Saint Vincent and the Grenadines3.32%Bulgaria7.17
    2China12901439.64%Bulgaria16.4%Togo84%Tunisia2.86%Panama1.34
    3Iran (Islamic Republic of)8718786.51%United States of America5.74%Anguilla48%State of Palestine2.48%Russian Federation0.714
    4Vietnam7318965.47%Vietnam3.1%Yemen18%Bermuda2.42%Netherlands0.305
    5Brazil6627724.95%China2.6%Laos17%Uruguay2.34%New Caledonia0.234
    6United States of America5994104.48%Netherlands2.51%Barbados14%Mongolia2.18%Malta0.216
    7Thailand5490354.1%France2.49%Tajikistan13%Singapore1.91%Croatia0.216
    8Indonesia3519512.63%Brazil2.39%United States Virgin Islands12%Syrian Arab Republic1.86%Antigua and Barbuda0.215
    9Pakistan3214822.4%Iran (Islamic Republic of)1.68%Mauritania11%Seychelles1.81%Ireland0.176
    10Algeria3170992.37%United Kingdom of Great Britain and Northern Ireland1.64%Timor-Leste9.8%Grenada1.78%Armenia0.17
    11Russian Federation3019772.26%Panama1.63%Syrian Arab Republic9.2%Cayman Islands1.76%Republic of Moldova0.17
    12Turkey2647401.98%Ukraine1.31%Myanmar8.1%Curaçao1.6%The former Yugoslav Republic of Macedonia0.141
    13Mexico2400791.79%South Africa1.05%Guernsey7.7%Ireland1.58%Serbia0.136
    14Morocco2393041.79%Japan1%French Guiana7.5%Mauritius1.52%Lithuania0.115
    15Egypt2282211.71%South Korea0.884%Isle of Man7.5%Iran (Islamic Republic of)1.52%Georgia0.108
    16Tunisia2124751.59%India0.88%Mayotte7.4%Sudan1.49%Ukraine0.0989
    17Venezuela (Bolivarian Republic of)2080481.55%Indonesia0.749%Sudan6.6%Barbados1.47%Mongolia0.0932
    18Sudan1920241.43%Canada0.693%Djibouti6.2%The former Yugoslav Republic of Macedonia1.42%Vietnam0.0915
    19Argentina1863501.39%Colombia0.671%El Salvador6.2%Thailand1.39%France0.09
    20South Korea1731621.29%Spain0.547%Afghanistan6.1%Belize1.26%Laos0.0891