A Single Network Architecture for Attack Detection and Multi-Class Classification

The increasing sophistication of cyberattacks in modern networks necessitates robust and adaptable detection mechanisms. This project proposes a unified network architecture for attack detection and multi-class classification, employing a diverse set of machine learning algorithms. The architecture integrates models such as XGBoost, Random Forest, Gradient Boosting, Dense Neural Network, and a CNN-LSTM hybrid approach to detect and classify network intrusions. The target classes include 'DoS', 'Exploits', 'Fuzzers', 'Generic', and 'Normal', covering a broad range of attack categories. The system aims to provide accurate, real-time detection of various types of attacks, utilizing a combination of traditional and deep learning-based algorithms to optimize performance. The proposed multi-model framework capitalizes on the strengths of each algorithm, ensuring high classification accuracy and resilience against a wide array of attack types. The system was implemented in Python using popular machine learning libraries such as scikit-learn for traditional models and Keras for deep learning. By leveraging a comprehensive set of features and multi-class classification, the architecture demonstrates its capability to effectively handle network security threats, offering a scalable solution for attack detection across diverse network environments.

Keywords: Network Security, Attack Detection, Multi-Class Classification, XGBoost, Random Forest, Gradient Boosting, Dense Neural Network, CNN-LSTM Hybrid, Intrusion Detection, Machine Learning, Cybersecurity, Real-Time Detection, Python.