Deep Learning-Based Framework for Zero-Day Attack Detection in Encrypted Network Traffic Using Python

Abstract

The increasing adoption of encryption in modern communication networks has reduced the effectiveness of traditional intrusion detection systems that rely on packet payload inspection. Consequently, there is a growing demand for payload-agnostic intrusion detection approaches capable of analyzing network behavior without accessing packet contents. This study proposes a deep learning-based intrusion detection framework that utilizes flow-level statistical features and a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture to capture both spatial and temporal characteristics of network traffic. The framework was evaluated using the UNSW-NB15 and CIC-IDS2018 benchmark datasets under both in-dataset and cross-dataset evaluation settings. Experimental results demonstrated excellent classification performance in in-dataset experiments, achieving high accuracy and area under the curve (AUC) values. A threshold calibration strategy was further employed to reduce false-positive rates and improve operational reliability. However, cross-dataset evaluation revealed a considerable decline in detection performance, highlighting the impact of domain shift on model generalization across heterogeneous network environments. These findings demonstrate the effectiveness of flow-level statistical features for intrusion detection while emphasizing the importance of developing more robust learning strategies to improve generalization beyond single-dataset training.