A Feasible and Explainable Network Traffic Classifier Utilizing DistilBERT

Shin, Chang-Yui; Park, Jee-Tae; Baek, Ui-Jun; Kim, Myung-Sup

doi:10.1109/access.2023.3293105

Cited by 3 publications

(11 citation statements)

References 50 publications

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“…In recent years, there has been a surge in research centered on transformer architectures characterized by self-attention and multi-headed attention mechanisms. Transformerstructured models mainly utilize the BERT model, which has proven to show strong performance in the NLP field, but recently, research has also been conducted using the masked autoencoder (MAE), which is used in the CV field [33][34][35].…”

Section: Encrypted Traffic Classificationmentioning

confidence: 99%

“…In [23], similar to [22], a pre-trained BERT model and a bidirectional LSTM are applied together, with an accuracy of about 99%. In [33], the authors utilize DistilBERT to perform encrypted traffic classification research. They introduce comparative learning to enhance classification speed without degrading performance.…”

Section: Encrypted Traffic Classificationmentioning

confidence: 99%

“…They introduce comparative learning to enhance classification speed without degrading performance. Although our study is similar to [33], which focuses on STL, our study specifically targets MTL. We apply MTL to simultaneously learn three tasks on a single model, resulting in superior performance.…”

Section: Encrypted Traffic Classificationmentioning

confidence: 99%

“…Based on other research [33,34] and experiments under various configurations, we chose 63 as the optimal byte value. The 63 bytes are composed of (1) IP, (2) TCP or UDP, and (3) Payload, depending on the network layer and data.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Sci. 2024, 14 Transformer-based methods are one of the more recent deep learning techniques to emerge, applying structures that have performed particularly well in natural language processing (NLP) for traffic classification [33][34][35][36]. The self-attention mechanism of the transformer effectively learns the global dependencies of sequence data, which has shown promising performance in a variety of applications.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification

Park,

Shin,

Baek

et al. 2024

Applied Sciences

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The classification of encrypted traffic plays a crucial role in network management and security. As encrypted network traffic becomes increasingly complicated and challenging to analyze, there is a growing need for more efficient and comprehensive analytical approaches. Our proposed method introduces a novel approach to network traffic classification, utilizing multi-task learning to simultaneously train multiple tasks within a single model. To validate the proposed method, we conducted experiments using the ISCX 2016 VPN/Non-VPN dataset, consisting of three tasks. The proposed method outperformed the majority of existing methods in classification with 99.29%, 97.38%, and 96.89% accuracy in three tasks (i.e., encapsulation, category, and application classification, respectively). The efficiency of the proposed method also demonstrated outstanding performance when compared to methods excluding lightweight models. The proposed approach demonstrates accurate and efficient multi-task classification on encrypted traffic.

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Section: Encrypted Traffic Classificationmentioning

confidence: 99%

Section: Encrypted Traffic Classificationmentioning

confidence: 99%

Section: Encrypted Traffic Classificationmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification

Park,

Shin,

Baek

et al. 2024

Applied Sciences

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CBS: A Deep Learning Approach for Encrypted Traffic Classification With Mixed Spatio-Temporal and Statistical Features

Seydali,

Khunjush,

Akbari

et al. 2023

IEEE Access

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With the rapid advancement of the internet and online applications, traffic classification has become an increasingly significant topic in computer networks. Managing network resources, improving service quality, and enhancing cybersecurity are critical. Due to traffic encryption techniques, traditional traffic classification approaches have become ineffective and inaccurate. Therefore, the scientific community considers deep learning a high-performance approach for classifying encrypted traffic. This paper proposes an encrypted traffic classification approach, CBS, based on a deep learning technique. CBS can classify encrypted traffic at two levels using 1D-CNN, attention-based Bi-LSTM, and SAE deep network models. The proposed model classifies traffic types and applications based on a comprehensive set of session and packet-level features. CBS accurately distinguishes traffic classes using spatial, temporal, and statistical features extracted from packet content relationships, temporal relationships between packets in a session, and statistical characteristics of a work session. A traffic data augmentation technique based on a GAN network is employed to mitigate the impact of data imbalance on traffic classes. The proposed platform's performance is evaluated on the public ISCX VPN-Non VPN 2016 dataset. The results demonstrate that the platform accurately and efficiently identifies applications and classifies encrypted traffic. Compared to state-of-the-art methods, the proposed traffic classification model improves precision by 21.3%, accuracy by 13.1%, recall by 18.11%, and F1 score by 19.79%.

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Recent Applications of Explainable AI (XAI): A Systematic Literature Review

Saarela,

Podgorelec

2024

Applied Sciences

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This systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of explainable AI (XAI) over the past three years. From an initial pool of 664 articles identified through the Web of Science database, 512 peer-reviewed journal articles met the inclusion criteria—namely, being recent, high-quality XAI application articles published in English—and were analyzed in detail. Both qualitative and quantitative statistical techniques were used to analyze the identified articles: qualitatively by summarizing the characteristics of the included studies based on predefined codes, and quantitatively through statistical analysis of the data. These articles were categorized according to their application domains, techniques, and evaluation methods. Health-related applications were particularly prevalent, with a strong focus on cancer diagnosis, COVID-19 management, and medical imaging. Other significant areas of application included environmental and agricultural management, industrial optimization, cybersecurity, finance, transportation, and entertainment. Additionally, emerging applications in law, education, and social care highlight XAI’s expanding impact. The review reveals a predominant use of local explanation methods, particularly SHAP and LIME, with SHAP being favored for its stability and mathematical guarantees. However, a critical gap in the evaluation of XAI results is identified, as most studies rely on anecdotal evidence or expert opinion rather than robust quantitative metrics. This underscores the urgent need for standardized evaluation frameworks to ensure the reliability and effectiveness of XAI applications. Future research should focus on developing comprehensive evaluation standards and improving the interpretability and stability of explanations. These advancements are essential for addressing the diverse demands of various application domains while ensuring trust and transparency in AI systems.

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A Feasible and Explainable Network Traffic Classifier Utilizing DistilBERT

Cited by 3 publications

References 50 publications

Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification

Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification

CBS: A Deep Learning Approach for Encrypted Traffic Classification With Mixed Spatio-Temporal and Statistical Features

Recent Applications of Explainable AI (XAI): A Systematic Literature Review

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