Detect and warn about suspicious IPs logging into Nextcloud
The app is still under development, so it’s time for you to get involved! 👩💻
The app is shipped and comes with the installation of Nextcloud Server. No additional steps are necessary.
Old versions of this app remain available through the app store. They can be installed through Nextcloud's app management UI.
Note
Newer versions of the app are not included in the app store since it is now a shipped app.
As a shipped app:
- changes are posted within the Nextcloud Server changelog.
- releases are not posted in this GitHub repository, but they are tagged for code perusal.
- it is automatically kept up-to-date with each Nextcloud Server release.
Once this app is enabled, it will automatically start tracking (IP, uid) tuples from
successful logins on the instance and feed them into the login_address table. This
insert operation is executed for the majority of requests (client authenticate on
almost all requests) and therefore has to be fast. In a background job, these rows
will be transformed into an aggregated format that is suitable for the training of
the neural net. The (IP, uid) tuple becomes (IP, uid, first_seen, last_seen, seen) so
that we know which (IP, uid) tuple has been seen first and last. The aggregated data
is a compressed format of the raw data. The original data gets deleted and thus the
database does not need much space for the collected login data.
When enough data is collected – which by default is 60 days (!) – a first training run can be started.
The app registers a background job that invokes the training once a day. As long as there isn't sufficient data, no trained model is generated.
The training can also be invoked via the OCC command line tool:
php -f occ suspiciouslogin:trainThis command uses several sensible default that should work for instances of any size.
The --stats flag is useful to see the measured performance of the trained model
after the training finishes. The duration of the training run depends on the size
of the input training set, but is usually between two to 15 minutes.
The full list of parameters, their description and default values can be seen with
php -f occ suspiciouslogin:train --helpTo find the best possible parameters for the training it's possible to start a hyper parameter optimization run via the CLI:
php -f occ suspiciouslogin:optimizeThis command uses the heuristic simulated annealing algorithm to find optimal parameter sets in the multidimensional parameter space. By default this will do 100 steps consisting of five training runs per step, hence this command might take a few days to execute on large instances. On smaller ones it will also take a few hours.
As soon as the first model is trained, the app will start classifying (IP, uid) tuples
on login. In contrast to the data collection it won't consider requests authenticated
via an app password as suspicious. Should it detect a password login where the (IP,
uid) is classified as suspicious by the trained model, it will add an entry to the
suspicious_login table, including the timestamp, request id and URL.
By default notifications about suspicious logins contain a link (button) to lookup more info about the noted IP address (geography, ISP). This link can be disabled if it isn't desired:
occ config:app:set --value 0 suspicious_login show_more_info_button
- ☁ Clone the app into the
appsfolder of your Nextcloud:git clone https://github.com/nextcloud/suspicious_login.git - 💻 Run
npm iorkrankerl upto install the dependencies - 🏗 To build the Javascript whenever you make changes, run
npm run dev - ☁ Enable the app through the app management of your Nextcloud or run
krankerl enable - 👍 Partytime! Help fix some issues and review pull requests