Slips is a behavioral-based Python intrusion prevention system that uses machine learning to detect malicious behaviors in the network traffic. Slips was designed to focus on targeted attacks, detection of command and control channels to provide good visualisation for the analyst. Slips is a modular software.
Now Slips can be run inside a docker if you want to analyze flow or pcap files. If you need to analyze the traffic of your computer (access to the network card) then, for now, you need to install Slips in your own computer.
docker run -it --rm --net=host stratosphereips/slips:latest
./slips.py -c slips.conf -f dataset/test3.binetflow
mkdir ~/dataset
cp <some-place>/myfile.pcap ~/dataset
docker run -it --rm --net=host -v ~/dataset:/StratosphereLinuxIPS/dataset stratosphereips/slips:latest
./slips.py -c slips.conf -f dataset/myfile.pcap
To build the docker locally from the Docker file, you can do as follows (we use --no-cache so the git clone is done all the time). If you cloned StratosphereLinuxIPS in '~/code/StratosphereLinuxIPS', then you can build the Docker image with:
cd docker
docker build --no-cache -t slips -f Dockerfile .
docker run -it --rm --net=host -v ~/code/StratosphereLinuxIPS/dataset:/StratosphereLinuxIPS/dataset slips
./slips.py -c slips.conf -f dataset/test3.binetflow
You can now put pcap files or other flow files in the ./dataset/ folder and analyze them
cp some-pcap-file.pcap ~/code/StratosphereLinuxIPS/dataset
docker run -it --rm --net=host -v ../dataset/:/StratosphereLinuxIPS/dataset slips
./slips.py -c slips.conf -f dataset/some-pcap-file.pcap
git clone https://github.com/stratosphereips/StratosphereLinuxIPS.git
- python 3.7+
- Redis database (In debian/ubuntu: apt-get install redis)
- python3 -m pip install --upgrade pip (Be sure your pip3 is the latest)
- python3 -m pip install redis (>3.4.x)
- python3 -m pip install maxminddb
- python3 -m pip install watchdog
- python3 -m pip install validators
- python3 -m pip install urllib3
- python3 -m pip install sklearn (for the ML modules, ignore if you ignore the package)
- python3 -m pip install numpy (for the ML modules, ignore if you ignore the package)
- python3 -m pip install tensorflow (for the ML modules, ignore if you ignore the package)
- python3 -m pip install keras (for the ML modules, ignore if you ignore the package)
- python3 -m pip install pandas (for the ML modules, ignore if you ignore the package)
- python3 -m pip install certifi (for the VirusTotal module)
- python3 -m pip install colorama
- Zeek (https://zeek.org/get-zeek/)
For using Kalipso interface you need to have:
- apt-get install node.js
- apt-get install npm
With npm you should install the following libraries
- npm install blessed
- npm install blessed-contrib
- npm install redis
- npm install async
- npm install chalk
- npm install strip-ansi
- npm install clipboardy
- npm install fs
- npm install sorted-array-async
- npm install yargs
- In Linux, as a daemon: redis-server --daemonize yes
- In macOS, as a daemon: sudo port load redis
- By hand and leaving redis running on the console in the foreground: redis-server /opt/local/etc/redis.conf
Slips uses Zeek to generate files for most input types.
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How to install and set Zeek (Bro) properly?
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Download a binary package ready for your system. Complete up to date instructions here https://zeek.org/get-zeek/
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Make Zeek visible for Slips. Some ideas:
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Create a link to "/bin" folder from compiled Zeek folder like
"sudo ln -s /opt/zeek/bin/zeek /usr/local/bin""sudo ln -s PATH_TO_COMPILED_ZEEK_FOLDER/bin/zeek /usr/local/bin"
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- Start Redis:
redis-server --daemonize yes - Run Slips:
./slips.py -c slips.conf -i <interface>(be sure you use python3) - Use Kalipso to see the results a. ./kalipso.sh b. Optionally run slips with -G to start Kalipso automatically
- Local logs are stored in the folder called with the date of today. All files are updated every 5 seconds.
Requirements to capture your own traffic:
- curl
- In debian/ubuntu:
apt-get install curl
- In debian/ubuntu:
- get authorization to zeek to capture the traffic in the linux interface:
setcap cap_net_raw,cap_net_admin=eip /usr/local/zeek/bin/zeek
Slips works at a flow level, instead of a packet level, gaining a high level view of behaviors. Slips creates traffic profiles for each IP address that appears in the traffic. A profile contains the complete behavior of an IP address. Each profile is divided into time windows. Each time window is 1 hour long by default and contains dozens of features computed for all connections that start in that time window. Detections are done in each time window, allowing the profile to be marked as uninfected in the next time window.
#Slips processes When Slips is run, it spawns several child processes to manage the I/O, to profile attackers and to run the detection modules. It also connects to the Redis database to store all the information. In order to detect attacks, Slips runs its Kalipso interface.
- Every time we receive something, we update the TW modification time in a new redis ordered set (zset) (the old is not used). This set has the TW id and the time of last modification. The TW in this zset are never deleted.
- We update the slips_internal_time, every time we receive anything
- Every 5 seconds, slips.py, retrieves the sublist of TW from the zset that were modified in the last “time window width” (default 1hs).
- This list of TW that were modified is used to print the statistics and as part of the set_host_ip check in slips.py.
- In theory if a TW was not modifed in the last hs, then it should not appear in the list, which decreases until no TW was modified for some rounds, and we stop slips.
