For the past few months, I’ve been running a handful of SSH Honeypots on some cloud providers, including Google Cloud, DigitalOcean, and NameCheap. As opposed to more complicated honeypots looking at attacker behavior, I decided to do something simple and was only interested in where they were coming from, what tools might be in use, and what credentials they are attempting to use to authenticate. My dataset includes 929,554 attempted logins over a period of a little more than 3 months.

If you’re looking for a big surprise, I’ll go ahead and let you down easy: my analysis hasn’t located any new botnets or clusters of attackers. But it’s been a fascinating project nonetheless.

Honeypot Design

With a mere 200ish lines of Go, I implemented a honeypot server using the library as the underlying implementation. I advertised a portable OpenSSH version as the server version string (sent to clients on connection). I then logged each connection to a SQLite database, including the timestamp, IP address, client version, and credentials used to (attempt to) authenticate.

Analysis of Credentials

In a surprise to absolutely nobody, root is by far the most commonly tried username for login sessions. I suspect there must be many attackers trying lists of passwords with just root as the username, as 78% of attempted logins were with username root. None of the remainder of the top 10 are particularly surprising, although usuario was not one I expected to see. (It is Spanish for user.)

Blank passwords are the most common attempted passwords, followed by other obvious choices, like 123456 and password. Just off the top 10 list was a surprising choice of password: J5cmmu=Kyf0-br8CsW. Interestingly, a Google search for this password only finds other people with experience running credential honeypots. It doesn’t appear in any of the password wordlists I have, including SecLists and others. If anyone knows what this is a password for, I’d love to know.

There were a number of other interesting passwords such as 7ujMko0admin, used for a bunch of networked DVRs, and also known to be used by malware attacking IoT devices. There are other passwords that don’t look obvious to a US-centric view of the world, like:

  • baikal – a lake in Siberia
  • prueba – Spanish for test
  • caonima – a Mandarin profanity written in Pinyin
  • meiyoumima – Mandarin for “no password”
  • woaini – Mandarin for “I love you”
  • poiuyt – The name for an optical illusion also known as the “devil’s tuning fork”

There are also dozens and dozens of keyboard walks, like 1q2w3e, 1qaz@WSX, and !QAZ2wsx. There are many more that took me much longer to realize they were keyboard walks, such as 4rfv$RFV and qpwoei.

It has actually fascinated me to look at some of the less obvious passwords and discern their background. Many are inexplicable, but I assume they are from hardcoded passwords in devices or something along those lines. Or perhaps someone let their cat walk across the keyboard to generate it. I’ve certainly had that experience.

Overall, the top 10 usernames and top 10 passwords (not necessarily together) are:

Username Count Password Count
root 729108 <blank> 40556
admin 23302 123456 14542
user 8420 admin 7757
test 7547 123 7355
oracle 6211 1234 7099
ftpuser 4012 root 6999
ubuntu 3657 password 6118
guest 3606 test 5671
postgres 3455 12345 5223
usuario 2876 guest 4423

There were a total of 128,588 unique pairings of username and password attempted, though only 38,112 were attempted 5 or more times. You can download the full list of pairs with counts here, but I’ve omitted those attempted less than 5 times in case a legitimate user typo’d an IP or otherwise was mistaken. The top 25 pairings are:

username password count
root   37580
root root 4213
user user 2794
root 123456 2569
test test 2532
admin admin 2531
root admin 2185
guest guest 2143
root password 2128
oracle oracle 1869
ubuntu ubuntu 1811
root 1234 1681
root 123 1658
postgres postgres 1594
support support 1535
jenkins jenkins 1360
admin password 1241
root 12345 1177
pi raspberry 1160
root 12345678 1126
root 123456789 1069
ubnt ubnt 1069
admin 1234 1012
root 1234567890 967
ec2-user ec2-user 963

Again, no real surprises here. ubnt is a little bit higher than I would have thought (for Ubiquiti networking gear) but I suppose there’s a fair bit of their gear on the internet. It’s interesting to see the mix of “lazy admin” and “default credentials” here. It’s mildly interesting to me that all substrings of the first 10 digits (3 or longer) are included, except for 7 digits. I guess 7 digit passwords are less common?

Timing Information

Though I imagine these kind of untargeted scans are long-term processes continually running, I decided to check and see what the timing looked like anyway. Neither the day of week analysis nor the hour of day analysis look like there’s any significant variance.

Day of Week Hour of Day

Looking at the number of login requests over the time period where I’ve been running the honeypots shows the traffic to be intermittent. While I didn’t expect the number to be constant, the variance is much higher than I expected. I imagine a larger sample size and more nodes would probably make the results more even.

Day of Study

Analysis of Sources

So where are all of these requests coming from? I want to start by noting that none of my analysis is an attempt to attribute the actors making the requests – that’s just not possible with this kind of data. There’s two ways to look at the source of requests – in terms of the network, and in terms of the (assumed) geography. My analysis relied on the IP to ASN and IP to Country data provided by

Looking at the country-level data, networks from China lead the pack by a long shot (62% of all login attempts), followed by the US.


Country Count
CN 577789
US 87589
TW 48645
FR 39072
RU 30929
NL 29920
JP 28033
DE 15408
IN 13921
LT 6623

Again, I’m not claiming that these countries mean anything other than location of the autonomous system (AS) that originates the requests. I also did not do individual IP geolocation, so the results should be taken with a small grain of salt.

So what networks are sourcing this traffic? I have the full AS counts and data, but the top networks are:

AS Name Country ASN Count
CHINANET-BACKBONE No.31,Jin-rong Street CN 4134 202024
CHINANET-JS-AS-AP AS Number for CHINANET jiangsu province backbone CN 23650 186274
CHINA169-BACKBONE CNCGROUP China169 Backbone CN 4837 122192
HINET Data Communication Business Group TW 3462 48492
OVH FR 16276 30865
VECTANT ARTERIA Networks Corporation JP 2519 27481
DIGITALOCEAN-ASN - DigitalOcean, LLC US 14061 26965
MICROSOFT-CORP-MSN-AS-BLOCK - Microsoft Corporation US 8075 20370
AS38994 NL 38994 14482
XMGBNET Golden-Bridge Netcom communication Co.,LTD. CN 45058 12418
CNNIC-ALIBABA-CN-NET-AP Hangzhou Alibaba Advertising Co.,Ltd. CN 37963 12045
CNNIC-TENCENT-NET-AP Shenzhen Tencent Computer Systems Company Limited CN 45090 10804
CNIX-AP China Networks Inter-Exchange CN 4847 10000
PONYNET - FranTech Solutions US 53667 9317
ITTI US 44685 7960
CHINA169-BJ China Unicom Beijing Province Network CN 4808 7835
AS12876 FR 12876 7262
AS209605 LT 209605 6586
CONTABO DE 51167 6261

AS Graph

Chinanet is no surprise given the high ratio of China in general. OVH is a low-cost host known to have liberal AUP, so is popular for both malicious and research purposes. DigitalOcean and Microsoft, of course, are popular cloud providers. Surprisingly, AWS only sourced about 600 connections, unless they have a large number of IPs on a non-Amazon ASN.

Overall, traffic came from 27,448 unique IPv4 addresses. Of those, more than 11 thousand sent only a single request. At the other end of the spectrum, the top IP source sent 64,969 login requests.

Most hosts sent relatively few requests, the large numbers are outliers:

IP Count Graph

Surely, by now a thought has crossed your mind: how many of these requests are coming from Tor? Surely the Tor network is a wretched hive of scum and villany, and the source of much malicious traffic, right?

Tor Graph

Not at all. Only 219 of the unique source IPs were identified as Tor exit nodes, representing only 0.8% of the sources. On a per-request basis, even a smaller percentage of requests is seen from Tor exit nodes.

Client Software

Remember – this is self-reported by the client application, and just like I can spoof the server version string, so can clients. But I still thought it would be interesting to take a brief look at those.

client count
SSH-2.0-PuTTY 309797
SSH-2.0-PUTTY 182465
SSH-2.0-libssh2_1.4.3 135502
SSH-2.0-Go 125254
SSH-2.0-libssh-0.6.3 62117
SSH-2.0-libssh2_1.7.0 23799
SSH-2.0-libssh2_1.9.0 21627
SSH-2.0-OpenSSH_7.3 9954
SSH-2.0-OpenSSH_7.4p1 8949
SSH-2.0-libssh2_1.8.0 5284
SSH-2.0-JSCH-0.1.45 3469
SSH-2.0-PuTTY_Release_0.70 2080
SSH-2.0-PuTTY_Release_0.63 1813
SSH-2.0-OpenSSH_5.3 1212
SSH-2.0-paramiko_1.8.1 1140
SSH-2.0-PuTTY_Release_0.62 1130
SSH-2.0-OpenSSH_4.3 795
SSH-2.0-PuTTY_Release_0.66 694
SSH-2.0-OpenSSH_7.9p1 Raspbian-10+deb10u2 690
SSH-2.0-libssh_0.11 660

You know, I didn’t expect that. PuTTY as the top client strings. (Also not sure what to make of the case difference.) I wonder if people are building the PuTTY SSH library into a tool for scanning or wrapping the binary in some kind of script.

Go, paramiko, and libssh are less surprising, as they’re libraries designed for integration. It’s hard to know if the OpenSSH requests are linked into a scanning tool or just wrapped versions of the SSH client. At some point in the future, I might dive more into this and trying to figure out which software uses which libraries (at least for the publicly-known tools).


I was hoping to find something earth-shattering in this research. Instead, I found things that were much as expected – common usernames and passwords, widespread scanning, large numbers of requests. One thing’s for sure though: connect it to the internet and someone’s going to pwn it.