Скачать или смотреть How to Implement an Intrusion Detection System Using Deep Learning and Python

This project implements an Intrusion Detection System (IDS) using deep learning and Python to classify network traffic. The IDS is a crucial part of cybersecurity strategy, offering early threat detection and minimizing business disruption. It supports compliance readiness, post-incident forensics, and adaptive behavioral analysis through anomaly detection. We use an LSTM neural network to learn temporal patterns in network flow features, enabling high accuracy in classifying traffic as Non-Tor, NonVPN, Tor, or VPN. The dataset, CICDarknet2020, includes over 85 features extracted from NetFlow logs, such as packet lengths, durations, and protocol flags. The model is trained using labeled data and achieves 94% accuracy, with strong precision and recall across all classes. A confusion matrix and classification report help assess the performance of the model in multi-class detection. Once trained, the system is used to dynamically classify new traffic and flag threats like Tor and VPN connections in real-time. The output can be used to trigger alerts, update logs, or feed into larger security operations systems. Overall, this IDS demonstrates how AI can enhance corporate network security through intelligent traffic monitoring and threat classification.