🧰 DIY: BUILD A SIMPLE AI FOR NETWORK TRAFFIC ANALYSIS

Monitoring network traffic manually is time-consuming and prone to human error. By building a simple AI-driven system, you can automate the detection of unusual patterns and potential threats in your network. This DIY guide walks you through creating a basic machine learning model to analyze network traffic for anomalies.

🔍 Step 1: Define Your Objective
Decide what you want your AI to detect:

• Unusual bandwidth usage

• Unauthorized access attempts

• Suspicious packet patterns (e.g., port scanning, DDoS indicators)

📦 Step 2: Collect Network Data
Use tools like Wireshark, Zeek, or NetFlow to capture packet-level data. Alternatively, use open datasets like:

• CICIDS 2017

• UNSW-NB15

• NSL-KDD

Ensure your dataset includes both normal and malicious traffic labeled accordingly.

🧹 Step 3: Preprocess the Data
Clean and prepare the data:

• Remove redundant fields

• Encode categorical variables (e.g., protocol type)

• Normalize numerical features (e.g., packet length, flow duration)

Tools like Pandas and Scikit-learn in Python are ideal for this.

🧠 Step 4: Choose a Machine Learning Model
Start simple:

• Decision Trees or Random Forest: Easy to interpret

• K-Nearest Neighbors (KNN): Good for anomaly detection

• Logistic Regression: Works well for binary classification

Later, try Autoencoders or LSTMs for more advanced, deep learning-based models.

🧪 Step 5: Train and Evaluate the Model
Split the data into training and testing sets. Use metrics like:

• Accuracy

• Precision and recall

• Confusion matrix

• ROC-AUC curve

This helps assess whether your model can effectively distinguish between normal and suspicious activity.

⚙️ Step 6: Deploy a Simple Monitoring Script
Use Python to build a lightweight monitoring script that:

• Captures live traffic (with libraries like scapy or socket)

• Extracts the same features used in training

• Applies your trained model to flag anomalies in real time

📈 Step 7: Visualize and Log Alerts
Use tools like Matplotlib, Seaborn, or Plotly to visualize traffic patterns and flagged events. Log anomalies to a file or send alerts via email or messaging apps.