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Deep Learning for Encrypted Traffic Classification and Unknown Data Detection
2022
Sensors
Preserved Fulltext
untrained in-app activity traffic as unknown data when this framework is employed. ...
) from a sniffed encrypted Internet traffic stream. ...
type and ISCX 93.75% Sensors 2022, 22, x FOR PEER REVIEW 5 of 18
Table 1 . 1 Positioning of the proposed wor
Research Article Encrypted Traffic Unknown Data Detection Classification Type
Table 1 ...
doi:10.3390/s22197643
pmid:36236739
pmcid:PMC9570541
fatcat:6kkbrzqu4neyvjfbul6o5tgsle
Deep Learning for Encrypted Traffic Classification and Unknown Data Detection
[article]
2022
arXiv
pre-print
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untrained in-app activity traffic as unknown data when this framework is employed. ...
from a sniffed encrypted Internet traffic stream. ...
unknown data detection rate. ...
arXiv:2203.15501v1
fatcat:wymatrawjfdqhhpvc6u5sh6jnm
Deep Learning Approaches for Network Traffic Classification in the Internet of Things (IoT): A Survey
[article]
2024
arXiv
pre-print
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Deep learning has emerged as a powerful technique for network traffic classification due to its ability to automatically learn complex patterns and representations from raw data. ...
of deep learning-based network traffic classification in IoT. ...
GLADS is a lightweight, multitask, and deep learning-based encrypted traffic classification model designed for resource-constrained IoT environments [11] . ...
arXiv:2402.00920v1
fatcat:kxeyztmqrbgidafh42hyfjfw24
A few approaches in Encrypted Malware Classifications
2022
Zenodo
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Nowadays, academics and industry developers are turning to learning-based systems for encrypted malware traffic categorization, and mining statistical patterns of traffic behaviors. ...
Several approaches for traffic classification problems have recently been researched with excellent accuracy thanks to the advent of deep learning algorithms. ...
Approval: All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. ...
doi:10.5281/zenodo.7456878
fatcat:jxrdga3advfdfnj4wgq2wznqsm
A Review of Intrusion Detection Technology Based on Deep Rein-forcement Learning
2021
Electronics Science Technology and Application
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On this basis, we further evaluate the performance of three common deep learning models in intrusion detection, and conclude that DBN algorithm has some strong advantages. ...
Through extensive investigation, this paper presents the latest work of network intrusion detection technology based on deep learning. ...
In addition, intrusion detection based on deep learning should provide support for encryption attack and 0day attack detection. ...
doi:10.18686/esta.v7i4.164
fatcat:qwmndfprk5d5rf6j75mvzfwjve
Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study
2021
Computers & security
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2022 pre-print arXiv:2203.09332v1 fatcat:b64evyfc6jgflnqqmascuo3ete
Thus, machine learning based approaches have become an important direction for encrypted malicious traffic detection. ...
As people's demand for personal privacy and data security becomes a priority, encrypted traffic has become mainstream in the cyber world. ...
[98] for encrypted and mobile traffic classification. The proposed framework provides clear guidelines for designers in deep learning based traffic classification. ...
doi:10.1016/j.cose.2021.102542
fatcat:7fk5kbpnk5f7rp5z55murufxhi
NETWORK CLASSIFICATION AND IDENTIFICATION BASED ON MACHINE LEARNING
2020
International Journal of Engineering Applied Sciences and Technology
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Machine learning method is used to the identify network traffic with encryption and unknown features, which makes up for the disadvantage of deep packet inspection that cannot identify new applications ...
The traffic identification is an important basis for traffic monitoring and data analysis. ...
The machine learning method based on the statistical characteristics of flow is used to assist the identification of network flows with encryption and unknown features, which makes up for the shortcomings ...
doi:10.33564/ijeast.2020.v05i03.107
fatcat:u2u2hdd23ncl3jktjv7d6mw3ta
CNN for User Activity Detection Using Encrypted In-App Mobile Data
2022
Future Internet
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The proposed method extracts and selects salient features for encrypted traffic classification. This is the first-known approach proposing to filter unknown traffic with an average accuracy of 88%. ...