Zeek has a parameter called "tcp_inactivity_timeout". By default is 5 minutes, like in TCP. However, it may happen that due to different circonstances there is more than 5 minutes delay between packets (even in normal connections from Google). Since Slips usually has a time window width of 1hs, it can wait more until having the complete flow. For this reason Slips modifies the "tcp_inactivity_timeout" to be 1hs.
The input process reads flows of different types: -Pcap files (internally using Zeek) -Packets directly from an interface (internally using Zeek) -Suricata flows (from JSON files created by Suricata, such as eve.json) -Argus flows (CSV file separated by commas or TABs) -Zeek/Bro flows from a Zeek folder with log files -Nfdump flows from a binary nfdump file All the input flows are converted to an internal format. So once read, Slips works the same with all of them.
The output process collects output from the modules and handles collected data display. Currently Slips creates log files as an output and runs a graphical user interface Kalipso.
The log files of Slips are stored in a folder called as the current date-time. So that multiple executions will not override the results. Inside this folder there is a folder per a IP address that is being profiled. See Section Architecture of Operation to understand which IP addresses are converted into profiles. Apart from the folders of the profiles, some files are created in this folder containing information about the complete capture, such as Blocked.txt that has information about all the IP addresses that were detected and blocked.
Inside the folder of each profile there are three types of files: time window files, timeline file and profile file.
Each of these files contains all the features extracted for this time window and its name is the start-time of the time window.
The timeline file is created by the timeline module and is a unique file interpreting what this profile IP address did.
This file contains generic features of the profile that are not part of any individual time-window, such as information about its Ethernet MAC address.
This is the new version of the Stratosphere IPS, a behavioral-based intrusion detection and prevention system that uses machine learning algorithms to detect malicious behaviors. It is part of a larger suite of programs that include the Stratosphere Windows IPS and the Stratosphere Testing Framework.
Example of specific usage: Slips can be used by passing input files:
./slips.py -c slips.conf -f dataset/test3.binetflow, where -c is the configuration file, -f is the binetflow input file
Other parameters for different input types: -r is for pcap -f <filename: is for binetflow files -f <folder name: for zeek folders with log files -b is for nfdump -i interface
To read your own packets you can do: sudo ./slips.py -c slips.conf -i
Kalipso is the Nodejs-based console interface of Slips. It works by reading the Redis datbase and showing you the results. You start it automatically by running Slips with -G option or manually by node ips_timewindows.js from StratosphereLinuxIPS/modules/blessed. You can exit Kalipso by pressing q or Control-C.
Modules are Python-based files that allow any developer to extend the functionality of Slips. They process and analyze data, perform additional detections and store data in Redis for other modules to consume. Currenty, Slips has the following modules: asn - module to load and find the ASN of each IP geoip - module to find the country and geolocation information of each IP https - module to train or test a RandomForest to detect malicious https flows port scan detector - module to detect Horizontal and Vertical port scans threat Intelligence - module to check if each IP is in a list of malicious IPs timeline - module to create a timeline of what happened in the network based on all the flows and type of data available VirusTotal - module to lookup IP address on VirusTotal Kalipso - graphical user interface to display analyzed traffic by Slips _Long Flows - In the module that analyzes individual flows. It check if a flow is too long _SSH Success - In the module that analyzes individual flows. It checks if the SSH connections were successful. Uses Zeek method and Slips method. The behavioral models are stored in the models folder and will be updated regularly. In this version you should pull the git repository by hand to update the models.
The core of the Slips program is not only the machine learning algorithm, but more importantly the behavioral models that are used to describe flows based on flows' duration, size, and periodicty. This is very important because the models are curated to maximize the detection. More about behavioral models is in Stratosphere Testing Framework.
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- Slips runs in
- Ubuntu 16.04+
- Debian stable/testing/unstable
- MacOS 10.9.5, 10.10.x to 10.12.x
- To try:
- Android
- IOS
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We are developing a new module lstm-cc-detection. This is a module to detect command and control channels using LSTM neural network and the stratosphere behavioral letters
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If you see the error
Error in run() of timeout must be non-negative <class 'ValueError'> timeout must be non-negative
It means that you have an older version of the xxx library. Please update to version >
Fails python3-redis 3.0.1 python3-redis 3.3.11 ii redis-server 5:5.0.2-1 amd64 Persistent key-value database with network interface ii redis-server 5:5.0.6-1 amd64 Persistent key-value database with network interface
Works redis (2.10.5)
VM apt python3-redis 3.3.11 ii redis-server 5:5.0.6-1 amd64 Persistent key-value database with network interface
jin apt ii redis-server 4:4.0.1-7 amd64 Persistent key-value database with network interface pip redis 3.2.1
In dockers running on CPU that dont support AVX, importing tensorflow may fail. In that case you need to uninstall tensoflow and install a wheel file without AVX, such as
https://tf.novaal.de/barcelona/tensorflow-2.4.1-cp37-cp37m-linux_x86_64.whl
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- Main author: Sebastian Garcia. [email protected], [email protected].
- Ondrej Lukas: During the original slips code, he worked on the new detection metric of infected IPs based on timewindows, detection windows, weighted scores and averages. Also all the ip_handler, alerts classes, etc.
- Frantisek Strasak. Work on all the new version of slips, features, output, core and the https Machine Learning detection module. (https://github.com/frenky-strasak)
- Dita hollmannova: Worked in the VirusTotal module and the Whois modul. ([email protected])
- Kamila Babayeva: Implemented the NodeJS interface ([email protected])
- Elaheh Biglar Beigi
- MariaRigaki
- kartik88363
- arkamar