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: 2020-04-02)
    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
    1India182657812.6%Russian Federation40.7%Martinique1.9e+02%Tunisia3.18%Bulgaria10.4
    2China155742810.7%Bulgaria24.4%Anguilla62%Bermuda2.67%Croatia1.97
    3Egypt9152866.31%United States of America6.95%Togo49%San Marino2.64%Russian Federation0.676
    4Vietnam8879906.12%China3.88%Laos19%State of Palestine2.6%New Caledonia0.265
    5Iran (Islamic Republic of)7925125.46%Croatia3.12%Yemen16%Algeria2.06%Estonia0.209
    6Brazil7469655.15%United Kingdom of Great Britain and Northern Ireland2.49%Timor-Leste12%Ireland2.01%Netherlands0.191
    7Thailand5290563.65%France1.68%Tajikistan11%Saint Vincent and the Grenadines2%Ireland0.188
    8Algeria4271252.94%Vietnam1.64%United States Virgin Islands10%Egypt1.98%Antigua and Barbuda0.159
    9Indonesia4094002.82%Netherlands1.6%Barbados9.2%Syrian Arab Republic1.86%Curaçao0.13
    10Turkey4009932.76%South Africa1.59%Syrian Arab Republic8.8%Sudan1.69%Romania0.108
    11United States of America3734632.57%Brazil0.918%French Guiana8.7%Uruguay1.69%South Africa0.095
    12Russian Federation3513492.42%Romania0.767%Djibouti8.7%The former Yugoslav Republic of Macedonia1.61%Chile0.0654
    13Pakistan2674871.84%Japan0.732%Isle of Man8.5%Seychelles1.6%Laos0.0615
    14Mexico2371791.64%Canada0.671%Myanmar8%Cayman Islands1.51%France0.0594
    15Tunisia2361541.63%Poland0.67%Montenegro6.7%Antigua and Barbuda1.4%China0.0504
    16Morocco2259921.56%Germany0.667%Somalia6.7%Mauritius1.4%Lithuania0.0502
    17Venezuela (Bolivarian Republic of)2232541.54%South Korea0.564%Mayotte6.6%Curaçao1.38%Vietnam0.0474
    18Sudan2182921.5%Chile0.52%Sudan6.6%Iran (Islamic Republic of)1.38%United States of America0.0473
    19Argentina2019591.39%India0.502%El Salvador5.8%Thailand1.34%Serbia0.0469
    20Colombia1671881.15%Spain0.426%Guadeloupe5.8%Vietnam1.32%Armenia0.0443