Enmob: Unveil the Behavior with Multi-flow Analysis of Encrypted App Traffic

Abstract In the contemporary digital landscape, mobile applications have become the predominant conduit for internet connectivity and daily tasks. Simultaneously, the advent of application encryption technology has safeguarded users’ privacy. However, this encryption, while fortifying privacy, intro...

Full description

Bibliographic Details
Published in:	Cybersecurity
Main Authors:	Ge Mengmeng, Feng Ruitao, Liu Likun, Yu Xiangzhan, Sachidananda Vinay, Xie Xiaofei, Liu Yang
Format:	Article
Language:	English
Published:	SpringerOpen 2025-04-01
Subjects:	Traffic analysis Encryption traffic Behavior Traffic Classification
Online Access:	https://doi.org/10.1186/s42400-024-00301-0

Similar Items

Robot Communication: Network Traffic Classification Based on Deep Neural Network
by: Mengmeng Ge, et al.
Published: (2021-03-01)

FedETC: Encrypted traffic classification based on federated learning
by: Zhiping Jin, et al.
Published: (2024-08-01)

Survey of research on encrypted traffic classification based on machine learning
by: FU Yu, et al.
Published: (2025-01-01)

Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning
by: Ibrahim A. Alwhbi, et al.
Published: (2024-05-01)

TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
by: Ziao Liu, et al.
Published: (2025-03-01)

Enhancing encrypted HTTPS traffic classification based on stacked deep ensembles models
by: Ahmed M. Elshewey, et al.
Published: (2025-10-01)

CNN for User Activity Detection Using Encrypted In-App Mobile Data
by: Madushi H. Pathmaperuma, et al.
Published: (2022-02-01)

Multi-Level Pre-Training for Encrypted Network Traffic Classification
by: Jee-Tae Park, et al.
Published: (2025-01-01)

Hybrid feature learning framework for the classification of encrypted network traffic
by: S. Ramraj, et al.
Published: (2023-12-01)

Review and perspective on encrypted traffic identification research
by: Wu-bin PAN, et al.
Published: (2016-09-01)

Review and perspective on encrypted traffic identification research
by: Wu-bin PAN, et al.
Published: (2016-09-01)

Encrypted traffic identification scheme based on sliding window and randomness features
by: LIU Jiachi, et al.
Published: (2024-08-01)

Encrypted traffic identification scheme based on sliding window and randomness features
by: Jiachi LIU, Boyu KUANG, Mang SU, Yaqian XU, Anmin FU
Published: (2024-08-01)

CBS: A Deep Learning Approach for Encrypted Traffic Classification With Mixed Spatio-Temporal and Statistical Features
by: Mehdi Seydali, et al.
Published: (2023-01-01)

A Survey of Network Based Traffic Classification Methods
by: Pooja MEHTA, et al.
Published: (2017-03-01)

Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
by: Jee-Tae Park, et al.
Published: (2024-04-01)

Dual-Stream Time-Series Transformer-Based Encrypted Traffic Data Augmentation Framework
by: Daeho Choi, et al.
Published: (2025-09-01)

The application and research of machine learning in malicious encrypted traffic detection
by: Tian Rui, et al.
Published: (2025-04-01)

Survey of encrypted malicious traffic detection based on deep learning
by: Mingfang ZHAI, et al.
Published: (2020-06-01)

Malicious Encrypted Traffic Detection Method Based on Conversation Statistical Encoder Model
by: GONG Siyue, LIU Hui, WANG Baohui
Published: (2024-11-01)

User Behavior Detection Using Multi-Modal Signatures of Encrypted Network Traffic
by: Jee-Tae Park, et al.
Published: (2023-01-01)

SwiftSession: A Novel Incremental and Adaptive Approach to Rapid Traffic Classification by Leveraging Local Features
by: Tieqi Xi, et al.
Published: (2025-03-01)

Encrypted Traffic Classification Method Based on Multi-Layer Bidirectional SRU and Attention Model
by: ZHANG Surong, BU Youjun, CHEN Bo, SUN Chongxin, WANG Han, HU Xianjun
Published: (2022-11-01)

Encrypted traffic classification method based on parallel traffic graph and graph neural network
by: LIU Taotao, et al.
Published: (2025-06-01)

Identification of Mobile Service Type of Encrypted Traffic Based on Fusion of Inception andSE-Attention
by: WANG Yijing, WANG Qingxian, DING Dazhao, YAN Tingju, CAO Yan
Published: (2024-10-01)

A Multi-Feature Semantic Fusion Machine Learning Architecture for Detecting Encrypted Malicious Traffic
by: Shiyu Tang, et al.
Published: (2025-07-01)

Identification of Malicious Encrypted Traffic Through Feature Fusion
by: Xianchun Zheng, et al.
Published: (2023-01-01)

Encrypted Traffic Classification of CNN and BiGRU Based on Self-attention
by: CHEN Siyu, MA Hailong, ZHANG Jianhui
Published: (2024-08-01)

Granular classifier: Building traffic granules for encrypted traffic classification based on granular computing
by: Xuyang Jing, et al.
Published: (2024-10-01)

Robust Classification of Encrypted Network Services Using Convolutional Neural Networks Optimized by Information Bottleneck Method
by: Zhijiong Wang, et al.
Published: (2025-01-01)

Acceleration of Intrusion Detection in Encrypted Network Traffic Using Heterogeneous Hardware
by: Eva Papadogiannaki, et al.
Published: (2021-02-01)

ET-Mamba: A Mamba Model for Encrypted Traffic Classification
by: Jian Xu, et al.
Published: (2025-04-01)

CoTNeT: Contextual transformer network for encrypted traffic classification
by: Hong Huang, et al.
Published: (2024-06-01)

Bootstrap Forest based method for Encrypted Network Traffic Analysis
by: Shobana Durairaju, et al.
Published: (2025-01-01)

ITC-Net-blend-60: a comprehensive dataset for robust network traffic classification in diverse environments
by: Marziyeh Bayat, et al.
Published: (2024-06-01)

Practical evaluation of encrypted traffic classification based on a combined method of entropy estimation and neural networks
by: Kun Zhou, et al.
Published: (2020-01-01)

CLA-BERT: A Hybrid Model for Accurate Encrypted Traffic Classification by Combining Packet and Byte-Level Features
by: Hong Huang, et al.
Published: (2025-03-01)

Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review
by: Il Hwan Ji, et al.
Published: (2024-01-01)

Traffic Sign Detection and Classification on the Austrian Highway Traffic Sign Data Set
by: Alexander Maletzky, et al.
Published: (2023-01-01)

A survey on traffic flow prediction and classification
by: Bernardo Gomes, et al.
Published: (2023-11-01)