专题：Internet Traffic Analysis and Secure E-voting

This cluster of papers focuses on the application of machine learning and deep learning techniques for the classification and analysis of internet traffic, including encrypted traffic, network behavior, and IoT device identification. It also covers topics related to anonymity, traffic analysis, and electronic voting.
最新文献
近5年高被引文献
ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification

conference-paper Full Text OpenAlex 589 FWCI52.7777

Federated Learning for intrusion detection system: Concepts, challenges and future directions

article Full Text OpenAlex 457 FWCI41.1835

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT

conference-paper Full Text OpenAlex 455 FWCI84.116

Anomaly-based intrusion detection system for IoT networks through deep learning model

article Full Text OpenAlex 449 FWCI42.6439

Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network

article Full Text OpenAlex 421 FWCI27.2316

CNN-LSTM: Hybrid Deep Neural Network for Network Intrusion Detection System

article Full Text OpenAlex 420 FWCI34.979

A hybrid CNN+LSTM-based intrusion detection system for industrial IoT networks

article Full Text OpenAlex 338 FWCI37.5341

RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System

article Full Text OpenAlex 327 FWCI26.4217

Survey on Fully Homomorphic Encryption, Theory, and Applications

article Full Text OpenAlex 322 FWCI17.5949

Revolutionizing Cyber Threat Detection With Large Language Models: A Privacy-Preserving BERT-Based Lightweight Model for IoT/IIoT Devices

article Full Text OpenAlex 318 FWCI52.3866