CIA 2010 covert communication websites 2013 DNS Census virtual host cleanup Updated 2025-04-24 +Created 1970-01-01
We've noticed that often when there is a hit range:and that this does not seem to be that common. Let's see if that is a reasonable fingerprint or not.
- there is only one IP for each domain
- there is a range of about 20-30 of those
Note that although this is the most common case, we have found multiple hits that viewdns.info maps to the same IP.
First we create a table The
u
(unique
) that only have domains which are the only domain for an IP, let's see by how much that lowers the 191 M total unique domains:time sqlite3 u.sqlite 'create table t (d text, i text)'
time sqlite3 av.sqlite -cmd "attach 'u.sqlite' as u" "insert into u.t select min(d) as d, min(i) as i from t where d not like '%.%.%' group by i having count(distinct d) = 1"
not like '%.%.%'
removes subdomains from the counts so that CGI comms are still included, and distinct
in count(distinct
is because we have multiple entries at different timestamps for some of the hits.Let's start with the 208 subset to see how it goes:OK, after we fixed bugs with the above we are down to 4 million lines with unique domain/IP pairs and which contains all of the original hits! Almost certainly more are to be found!
time sqlite3 av.sqlite -cmd "attach 'u.sqlite' as u" "insert into u.t select min(d) as d, min(i) as i from t where i glob '208.*' and d not like '%.%.%' and (d like '%.com' or d like '%.net') group by i having count(distinct d) = 1"
This data is so valuable that we've decided to upload it to: archive.org/details/2013-dns-census-a-novirt.csv Format:The numbers of the first column are the IPs as a 32-bit integer representation, which is more useful to search for ranges in.
8,chrisjmcgregor.com
11,80end.com
28,fine5.net
38,bestarabictv.com
49,xy005.com
50,cmsasoccer.com
80,museemontpellier.net
100,newtiger.com
108,lps-promptservice.com
111,bridesmaiddressesshow.com
To make a histogram with the distribution of the single hostname IPs:Which gives the following useless noise, there is basically no pattern:
#!/usr/bin/env bash
bin=$((2**24))
sqlite3 2013-dns-census-a-novirt.sqlite -cmd '.mode csv' >2013-dns-census-a-novirt-hist.csv <<EOF
select i, sum(cnt) from (
select floor(i/${bin}) as i,
count(*) as cnt
from t
group by 1
union
select *, 0 as cnt from generate_series(0, 255)
)
group by i
EOF
gnuplot \
-e 'set terminal svg size 1200, 800' \
-e 'set output "2013-dns-census-a-novirt-hist.svg"' \
-e 'set datafile separator ","' \
-e 'set tics scale 0' \
-e 'unset key' \
-e 'set xrange[0:255]' \
-e 'set title "Counts of IPs with a single hostname"' \
-e 'set xlabel "IPv4 first byte"' \
-e 'set ylabel "count"' \
-e 'plot "2013-dns-census-a-novirt-hist.csv" using 1:2:1 with labels' \
;
As highlighted e.g. at Human Compatible by Stuart J. Russell (2019), this AI alignment intrinsically linked to the idea of utility in economy.
Draft by Ciro Santilli with cross language input/output test cases: github.com/cirosantilli/algorithm-cheat
By others:
This is the lowest level of abstraction computer, at which the basic gates and power are described.
All indefinite orthogonal groups of matrices of equal metric signature are isomorphic Updated 2025-04-24 +Created 1970-01-01
Following the definition of the indefinite orthogonal group, we want to show that only the metric signature matters.
First we can observe that the exact matrices are different. For example, taking the standard matrix of :and:both have the same metric signature. However, we notice that a rotation of 90 degrees, which preserves the first form, does not preserve the second one! E.g. consider the vector , then . But after a rotation of 90 degrees, it becomes , and now ! Therefore, we have to search for an isomorphism between the two sets of matrices.
For example, consider the orthogonal group, which can be defined as shown at the orthogonal group is the group of all matrices that preserve the dot product can be defined as:
mlcommons.org/en/ Their homepage is not amazingly organized, but it does the job.
Benchmark focused on deep learning. It has two parts:Furthermore, a specific network model is specified for each benchmark in the closed category: so it goes beyond just specifying the dataset.
Results can be seen e.g. at:
Those URLs broke as of 2025 of course, now you have to click on their Tableau down to the 2.1 round and there's no fixed URL for it:
And there are also separate repositories for each:
There are unlisted articles, also show them or only show them.