A Method for Out-of-Distribution Detection in Encrypted Mobile Traffic Classification

The widespread use of encrypted communication in mobile networks poses significant challenges in accurately classifying traffic. Detecting out-of-distribution (OOD) samples, which significantly deviate from known classes, adds complexity to the task. This paper proposes a feature analysis-based OOD detection scheme for traffic classification in Long-Term Evolution (LTE) systems. Our method utilizes Long Short-Term Memory (LSTM) networks for feature extraction, capturing the feature vectors of the traffic series. Principal Component Analysis (PCA) is then applied to obtain principal and residual principal components. Leveraging the residual feature vector, we construct an OOD score to quantify deviation from the ID dataset. Extensive experiments on a large-scale encrypted mobile traffic dataset demonstrate the superiority of our approach, achieving high accuracy in OOD detection compared to existing techniques. Our method contributes to enhanced security and reliable traffic classification in LTE systems, addressing challenges posed by OOD samples.