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: 2019-06-18)
    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
    1India194704215.1%United States of America13.8%Guernsey90%China3.12%Netherlands0.283
    2China148373811.5%Russian Federation10.5%Togo37%Ireland2.86%Canada0.249
    3Vietnam9205667.16%China8.24%Anguilla26%State of Palestine2.38%Antigua and Barbuda0.249
    4Iran (Islamic Republic of)8375836.51%Brazil7.85%Yemen19%Mongolia2.3%Belize0.237
    5United States of America5815244.52%United Kingdom of Great Britain and Northern Ireland6.75%Mayotte17%Cayman Islands2.21%Singapore0.202
    6Thailand5564534.33%Canada6.65%Laos17%Vietnam1.94%Ireland0.2
    7Brazil5517204.29%France4.8%Falkland Islands (Malvinas)15%Tunisia1.9%Seychelles0.154
    8Russian Federation3134372.44%Vietnam4.61%Tajikistan12%Aruba1.78%Somalia0.153
    9Indonesia3088892.4%Netherlands3.55%Syrian Arab Republic8.6%Iran (Islamic Republic of)1.71%Bulgaria0.124
    10Pakistan2787062.17%South Africa2.58%United States Virgin Islands7.7%The former Yugoslav Republic of Macedonia1.7%Vietnam0.124
    11Egypt2779032.16%India2.11%Myanmar7.6%Syrian Arab Republic1.58%Russian Federation0.122
    12Algeria2648742.06%Iran (Islamic Republic of)2.03%Iraq7.4%Thailand1.52%Laos0.115
    13Malaysia2485531.93%South Korea1.49%Isle of Man7.2%Antigua and Barbuda1.48%France0.113
    14Venezuela (Bolivarian Republic of)2415391.88%Ukraine1.28%Bhutan7.2%Bahamas1.45%South Africa0.103
    15Mexico2293751.78%Indonesia1.24%Mauritania5.3%Grenada1.43%Mongolia0.101
    16Turkey1883551.46%Argentina1.15%State of Palestine5%Curaçao1.4%Lithuania0.0912
    17Germany1380071.07%Italy1.12%Mongolia4.8%Algeria1.34%Kiribati0.0901
    18Philippines1349731.05%Japan1.1%Sint Maarten (Dutch part)4.7%Mauritius1.34%Montenegro0.0779
    19Morocco1273230.99%Poland0.917%Cambodia4.6%Bermuda1.33%Albania0.072
    20Tunisia1213900.944%Australia0.852%Curaçao4.3%Libya1.29%United States of America0.0718