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In the ever-evolving realm of network security, the swift and accurateidentification of diverse attack classes within network traffic is of paramountimportance. This paper introduces "ByteStack-ID," a pioneering approachtailored for packet-level intrusion detection. At its core, ByteStack-IDleverages grayscale images generated from the frequency distributions ofpayload data, a groundbreaking technique that greatly enhances the model'sability to discern intricate data patterns. Notably, our approach isexclusively grounded in packet-level information, a departure from conventionalNetwork Intrusion Detection Systems (NIDS) that predominantly rely onflow-based data. While building upon the fundamental concept of stackingmethodology, ByteStack-ID diverges from traditional stacking approaches. Itseamlessly integrates additional meta learner layers into the concatenated baselearners, creating a highly optimized, unified model. Empirical resultsunequivocally confirm the outstanding effectiveness of the ByteStack-IDframework, consistently outperforming baseline models and state-of-the-artapproaches across pivotal performance metrics, including precision, recall, andF1-score. Impressively, our proposed approach achieves an exceptional 81\%macro F1-score in multiclass classification tasks. In a landscape marked by thecontinuous evolution of network threats, ByteStack-ID emerges as a robust andversatile security solution, relying solely on packet-level informationextracted from network traffic data.

ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection