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: 2019-12-07)
    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
    1India221471917%Russian Federation32.5%Martinique74%Mongolia2.34%Bulgaria1.98
    2China164095012.6%United States of America10.3%Togo47%State of Palestine2.27%Seychelles0.909
    3Vietnam8605176.62%Bulgaria9.9%Anguilla23%Cayman Islands2.2%Russian Federation0.253
    4Iran (Islamic Republic of)7313415.62%China5.77%Yemen18%Bermuda1.87%New Caledonia0.205
    5Thailand5531214.25%Vietnam5.63%Tajikistan18%Ireland1.84%Ireland0.171
    6Brazil5021313.86%United Kingdom of Great Britain and Northern Ireland3.56%Laos17%Syrian Arab Republic1.81%Netherlands0.128
    7United States of America4734313.64%France2.86%Mayotte16%Tunisia1.67%Lithuania0.116
    8Indonesia3995833.07%Brazil2.86%United States Virgin Islands11%The former Yugoslav Republic of Macedonia1.53%Antigua and Barbuda0.11
    9Algeria3330452.56%Netherlands2.3%Guernsey11%Saint Vincent and the Grenadines1.51%Laos0.0826
    10Russian Federation3171062.44%Japan1.92%Mauritania11%Thailand1.4%Montenegro0.0817
    11Pakistan3164332.43%South Africa1.6%Timor-Leste11%Bahamas1.36%Vietnam0.0761
    12Morocco2599202%India1.57%Barbados9.1%Algeria1.32%Armenia0.074
    13Mexico2071731.59%Ukraine1.31%Syrian Arab Republic8.9%Seychelles1.3%Czechia0.0696
    14Venezuela (Bolivarian Republic of)2000461.54%Canada1.3%Iraq6.4%Vietnam1.28%Serbia0.059
    15Turkey1704851.31%Indonesia0.965%Bhutan5.9%Iran (Islamic Republic of)1.28%Latvia0.0539
    16Germany1429161.1%Iran (Islamic Republic of)0.915%Afghanistan5.7%Grenada1.23%Barbados0.05
    17Sudan1422521.09%Germany0.871%Isle of Man5.5%Morocco1.11%France0.0473
    18Philippines1398401.08%Ireland0.771%Myanmar5.4%Sudan1.1%Uruguay0.0454
    19Italy1362011.05%Colombia0.735%Uzbekistan5%Belize1.08%Ukraine0.0454
    20Egypt1299621%Bangladesh0.684%Somalia5%Libya1.01%Republic of Moldova0.0449