https://github.com/JPCERTCC/DetectLM

Detecting Lateral Movement with Machine Learning
https://github.com/JPCERTCC/DetectLM

deep-learning elasticsearch kibana machine-learning powershell python security

Last synced: about 1 month ago
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Detecting Lateral Movement with Machine Learning

• Host: GitHub
• URL: https://github.com/JPCERTCC/DetectLM
• Owner: JPCERTCC
• Archived: true
• Created: 2017-10-31T09:02:30.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2017-10-31T09:24:55.000Z (about 7 years ago)
• Last Synced: 2024-08-02T13:31:30.857Z (4 months ago)
• Topics: deep-learning, elasticsearch, kibana, machine-learning, powershell, python, security
• Language: Python
• Size: 389 KB
• Stars: 138
• Watchers: 12
• Forks: 38
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# DetectLM

  Detecting Lateral Movement with Machine Learning.  

  DetectLM is a proof of concept code to analyze and detect malicious commands executed via cmd.exe with machine learning.    

  ![DetectLM Architecture](images/system.png)

* **DetectLM.py**: Runs on server. Detect malicious Windows command execution using machine learning and blacklist.

* **Invoke-DetectLM.ps1**: Runs on client. Sends the logs to the Elasticsearch. Confirm analysis result and display alert message.

* **cmdlogs.bat**: Collect commands executed via cmd.exe.

## Requirements

  Each tools require the following modules:

### for Server

* Python3

* NumPy http://www.numpy.org/

* Pandas http://pandas.pydata.org/

### for Client

* PowerShell v3 or later

## Supported Client OS

* Windows 8.1

* Windows 10

## Usage

### for Server

1. Download and install [Elasticsearch](https://www.elastic.co/downloads/elasticsearch).  

2. Download and install [Kibana](https://www.elastic.co/downloads/kibana).  

3. Start Elasticsearch and Kibana.  

4. Download and save the DetectLM from Github.

  ```

  git clone https://github.com/JPCERTCC/DetectLM.git

  ```

5. And your Elasticsearch IP Address to **DetectLM/server/config/config.ini**.  

  ```

  els_server      = localhost

  ```

6. And mapping in Elasticsearch.

  ```

  $ python DetectLM.py -m

  ```

7. Create cron jobs for **DetectLM.py**.  

  For example  

  ```

  */15 * * * * python3.6 [Folder Name]/DetectLM.py

  ```

### for Client

1. Download **client/cmdlogs.bat** and **client/Invoke-DetectLM.ps1**.  

2. Register **cmdlogs.bat** in the registry entry that starts automatically when the cmd.exe is executed.  

  ```

  > reg add "HKEY_CURRENT_USER\Software\Microsoft\Command Processor" /v AutoRun /d [Downloaded Folder Name]\cmdlogs.bat

  ```

  or

  ```

  > reg add "HKEY_LOCAL_MACHINE\Software\Microsoft\Command Processor" /v AutoRun /d [Downloaded Folder Name]\cmdlogs.bat

  ```

3. Create a scheduled task for **Invoke-DetectLM.ps1**.

  ```

  > powershell -exec bypass .\Invoke-DetectLM.ps1 -ehost [Elasticsearch Server]

  ```

## How to Check Log

  Executed command logs can be checked from Kibana.  

  AlertLevel 2 is the malicious commands.

  ![Kibana GUI](images/kibana.png)

### AlertLevel

  The log has three levels of detection level  

* **2** : Error log reported by user

* **1** : Logs detected as malicious by machine learning

* **0** : No Alert (Default)

### Ignore Flag

  Ignore flag is automatically added to any command executed by the user.    

  When malicious Windows command execution is detected by machine learning, a notification will be sent to the client.  

  A user also can set a ignore flag to specific command execution when asked by client tool.  

  ![Alert example](images/alert.png)

### Dashboard

  Using Kibana dashboard, logs are visualized.  

  Example of dashboard is in **kibana_objects**. Import is [Management -> Saved Objects -> Import]

  ![Kibana dashbord](images/kibana_dashboard.png)

## Known Issues

* This tool supports Windows commands executed via cmd.exe. Any command via PowerShell are not logged nor monitored.

* Client tool has an issue when handling command line includes "/c" or "/k".