Cyber Attacks Classification using Machine Learning

• Tech Stack: Python (version 3.7+), Pandas, NumPy, Scikit-learn, SVM, KNN, DT, XGBoost, Random Forest, Logistic Regression
• Github URL: Project Link

In the rapidly evolving landscape of cybersecurity, the ability to quickly and accurately classify cyber attacks is crucial. This project leverages machine learning algorithms to analyze and classify patterns in network traffic, aiding in the identification and mitigation of potential threats. The project incorporates various machine learning models, including Decision Trees, Random Forests, Support Vector Machines (SVM), and Neural Networks, to enhance the accuracy of cyber attack classification. By performing feature engineering, the system extracts relevant features from network data, such as packet size, frequency, and anomalies, to improve classification precision. Additionally, the models are designed for real-time analysis, enabling immediate detection and response to potential cyber threats.