Unsupervised Machine Learning web attacks detection.
Image source:https://unsplash.com/photos/i4Y9hr5dxKc (Mathew Schwartz)
Webhawk/Catch helps automatically finding web attack traces in HTTP logs and abnormal OS processes without using any preset rules. Based on the usage of Unsupervised Machine Learning, Catch groups log lines into clusters, and detects the outliers that it considers as potentially attack traces.
The tool is able to parse both raw HTTP log files (Apache, Nginx, ...) and files including OS statistics (generated by top command). The tool takes these files as input and returns a report with a list of findings.
Catch uses PCA (Principal Component Analysis) technique to select the most relevant features (Example: user-agent, IP address, number of transmitted parameters, etc.. ). Then, it runs DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to get all the possible log line clusters and anomalous points (potential attack traces).
Advanced users can fine tune Catch based on a set of options that help optimising the clustering algorithm (Example: minimum number of points by cluster, or the maximum distance between two points within the same cluster).
The current version of Webhawk/Catch generates an easy-to-read HTML report which includes all the findings, and the severity of each one.
Webhawk/Catch is an open-source tool. Catch is the unsupervised version of Webhawk which is a supervised machine learning based cyber-attack detection tool. In contrary to the supervised Webhawk, Catch can be used without manually pertaining a model, the thing that makes it a lightweight and flexible solution to easily identify potential attack traces. Catch is available as an independent repository in Github, it is also included as part of Webhawk which is starred 125 times and forked 68 times.
python -m venv webhawk_venv
source webhawk_venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txtCopy settings_template.conf file to settings.conf and fill it with the required parameters as the following.
[FEATURES]
features:length,params_number,return_code,size,upper_cases,lower_cases,special_chars,url_depth,user_agent,http_query,ip
[LOG]
apache_regex:([(\d\.)]+) - - \[(.*?)\] "(.*?)" (\d+) (.+) "(.*?)" "(.*?)"
apache_names:["ip","date","query","code","size","referrer","user_agent"]
nginx_regex:([(\d\.)]+) - - \[(.*?)\] "(.*?)" (\d+) (\d+) (.+) "(.*?)" "(.*?)"
nginx_names:["ip","date","query","code","size","referrer","user_agent"]
http_regex:^(\d*?\.\d*?)\t.*?\t(.*?)\t.*?\t.*?\t.*?\t.*?\t(.*?\t.*?\t.*?\t.*?)\t(.*?)\t.*?\t(.*?)\t(.*?)\t.*$
http_names:["date","ip","query","user_agent","size","code"]
apache_error:
nginx_error:
[PROCESS_DETAILS]
attributes:['status', 'num_ctx_switches', 'memory_full_info', 'connections', 'cmdline', 'create_time', 'num_fds', 'cpu_percent', 'terminal', 'ppid', 'cwd', 'nice', 'username', 'cpu_times', 'memory_info', 'threads', 'open_files', 'name', 'num_threads', 'exe', 'uids', 'gids', 'memory_percent', 'environ']python catch.py -h
usage: catch.py [-h] -l LOG_FILE -t LOG_TYPE [-e EPS] [-s MIN_SAMPLES] [-j LOG_LINES_LIMIT] [-y OPT_LAMDA] [-m MINORITY_THRESHOLD] [-p] [-o] [-r] [-z] [-b] [-c] [-v]
options:
-h, --help show this help message and exit
-l LOG_FILE, --log_file LOG_FILE
The raw log file
-t LOG_TYPE, --log_type LOG_TYPE
apache, http, nginx or os_processes
-e EPS, --eps EPS DBSCAN Epsilon value (Max distance between two points)
-s MIN_SAMPLES, --min_samples MIN_SAMPLES
Minimum number of points with the same cluster. The default value is 2
-j LOG_LINES_LIMIT, --log_lines_limit LOG_LINES_LIMIT
The maximum number of log lines of consider
-y OPT_LAMDA, --opt_lamda OPT_LAMDA
Optimization lambda step
-m MINORITY_THRESHOLD, --minority_threshold MINORITY_THRESHOLD
Minority clusters threshold
-p, --show_plots Show informative plots
-o, --standardize_data
Standardize feature values
-r, --report Create a HTML report
-z, --opt_silouhette Optimize DBSCAN silouhette
-b, --debug Activate debug logging
-c, --label_encoding Use label encoding instead of frequeny encoding to encode categorical features
-v, --find_cves Find the CVE(s) that are related to the attack traces
Encoding is automatic for the unsupervised mode. You just need to run the catch.py script. Get inspired from this example:
python catch.py -l SAMPLE_DATA/RAW_APACHE_LOGS/access.log.2021-10-22 --log_type apache --show_plots --standardize_data --reportThe output of this command is:
Before running the catch.py, you need to generate a .txt file containing the OS process statistics by taking advantage of top command:
top > PATH/os_processes.txtYou can then run the catch.py to detect potential abnormal OS processes:
python catch.py -l PATH/os_processes.txt --log_type os_processes --show_plots --standardize_data --reportThe data you will find in SAMPLE_DATA folder comes from
https://www.secrepo.com.
https://www.kaggle.com/datasets/eliasdabbas/web-server-access-logs https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/3QBYB5
Nothing for now.
Silhouette Effeciency
https://bioinformatics-training.github.io/intro-machine-learning-2017/clustering.html
Optimal Value of Epsilon
https://towardsdatascience.com/machine-learning-clustering-dbscan-determine-the-optimal-value-for-epsilon-eps-python-example-3100091cfbc
Max curvature point
https://towardsdatascience.com/detecting-knee-elbow-points-in-a-graph-d13fc517a63c
All feedbacks, testing and contribution are very welcome! If you would like to contribute, fork the project, add your contribution and make a pull request.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent