Fast and lean encrypted Internet traffic classification

Roy, Sangita; Shapira, Tal; Shavitt, Yuval

doi:10.1016/j.comcom.2022.02.003

Cited by 25 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several works converted the network flow into an image to harness image processing techniques and equivalent Deep-Learning (DL) architectures [2], [11], [17], [18], [20], [21]. Newer works incorporated the Ordinary Differential Equation Network (ODENet) within a DL architecture to classify uni-directional network flows [22]. If we shift the focus to the cyber domain, many works tackle the task of malware network traffic detection and classification.…”

Section: Related Workmentioning

confidence: 99%

“…Along with detection or classification methods, the data in use have great importance as well. Some publicly available datasets such as ISCXVPN2016 are widely used in the literature in works such as [11]- [13], [16], [22], [23], [31]. Where the works adopt the dataset for the purpose of classifying a network flow according to its encapsulation type (tunneled via VPN or not), traffic type (e.g., video/audio/chat), and application (e.g., Skype/Netflix).…”

Section: Related Workmentioning

confidence: 99%

“…• Date: Datasets are quite old, for instance, the IS-CXVPN2016, USTC-TFC2016 and Ariel (BOA2016) datasets are from 2016. Still, these datsets are the deafult datasets used in most state-of-the-art works [11]- [13], [16], [22], [23], [31] 1 . Applications change over time, along with the increasing trend of adopting encryption with almost every network traffic flow.…”

Section: Datasets Disadvantagesmentioning

confidence: 99%

“…and an encapsulation label (non-VPN/VPN). Some works that adopted this dataset can be found in [11]- [13], [16], [22], [23], [32].…”

mentioning

confidence: 99%

See 3 more Smart Citations

Open-Source Framework for Encrypted Internet and Malicious Traffic Classification

Bader¹,

Lichy²,

Dvir³

et al. 2022

Preprint

View full text Add to dashboard Cite

Internet traffic classification plays a key role in network visibility, Quality of Services (QoS), intrusion detection, Quality of Experience (QoE) and traffic-trend analyses. In order to improve privacy, integrity, confidentiality, and protocol obfuscation, the current traffic is based on encryption protocols, e.g., SSL/TLS. With the increased use of Machine-Learning (ML) and Deep-Learning (DL) models in the literature, comparison between different models and methods has become cumbersome and difficult due to a lack of a standardized framework. In this paper, we propose an open-source framework, named OSF-EIMTC, which can provide the full pipeline of the learning process. From the well-known datasets to extracting new and well-known features, it provides implementations of well-known ML and DL models (from the traffic classification literature) as well as evaluations. Such a framework can facilitate research in traffic classification domains, so that it will be more repeatable, reproducible, easier to execute, and will allow a more accurate comparison of well-known and novel features and models. As part of our framework evaluation, we demonstrate a variety of cases where the framework can be of use, utilizing multiple datasets, models, and feature sets. We show analyses of publicly available datasets and invite the community to participate in our open challenges using the OSF-EIMTC.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Datasets Disadvantagesmentioning

confidence: 99%

“…and an encapsulation label (non-VPN/VPN). Some works that adopted this dataset can be found in [11]- [13], [16], [22], [23], [32].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Open-Source Framework for Encrypted Internet and Malicious Traffic Classification

Bader¹,

Lichy²,

Dvir³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, the lightweight deep learning model has gradually become the research focus. Roy et al [24] improved on CNN-LSTM and proposed a traffic classification method based on OdeNet-LSTM, which achieved faster reasoning speed. Fauvel et al [25] introduced a new residual structure design into the CNN-based traffic classification method and proposed LexNet, significantly reducing the parameter.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Traffic Classification Model Based on Deep Learning

Sun

Chen

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The development of mobile computing and the Internet of Things (IoT) has led to a surge in traffic volume, which creates a heavy burden for efficient network management. The network management requires high computational overheads to make traffic classification, which is even worse when in edge networks; existing approaches sacrifice the efficiency to obtain high-precision classification results, which are no longer suitable for limited resources edge network scenario. Given the problem, existing traffic classification generally has huge parameters and especially computational complexity. We propose a lightweight traffic classification model based on the Mobilenetv3 and improve it for an ingenious balance between performance and lightweight. Firstly, we adjust the model scale, width, and resolution to substantially reduce the number of model parameters and computations. Secondly, we embed precise spatial information on the attention mechanism to enhance the traffic flow-level feature extraction capability. Thirdly, we use the lightweight multiscale feature fusion to obtain the multiscale flow-level features of traffic. Experiments show that our model has excellent classification accuracy and operational efficiency. The accuracy of the traffic classification model designed in our work has reached more than 99.82%, and the parameter and computation amount are significantly reduced to 0.26 M and 5.26 M. In addition, the simulation experiments on Raspberry Pi prove the proposed model can realize real-time classification capability in the edge network.

show abstract