Deep Packet: Deep Learning Model for Intrusion Detection

Intrusion Detection Systems (IDS) are essential for maintaining network security, yet traditional rule-based systems struggle to keep pace with evolving cyber threats. This research introduces Deep Packet, a novel IDS model that leverages Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, combined with the Word2Vec embedding method for data representation. The proposed model aims to enhance detection accuracy and adaptability by capturing both spatial and temporal features in network traffic data.