Overview / Usage

This project centers on the analysis of network traffic data and the detection of anomalies within it. Network traffic data, which includes information about communication between devices or systems over a network, can be vast and complex. Detecting unusual patterns or behaviors within this data is crucial for network security, system optimization, and performance monitoring.

Problems Being Solved:

1. Anomaly Detection: The primary problem addressed by this project is the detection of anomalies within network traffic data. Anomalies can indicate security breaches, network issues, or unusual behaviors that require attention. By identifying these anomalies, organizations can respond promptly to potential threats or problems.
2. Data Preprocessing: Network traffic data is often raw and unstructured. This project tackles the challenge of preprocessing this data to make it suitable for machine learning models. Data preprocessing includes tasks such as feature extraction, encoding categorical variables, and scaling data.
3. Autoencoder-Based Approach: An autoencoder neural network architecture is used for anomaly detection. Autoencoders are well-suited for capturing complex patterns in data and reconstructing it. In this context, they can learn to represent normal network traffic and flag deviations from this norm.

Methodology / Approach

The methodology for addressing anomaly detection in network traffic data involves several key steps and utilizes specific technologies and techniques:

1. Data Collection: The first step is to gather network traffic data, which includes information about communication between devices or systems. This data is often collected from network monitoring tools or devices.

2. Data Preprocessing: Network traffic data is often raw and unstructured. It needs to be preprocessed to make it suitable for machine learning models. Key preprocessing steps include:

   • Feature selection: Choosing relevant features from the data.
   • Categorical encoding: Converting categorical variables like protocol types into numerical values.
   • Scaling: Ensuring that all features have the same scale for modeling.

3. Autoencoder Architecture: An autoencoder neural network architecture is employed for anomaly detection. This architecture consists of an encoder and a decoder. The encoder learns to compress the input data into a lower-dimensional representation, and the decoder tries to reconstruct the original input. The key idea is that the model should perform well on normal data but struggle with anomalies.

4. Model Training: The autoencoder model is trained using the preprocessed data. During training, the model learns to represent normal network traffic patterns.

5. Anomaly Detection: Once the model is trained, it can be used for anomaly detection. This is achieved by comparing the input data with the model's reconstruction. Anomalies are data points that deviate significantly from their reconstructions.

6. Threshold Setting: To distinguish between normal and anomalous data, a threshold for the reconstruction error is set. Data points with errors exceeding this threshold are flagged as anomalies.

7. Evaluation: The model's performance is evaluated using metrics like precision, recall, and F1-score. This helps in assessing the accuracy of anomaly detection and fine-tuning the threshold if needed.

Technology Stack and Techniques:

• Python: The primary programming language used for data preprocessing, modeling, and evaluation.
• Libraries: Key libraries like Pandas, NumPy, Scikit-learn, and TensorFlow are utilized for data manipulation, machine learning, and deep learning tasks.
• Autoencoders: A deep learning technique employed for learning complex data representations and detecting anomalies.
• Isolation Forest: An ensemble machine learning algorithm used as an alternative anomaly detection method.
• Data Visualization: Matplotlib is used for creating visualizations, such as bar plots to display anomalies.
• Data Scaling: Min-max scaling is applied to ensure that features have the same scale.
• Evaluation Metrics: Precision, recall, F1-score, and ROC-AUC are used to evaluate model performance.
• Contamination Parameter: In the Isolation Forest, the contamination parameter is adjusted to control the expected proportion of anomalies.
• Data Exploration: Data exploration techniques are used to understand the distribution of data and identify patterns.

Technologies Used

Technologies and Libraries:

1. Python: The primary programming language for data preprocessing, modeling, and evaluation.
2. Pandas: Used for data manipulation and analysis, including reading and processing network traffic data.
3. NumPy: Essential for numerical computations and working with arrays.
4. Scikit-learn: Provides machine learning tools, including the Isolation Forest algorithm for anomaly detection and various evaluation metrics.
5. TensorFlow: Used for implementing deep learning models, specifically autoencoders for anomaly detection.
6. Matplotlib: Utilized for creating data visualizations, such as bar plots to visualize anomalies.
7. dpnp: A library for array manipulation, particularly used for working with NumPy-like arrays.
8. Intel oneAPI for TensorFlow: An optimized version of TensorFlow designed to leverage Intel hardware acceleration, including the oneDNN (Deep Neural Network Library).
9. sklearnex: A package used to patch the scikit-learn (sklearn) library, enabling seamless integration with TensorFlow models.

Repository

https://github.com/balasuriyaranganathan/Network-Traffic-Analysis-using-Machine-learning