"CyberSecNet" Cyber Attack Classification and Clustering using different Machine learning Algorithms

CyberSecNet is a project aimed at analyzing and classifying cyber attacks using machine learning techniques. The project focuses on the comprehensive analysis of network traffic data to identify various types of cyber attacks and cluster them based on their characteristics.

Features and Functionality Data Exploration: The project provides functionality to explore the dataset and understand the characteristics and distributions of different features.

Data Preprocessing: It performs data preprocessing tasks such as cleaning the data, handling missing values, outliers, and feature scaling to prepare the data for classification.

Feature Selection: The project employs correlation analysis to identify the most relevant features for classification, improving the accuracy and efficiency of the models.

Classification Algorithms: Three classification algorithms, namely Decision Tree, K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN), are implemented to classify cyber attacks based on the selected features.

Clustering: The project also includes clustering using the k-Means algorithm to group similar attacks together. The results are visualized using scatter plots to provide insights into the patterns and relationships within the data.

CYBERSECNET

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