In this study, matrices were constructed for each encrypted traffic flow segment. ...
Traffic flow data are converted into images and image classification deep learning techniques are used to detect in-app activities. ...
doi:10.3390/fi14020067
fatcat:moosqemiinctxi5ptynkgynrla
OpenCBD: A Network-Encrypted Unknown Traffic Identification Scheme Based on Open-Set Recognition
2022
Wireless Communications and Mobile Computing
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The encryption of network traffic promotes the development of encrypted traffic classification and identification research. ...
However, many existing studies are only effective for closed-set experimental data, that is to say, only for traffic of known classes, while there are often lots of unknown classes traffic in the real ...
It uses the idea of open-set recognition, combines deep learning and ensemble learning, learns the basic characteristics of encrypted traffic from unlabeled data, and then trains on known classes of traffic ...
doi:10.1155/2022/1746373
fatcat:d2f2frvwhvgzzjs6ofdmzpogzi
A new dynamic security defense system based on TCP_REPAIR and deep learning
2023
Journal of Cloud Computing: Advances, Systems and Applications
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The system accurately distributes encrypted or non-encrypted attack traffic and its variants through the intelligent firewall. ...
slow TCP connection switching speed and inability to efficiently identify encrypted malicious traffic. ...
Traffic classification by deep learning is generally divided into two steps: first input of raw data and then deep learning algorithms such as CNN and RNN are used to learn the original data features and ...
doi:10.1186/s13677-022-00379-2
fatcat:fmbhebcj3zclvjpiat44t7hl7q
VPN and Non-VPN Network Traffic Classification Using Time-Related Features
2022
Computers Materials & Continua
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Therefore, this paper suggests two machine learning models that categorize network traffic into encrypted and non-encrypted traffic. ...
This affects and complicates the quality of service (QoS), traffic monitoring, and network security provided by Internet Service Providers (ISPs), particularly for analysis and anomaly detection approaches ...
We wish to express our gratitude to all members of our colleges, Princess Sumaya University for Technology (PSUT), Jouf University, and Yarmouk University, for their support. ...
doi:10.32604/cmc.2022.025103
fatcat:icvxnanburfnpf4nxskq7cpyqa
Edge Intelligence Based Identification and Classification of Encrypted Traffic of Internet of Things
2021
IEEE Access
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ACKNOWLEDGMENT The authors would like to thanks Kaijun Wu, Yao Hao, and Hong Su for their kind help and valuable suggestions. ...
They would also like to thanks the anonymous reviewers for their insightful comments which have significantly improved the quality of the paper. ...
It is efficient in encrypted traffic detection and identification in binary classification scenarios. ...
doi:10.1109/access.2021.3056216
fatcat:5lxrofspa5aqpdrmqjwsfrvjtm
Facing Unknown: Open-World Encrypted Traffic Classification Based on Contrastive Pre-Training
[article]
2023
arXiv
pre-print
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the known applications and detecting unknown applications. ...
Traditional Encrypted Traffic Classification (ETC) methods face a significant challenge in classifying large volumes of encrypted traffic in the open-world assumption, i.e., simultaneously classifying ...
[12] built a deep learning framework to select and combine the statistical features to enhance the performance of traffic classification. Chen et al. ...
arXiv:2308.16861v1
fatcat:2erbbsl4kvh6dd2odnthzcsyt4
Deep Learning-Based Encrypted Network Traffic Classification and Resource Allocation in SDN
2021
Journal of Web Engineering
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The extensive application of deep learning provides a new idea for the study of traffic classification. ...
of encrypted networks based on deep learning. ...
Excellent achievements have been made in protocol classification, anomaly detection and unknown recognition. ...
doi:10.13052/jwe1540-9589.2085
fatcat:ttmm4adxezezzh5ifz4x6nfvwa
BSTFNet: An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features
2024
Computers Materials & Continua
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To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic ...
The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic. ...
Acknowledgement: The authors would like to thank the reviewers for their contribution to this paper. ...
doi:10.32604/cmc.2024.047918
fatcat:bmux6w7vnzecrn3r4xoucudz7e
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