projeto "Moisés Phase 1

Project: Advanced Firewall with Behavioral Analysis and DPI

1. Dependency Installation

In addition to scapy, this project will use libraries like cryptography for encryption, sklearn for machine learning, and others for various functions.

pip install scapy cryptography scikit-learn

2. Project Structure

Create the following directory and file structure:

firewall/
│
├── main.py
├── config.py
├── packet_inspection.py
├── encryption.py
├── anomaly_detection.py
└── logging_service.py

3. config.py

This file will contain the firewall settings, such as blocking rules and encryption keys.

# config.py

# List of blocked domains
BLOCKED_DOMAINS = ["example.com", "badwebsite.com"]

# List of blocked IPs
BLOCKED_IPS = ["192.168.1.1", "10.0.0.1"]

# Encryption key (generate a key with a secure generator)
ENCRYPTION_KEY = b'YOUR_256_BIT_KEY'

# Machine learning configuration
ML_MODEL_PATH = "ml_model.pkl"

4. packet_inspection.py

This module is responsible for deep packet inspection.

# packet_inspection.py

from scapy.all import *

def deep_packet_inspection(packet):
    if IP in packet:
        ip_src = packet[IP].src
        ip_dst = packet[IP].dst

        if TCP in packet:
            payload = packet[TCP].payload
            # Perform more detailed payload analysis here
            return ip_src, ip_dst, payload

    return None, None, None

5. encryption.py

This module encrypts and decrypts the captured data.

# encryption.py

from cryptography.fernet import Fernet
from config import ENCRYPTION_KEY

cipher = Fernet(ENCRYPTION_KEY)

def encrypt_data(data):
    return cipher.encrypt(data)

def decrypt_data(data):
    return cipher.decrypt(data)

6. anomaly_detection.py

This module implements anomaly detection using machine learning.

# anomaly_detection.py

import joblib
from sklearn.ensemble import IsolationForest
from config import ML_MODEL_PATH

def load_model():
    return joblib.load(ML_MODEL_PATH)

def detect_anomaly(features, model):
    prediction = model.predict([features])
    return prediction[0] == -1  # Returns True if it is an anomaly

7. logging_service.py

This module logs all firewall activities.

# logging_service.py

import logging

logging.basicConfig(filename='firewall.log', level=logging.INFO)

def log_packet(packet_info, action):
    logging.info(f"Packet: {packet_info}, Action: {action}")

8. main.py

The main file that integrates all modules and manages packet flow.

# main.py

from scapy.all import sniff, send
from packet_inspection import deep_packet_inspection
from encryption import encrypt_data, decrypt_data
from anomaly_detection import load_model, detect_anomaly
from logging_service import log_packet
from config import BLOCKED_IPS, BLOCKED_DOMAINS

def process_packet(packet):
    ip_src, ip_dst, payload = deep_packet_inspection(packet)
    if ip_src and ip_dst:
        # IP blocking
        if ip_src in BLOCKED_IPS or ip_dst in BLOCKED_IPS:
            log_packet(f"{ip_src} -> {ip_dst}", "Blocked")
            return False

        # Domain blocking
        if any(domain in str(payload) for domain in BLOCKED_DOMAINS):
            log_packet(f"{ip_src} -> {ip_dst}", "Blocked (Domain)")
            return False

        # Anomaly analysis
        model = load_model()
        features = [len(payload), packet[IP].ttl]  # Example of features
        if detect_anomaly(features, model):
            log_packet(f"{ip_src} -> {ip_dst}", "Blocked (Anomaly)")
            return False

        # Encrypt the packet data for secure storage
        encrypted_payload = encrypt_data(bytes(payload))
        log_packet(f"{ip_src} -> {ip_dst} (Encrypted)", "Allowed")

    return True

if __name__ == "__main__":
    print("Advanced Firewall Started...")
    sniff(filter="ip", prn=lambda packet: process_packet(packet) and send(packet))

Functionality Explanation

Deep Packet Inspection (DPI): The packet_inspection.py module performs a detailed analysis of the packet contents. This is the first step in identifying potentially dangerous packets.
Encryption: The encryption.py module is used to encrypt sensitive packet data that is stored or processed, enhancing security.
Anomaly Detection with ML: The anomaly_detection.py module uses machine learning to identify packets with suspicious characteristics, such as a very long payload or an anomalous Time to Live (TTL).
Logging and Monitoring: The logging_service.py module maintains a detailed log of all firewall actions, allowing for auditing and later review.
Blocking and Permitting: The main.py file integrates everything, inspecting packets in real-time and deciding whether they should be blocked, allowed, or analyzed further.

How to Use

Train the ML Model: You need to train a machine learning model with network data to detect anomalies. This can be done with sklearn and then saving the trained model as ml_model.pkl.
Configure the Firewall: Modify config.py to include the appropriate domains, IPs, and the encryption key.
Run the Firewall: Run the main script as a superuser:

sudo python3 main.py

Legal Disclaimer

This code is for educational and experimental purposes. Developing and using an advanced firewall like this must comply with the cybersecurity laws in your region. It is crucial to understand the ethical and legal implications before implementing such a system in a real environment.

googlecodelabs / tools