Given the growing complexity of cyber threats, Intrusion Detection Systems (IDS) must advance to detect both established and novel attack vectors in real time. This study investigates the application of Artificial Intelligence (AI) methodologies in the context of intrusion detection, with a particular emphasis on two pivotal domains: payload analysis and the clustering of anomalous, previously unclassified network traffic. For payload classification, a novel deep learning framework is proposed, wherein raw hexadecimal payload data are converted into spectrogram representations. This transformation facilitates the deployment of a hybrid architecture combining Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks, thereby enabling the extraction of both spatial and temporal features.