WinIDS 0.1.0

Windows-based Intrusion Detection System using machine learning and reinforcement learning for adaptive security

WinIDS - Windows Intrusion Detection System

A machine learning and reinforcement learning-based intrusion detection system designed for Windows environments with real-time monitoring capabilities.

Features

• 🔍 Real-time network traffic monitoring
• 🧠 Neural network-based intrusion detection
• 🤖 Reinforcement learning for adaptive thresholds
• 🚨 Detect multiple attack types: DOS, Probe, R2L, U2R
• 📊 Professional dashboard with visualizations
• 🛡️ Traffic generation and simulation capabilities
• 🔄 Bridge and Monitor components for flexible deployment
• 📈 Self-learning capabilities through RL feedback

Installation

From PyPI

pip install winids

From Source

git clone https://github.com/yourusername/winids.git
cd winids
pip install -e .

Quick Start

WinIDS provides both command-line tools and Python library components.

Using Command-line Tools

1. Start the Monitor:

   winids-monitor --host localhost --port 5000
   

2. Start the Bridge (traffic generator):

   winids-bridge --monitor-host localhost --monitor-port 5000
   

3. Launch the Dashboard:

   winids-dashboard
   

4. Test with Attack Panel (optional):

   winids-attack-panel
   

Using as a Python Library

from winids import FastIDS, IDSBridge, IDSMonitor

# Create and start the monitor
monitor = IDSMonitor(host="localhost", port=5000)
monitor.start()

# Create and start the bridge
bridge = IDSBridge(monitor_host="localhost", monitor_port=5000)
bridge.start()

# Create and start the IDS with reinforcement learning
ids = FastIDS(model_path="models/best_fast_model.h5", 
             norm_params_path="models/normalization_params.json",
             use_rl=True)
ids.connect_to_bridge()
ids.start()

# Get current stats
stats = ids.get_stats()
print(f"Uptime: {stats['uptime']}s, Packets: {stats['total_packets']}, Alerts: {stats['alerts']}")

# Stop components when done
ids.stop()
bridge.stop()
monitor.stop()

Components

FastIDS

The core intrusion detection engine using neural network models with reinforcement learning capabilities.

from winids import FastIDS

ids = FastIDS(
    model_path="models/best_fast_model.h5",
    norm_params_path="models/normalization_params.json",
    threshold=0.7,
    bridge_host="localhost",
    bridge_port=5000,
    use_rl=True,
    rl_model_dir="./rl_models",
    rl_training_mode=True
)

IDSMonitor

Connection manager between the bridge and the IDS system.

from winids import IDSMonitor

monitor = IDSMonitor(
    host="localhost",
    port=5000,
    check_interval=1.0,
    traffic_file="data/traffic_log.json",
    disable_attacks=False
)

IDSBridge

Traffic generator that connects to the monitor.

from winids import IDSBridge

bridge = IDSBridge(
    monitor_host="localhost",
    monitor_port=5000,
    data_file="data/training_data.csv",
    synthetic=True
)

ProDashboard

Graphical user interface for the IDS system.

from winids import ProDashboard, FastIDS

ids = FastIDS(model_path="models/best_fast_model.h5", use_rl=True)
dashboard = ProDashboard(ids, dark_mode=True)
dashboard.run()

AttackPanel

Tool for generating test attacks.

from winids import AttackPanel

panel = AttackPanel(
    bridge_host="localhost",
    bridge_port=5000,
    dark_mode=True
)
panel.run()

Reinforcement Learning

WinIDS uses reinforcement learning to continuously adapt and optimize its detection capabilities:

Adaptive Thresholds

The RL agent automatically adjusts detection thresholds based on:

• Historical attack patterns
• False positive rates
• System performance

from winids import FastIDS

# Create an IDS with reinforcement learning enabled
ids = FastIDS(
    model_path="models/best_fast_model.h5",
    use_rl=True,
    rl_model_dir="./custom_rl_models"
)

# RL will automatically adjust thresholds based on traffic patterns
ids.start()

Feedback Mechanism

You can provide explicit feedback to improve detection:

# Example of providing feedback to the RL system
feedback = {
    "alert_id": "alert-1234",
    "is_attack": True,  # True if this was indeed an attack, False if false positive
    "confidence": 0.85
}

# Send feedback (handled internally by the IDS)
bridge.send_feedback(feedback)

Custom RL Model Directory

Specify where to store trained RL models:

winids-dashboard --rl-model-dir /path/to/rl_models

Attack Simulation

WinIDS includes an attack simulator for testing the IDS system:

from winids.attack_simulator import simulate_attack

# Simulate a DOS attack with 75% intensity for 10 seconds
attack_data = simulate_attack(attack_type="dos", intensity=0.75, duration=10)

# Simulate a probe attack
probe_attack = simulate_attack(attack_type="probe", intensity=0.5, duration=5)

Training Custom Models

WinIDS provides scripts for training custom models:

python -m winids.scripts.train_model --dataset your_data.csv --model-output models/custom_model.h5

Command-line Options

winids-dashboard

usage: winids-dashboard [-h] [--model MODEL] [--norm-params NORM_PARAMS]
                        [--threshold THRESHOLD] [--bridge-host BRIDGE_HOST]
                        [--bridge-port BRIDGE_PORT] [--light-mode]
                        [--disable-attacks] [--disable-rl]
                        [--rl-model-dir RL_MODEL_DIR] [--disable-rl-training]

winids-bridge

usage: winids-bridge [-h] [--monitor-host MONITOR_HOST]
                     [--monitor-port MONITOR_PORT] [--interval INTERVAL]
                     [--synthetic] [--data-file DATA_FILE]

winids-monitor

usage: winids-monitor [-h] [--host HOST] [--port PORT]
                      [--check-interval CHECK_INTERVAL]
                      [--traffic-file TRAFFIC_FILE] [--disable-attacks]

winids-attack-panel

usage: winids-attack-panel [-h] [--bridge-host BRIDGE_HOST]
                           [--bridge-port BRIDGE_PORT] [--light-mode]

How Reinforcement Learning Works in WinIDS

WinIDS implements a Deep Q-Network (DQN) approach to optimize intrusion detection:

1. State: The current system state includes metrics like false positive rate, attack distribution, and current threshold.

2. Actions: The RL agent can adjust detection thresholds up or down with varying degrees.

3. Rewards: The system receives rewards for:

   • Successfully detecting real attacks
   • Avoiding false positives
   • Maintaining an optimal balance between security and performance

4. Training: The agent continuously learns from interactions with the network traffic and feedback.

5. Adaptation: The system automatically adjusts to changing network conditions and attack patterns.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgements

• Special thanks to all contributors
• Built with TensorFlow, NumPy, and other open-source libraries