davedawitdave / Network-security-analysis-and-botnet-prediction

Network-security-analysis-and-prediction

Project Overview

This project aims to detect botnet activities in network traffic using machine learning and data analysis techniques. Botnets pose significant threats to network security, and timely detection can prevent widespread damage. The project encompasses data preprocessing, exploratory data analysis, feature selection, model building, and hyperparameter tuning.

Table of Contents

1. Objective
2. Data
3. Exploratory Data Analysis (EDA)
4. Feature Engineering and Selection
5. Model Building
6. Hyperparameter Tuning
7. Clustering
8. Results and Evaluation
9. Conclusion
10. Usage

Objective

The primary objective of this project is to develop an effective botnet detection system using various machine-learning models. This includes:

• Preprocessing and analyzing network traffic data.
• Extracting and selecting relevant features.
• Building and tuning machine learning models for classification.
• Evaluating model performance using various metrics.

Data

The dataset used in this project consists of network traffic data with the following characteristics:

• Source_Type_of_Service
• Destination_Type_of_Service
• Duration
• Total_Packets
• Total_Bytes
• Label (indicating whether the traffic is from a botnet or not)

Exploratory Data Analysis (EDA)

EDA is performed to understand the data distribution, detect anomalies, and identify patterns. Key steps include:

• Visualizing the distribution of features.
• Correlation analysis to identify relationships between features.
• Plotting distributions of features for suspicious and non-suspicious traffic.

Feature Engineering and Selection

Feature Engineering

New features are created to enhance model performance. For instance:

• Transforming categorical features into numerical format.
• Generating interaction features based on domain knowledge.

Feature Selection

Relevant features are selected using techniques like:

• Correlation analysis.
• Chi-squared test.
• Variance thresholding to remove low variance features.

Model Building

Several machine learning models are built and trained on the dataset:

• Logistic Regression
• Random Forest
• Support Vector Machine (SVM)
• Decision Tree
• K-Nearest Neighbors (KNN)

The models are trained on the training set and evaluated on the test set using metrics such as accuracy, precision, recall, and F1-score.

Hyperparameter Tuning

Hyperparameter tuning is performed using GridSearchCV to optimize model performance. The following parameters are tuned:

• Logistic Regression: C, penalty
• Random Forest: n_estimators, max_depth
• SVM: C, kernel
• Decision Tree: max_depth
• KNN: n_neighbors, weights

Clustering

Clustering techniques like k-means or DBSCAN are applied to identify anomalous traffic patterns in an unsupervised manner. This helps in detecting botnets without labeled data.

Results and Evaluation

The performance of each model is evaluated based on:

• Accuracy: Proportion of correctly classified instances.
• Precision: Proportion of true positive instances among the predicted positives.
• Recall: Proportion of true positive instances among the actual positives.
• F1-Score: Harmonic mean of precision and recall.
• ROC-AUC: Area under the ROC curve.

Conclusion

The project successfully demonstrates the use of machine-learning techniques for botnet detection. By combining feature engineering, model building, and hyperparameter tuning, effective detection systems can be developed.

Usage

To use the botnet detection system:

1. Clone this repository.
2. Install the required libraries:

   pip install -r requirements.txt

3. Run the Jupyter notebook botnet_detection.ipynb to see the complete workflow.

Feel free to contribute by submitting issues or pull requests.

---

You can customize this README file further based on the specific details and results of your project.