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Deep Learning-Driven Intelligent Analysis and Detection of Encrypted Malicious Traffic: A Survey

https://doi.org/10.2139/ssrn.6878397
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/nem.1901openalex-snapshot
A survey of methods for encrypted traffic classification and analysis
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Deep packet: a novel approach for encrypted traffic classification using deep learning
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An approach for feature selection using local searching and global optimization techniques
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Hybrid semantic deep learning architecture and optimal advanced encryption standard key management scheme for secure cloud storage and intrusion detection
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Deep learning: systematic review, models, challenges, and research directions
inferredcites →https://doi.org/10.1007/s10462-020-09825-6openalex-snapshot
A survey of the recent architectures of deep convolutional neural networks
inferredcites →https://doi.org/10.1007/s10462-023-10466-8openalex-snapshot
Deep learning modelling techniques: current progress, applications, advantages, and challenges
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Federated RNN for Intrusion Detection System in IoT Environment Under Adversarial Attack
inferredcites →https://doi.org/10.1007/s11227-020-03372-1openalex-snapshot
THS-IDPC: A three-stage hierarchical sampling method based on improved density peaks clustering algorithm for encrypted malicious traffic detection
inferredcites →https://doi.org/10.1007/s44163-025-00578-1openalex-snapshot
A comprehensive survey on intrusion detection systems with advances in machine learning, deep learning and emerging cybersecurity challenges
inferredcites →https://doi.org/10.1016/j.comnet.2023.109982openalex-snapshot
Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things
inferredcites →https://doi.org/10.1016/j.comnet.2023.110010openalex-snapshot
F-NIDS — A Network Intrusion Detection System based on federated learning
inferredcites →https://doi.org/10.1016/j.comnet.2024.110255openalex-snapshot
Artificial intelligence empowered physical layer security for 6G: State-of-the-art, challenges, and opportunities
inferredcites →https://doi.org/10.1016/j.cose.2024.104146openalex-snapshot
A hybrid CNN-LSTM approach for intelligent cyber intrusion detection system
inferredcites →https://doi.org/10.1016/j.eswa.2019.01.064openalex-snapshot
Feature analysis of encrypted malicious traffic
inferredcites →https://doi.org/10.1038/s41598-025-07706-yopenalex-snapshot
Deep learning for network security: an Attention-CNN-LSTM model for accurate intrusion detection
inferredcites →https://doi.org/10.1038/s41598-025-19450-4openalex-snapshot
End-to-end deep learning for smart maritime threat detection: an AE–CNN–LSTM-based approach
inferredcites →https://doi.org/10.1038/s41598-026-36135-8openalex-snapshot
Harnessing multimodal deep representation with dimensionality reducing approach for enhanced intrusion detection system in internet of things networks
inferredcites →https://doi.org/10.1109/access.2019.2908225openalex-snapshot
$Deep-Full-Range$ : A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework
inferredcites →https://doi.org/10.1109/access.2020.3022862openalex-snapshot
TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems
inferredcites →https://doi.org/10.1109/access.2020.3034015openalex-snapshot
A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network
inferredcites →https://doi.org/10.1109/access.2020.3035967openalex-snapshot
A Deep Hierarchical Network for Packet-Level Malicious Traffic Detection
inferredcites →https://doi.org/10.1109/access.2020.3048348openalex-snapshot
Explaining Deep Learning-Based Traffic Classification Using a Genetic Algorithm
inferredcites →https://doi.org/10.1109/access.2021.3064561openalex-snapshot
Deep Learning Approach for Detecting Malicious Activities Over Encrypted Secure Channels
inferredcites →https://doi.org/10.1109/access.2021.3139835openalex-snapshot
Intrusion Detection Using Payload Embeddings
inferredcites →https://doi.org/10.1109/comst.2022.3208196openalex-snapshot
Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/msp.2009.12openalex-snapshot
Man-in-the-Middle Attack to the HTTPS Protocol
inferredcites →https://doi.org/10.1109/tbdata.2024.3403394openalex-snapshot
Towards Real-Time Network Intrusion Detection With Image-Based Sequential Packets Representation
inferredcites →https://doi.org/10.1109/tnnls.2018.2876865openalex-snapshot
Object Detection With Deep Learning: A Review
inferredcites →https://doi.org/10.1109/tnsm.2020.3016246openalex-snapshot
Survey of Network Intrusion Detection Methods From the Perspective of the Knowledge Discovery in Databases Process
inferredcites →https://doi.org/10.1109/tsg.2022.3201163openalex-snapshot
Attack Intensity Dependent Adaptive Load Frequency Control of Interconnected Power Systems Under Malicious Traffic Attacks
inferredcites →https://doi.org/10.1145/3594869openalex-snapshot
Interpreting Adversarial Examples in Deep Learning: A Review
inferredcites →https://doi.org/10.1145/3657282openalex-snapshot
Lightweight Deep Learning for Resource-Constrained Environments: A Survey
inferredcites →https://doi.org/10.1145/3687124openalex-snapshot
Survey on Federated Learning for Intrusion Detection System: Concept, Architectures, Aggregation Strategies, Challenges, and Future Directions
inferredcites →https://doi.org/10.1145/3689036openalex-snapshot
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
inferredcites →https://doi.org/10.3389/frai.2025.1593944openalex-snapshot
PACKETCLIP: multi-modal embedding of network traffic and language for cybersecurity reasoning
inferredcites →https://doi.org/10.3390/computers12100209openalex-snapshot
On the Robustness of ML-Based Network Intrusion Detection Systems: An Adversarial and Distribution Shift Perspective
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