The aim of this project is to apply different classification and clustering algorithms for the classification of cyber-attacks in network traffic. The project requires us to perform data preprocessing, feature engineering, and implement classification and clustering algorithms. The dataset provided contains 23 different classes (attack types) that need to be converted into 5 classes. The most relevant features for classification have to be identified using correlation analysis. The classification algorithms to be used are Decision Tree Algorithm, K-Nearest Neighbors Algorithm, and Artificial Neural Networks (ANN). The performance of the algorithms will be evaluated using appropriate metrics such as accuracy, precision, recall, and F1 score. Lastly, the dataset will be labeled using clustering algorithms, and the results will be visualized using scatter plots.
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