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Kernel Anamoly Detector

License Issues Contributors Version Platform Kernel Development

Table of Contents

About

A kernel module for monitoring system processes and detecting anomalies as potential malware threats based on CPU and memory usage.

Installation

Note: Installation instructions to be updated as progress is made on the project

  1. Clone repo into a local project directory
  2. Open a bash terminal and follow the following commands to install linux headers: 
    sudo apt update
sudo apt install gcc
sudo apt install linux-headers-$(uname -r)
sudo apt install make

VSCode c_cpp properties

c_cpp_properties.json file has been included for use in VSCode IDE. If you are not using Code for development, delete this directory. If using Code, in a Bash shell enter the command 'uname -r' after installing the above packages. Copy the result and replace the (uname -r) portions of the json file with the value.

Build

# Navigate to the directory you cloned the module into
cd ~/module_dir_path

# Compile the kernel module
make

# Load module (insure the ko file was generated after the make build first)
sudo insmod kernel_module.ko

# Check last log to see if the module loaded
sudo dmesg | tail -1

# Unload module
sudo rmmod kernel_module

# Check last log to ensure the module unloaded
sudo dmesg | tail -1

Roadmap

This will serve as a static guide of the project roadmap. GitHub issues will be created to manage each milestone.

  1. Set Up the Development Environment and Kernel Module Skeleton
  2. Implement Process Monitoring
  3. Add Anomaly Detection Logic (Using dynamic historical statistics)
  4. Improve Logging and Report Generation
  5. Testing and Threshold Adjustment

Time Permitted Kernel-ml integration

  1. Set Up Basic Machine Learning Model in User Space (C++/Rust/Python)
  2. Integrate User Space Model with Monitoring as a second level
  3. Set Up Kernel-ML model in Kernel Space (Experimental)
  4. Integrate first level anomoly detection, to kernel-ml, to user space model
  5. Extensive Testing and ML Benchmarking

Authors