Unsupervised Machine Learning web attacks detection.
Image source:https://unsplash.com/photos/i4Y9hr5dxKc (Mathew Schwartz)
Webhawk is an ML/AI powered detection tool designed to automatically identify attack traces in application logs (Eg: HTTP) without relying on preset rules. Using unsupervised machine learning, Webhawk groups log entries into clusters and detects outliers that may indicate potential attack traces. After detection, Webhawk leverages Agentic AI to provide detailed analysis and actionable recommendations.
Webhawk comes with a user-friendly web interface. Webhawk UI allows SOC team members to easily manage and review the detections. Detection results can also be fed to your existing SOC ecosystem (Eg: SIEM) thanks to the builtin API.
Webhawk transforms raw logs into numerical data and applies Principal Component Analysis (PCA) to extract the most relevant features (such as user-agent, IP address, and the number of transmitted parameters). It then uses the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to cluster log lines and identify anomalous points, which may represent potential attack traces.
Advanced users can fine-tune Webhawk through a set of configuration options that optimize the clustering algorithm—for example, by adjusting the minimum number of points per cluster or the maximum distance between points within the same cluster.
Once detections are complete, the results are sent to an LLM of your choice for in-depth analysis and actionable recommendations. Webhawk leverages agentic AI design patterns to ensure that the insights and suggestions delivered to users provide meaningful, real-world value.
To get data from your endpoints, Webhawk provide an agent that your can easily deploy on every endpoint. Once launched, this agent gets logs from the concerned file and transmit them to the detection engine via the API.
The tool is easy to deploy using Docker Compose, which simplifies the installation of the three main components: the detection engine, the web application, and the LLM services.
Details about setting up this configuration file can be found in Development setup/Create a settings.conf file section below.
docker compose build
docker compose upOnce the above command are launched, three services will be running:
This is the service used for detection, it takes as input a log file and it return detections.
This service is used to make prompt for LLMs and getting response.
This service is used to run the web application where the detections results will be treated by cyber analysts.
If you are launching the Docker services after the first build then you need to expect some delay for the first agent request. In fact this delay is related to downloading of the selected Ollama model, note that this delay will disappear if you have already added this model pulling to ./ollama_starter.sh.
To make detection within your endpoints you need to configure and execute webhawk agent which is available in ./webhawk_agent.
python webhawk_agent.py -l ./HTTP_LOGS/access.log.2025-02-08Once the execution is done new incidents will appear in Webhawk web application.
The goal of this section is to help using webhawk detection, without using the web application. For the next steps you need to be in ./webhawk folder.
python -m venv webhawk_venv
source webhawk_venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txtCopy settings.conf.template 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']
[CVE]
source:https://services.nvd.nist.gov/rest/json/cves/2.0?keywordSearch=
year_threshold:YYYY
[LLM]
url:http://ollama:11434 #if using docker compose
url:http://localhost:11434 #if not using docker compose
model:intigration/analyzer:latest #or select a model of your choice
prompt:Analyze this web log line for malicious activity. Provide a brief one pragraph (less than 60 words) as a response. Indidate if there is a known related attack or vulnerability. Do not start with 'This log line'
[WEBAPP]
webhawk_ui
url:http://webhawk_ui:8080/api/v1/incidents #if using docker compose
url:http://localhost:8080/api/v1/incidents #if not using docker composepython 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] [-a] [-q]
options:
-h, --help show this help message and exit
-l, --log_file LOG_FILE
The raw log file
-t, --log_type LOG_TYPE
apache, http, nginx or os_processes
-e, --eps EPS DBSCAN Epsilon value (Max distance between two points)
-s, --min_samples MIN_SAMPLES
Minimum number of points with the same cluster. The default value is 2
-j, --log_lines_limit LOG_LINES_LIMIT
The maximum number of log lines of consider
-y, --opt_lamda OPT_LAMDA
Optimization lambda step
-m, --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
-a, --get_ai_advice Get AI advice on the detection
-q, --quick_scan Only most critical detection (no minority clusters)
-f, --submit_to_app Submit the finding to Webhawk appEncoding 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.2022-12-22 --log_type apache --standardize_data --report --find_cves --get_ai_adviceThe 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 --reportWebhawk API can be launched using the following command:
uvicorn app:app --reloadTesting the API using: The API can be tested using the script api_test.py or by launching the follwoing python commands:
import requests
with open("./SAMPLE_DATA/RAW_APACHE_LOGS/access.log.2017-05-24",'r') as f:
logs=str(f.read())
params = {"hostname":"nothing","logs_content":logs}
response=requests.post("http://127.0.0.1:8000/scan",json=params)
print(response.json())The data you will find in ./SAMPLE_DATA folder comes from
https://www.secrepo.com.
You can alos generate test data using the script ./TESTING_LOGS_GENERATOR/apache_http_log_generator.py
https://www.kaggle.com/datasets/eliasdabbas/web-server-access-logs https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/3QBYB5
Adding more details to the high level design digaram Adding finding one by one to Webhawk UI Enhancing the UI Decoupling data transfer using Kafka (or equivalent)
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 feedback, testing, and contributions are very welcome! If you would like to contribute, fork the project, add your changes, and submit a pull request.