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: 2020-05-25)
    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
    1India164122913.5%Russian Federation36.3%Martinique1.5e+02%Bermuda2.53%Panama9.11
    2China138479211.4%Bulgaria17.2%Anguilla64%State of Palestine2.16%Bulgaria6.62
    3Vietnam10430108.6%Panama12.5%Togo49%Tunisia1.84%New Caledonia0.602
    4Iran (Islamic Republic of)8005416.6%United States of America5.27%Laos17%Vietnam1.55%Russian Federation0.546
    5Brazil5299824.37%China3.74%Tajikistan14%Suriname1.54%Estonia0.463
    6Thailand4848164%Netherlands2.87%Yemen11%Algeria1.53%Netherlands0.309
    7United States of America3626792.99%South Africa2.41%Barbados9.1%Syrian Arab Republic1.5%Antigua and Barbuda0.152
    8Indonesia3504212.89%United Kingdom of Great Britain and Northern Ireland1.96%Isle of Man7.9%The former Yugoslav Republic of Macedonia1.45%South Africa0.13
    9Algeria3173392.62%Vietnam1.84%Myanmar7.9%Saint Kitts and Nevis1.41%Cayman Islands0.122
    10Russian Federation2649062.18%Poland1.42%Syrian Arab Republic7.1%Iran (Islamic Republic of)1.4%Saint Kitts and Nevis0.111
    11Pakistan2351691.94%Brazil1.21%Mayotte6.6%Saint Vincent and the Grenadines1.38%Lithuania0.0866
    12Egypt2318121.91%Japan1.1%United States Virgin Islands6.3%Ireland1.27%Poland0.0848
    13Venezuela (Bolivarian Republic of)2310221.9%Italy0.814%Timor-Leste6.2%Cayman Islands1.23%Georgia0.0796
    14Mexico2165031.78%South Korea0.808%Mauritania5.7%Thailand1.23%Suriname0.0599
    15Turkey2141711.77%India0.734%Iraq5.6%San Marino1.19%Laos0.0564
    16Morocco1882401.55%Ukraine0.626%Montenegro5.4%Venezuela (Bolivarian Republic of)1.11%Armenia0.0528
    17Taiwan1629941.34%Germany0.608%Suriname5.2%Mongolia1.09%Republic of Moldova0.0524
    18Germany1612531.33%France0.518%British Indian Ocean Territory5.1%Barbados1.02%Albania0.0483
    19Italy1581761.3%Australia0.486%Somalia4.9%Uruguay1.01%Serbia0.0481
    20South Korea1489311.23%Canada0.441%Mongolia4.5%Curaçao0.992%Vietnam0.048