Wyszukiwanie zaawansowane ?

Obiekt

Explainable Network Traffic Classification Using XGBoost and SHAP: Interpreting Feature Contributions Across Traffic Classes, Journal of Telecommunications and Information Technology, 2026, nr 3
Pobierz

Tytuł: Explainable Network Traffic Classification Using XGBoost and SHAP: Interpreting Feature Contributions Across Traffic Classes, Journal of Telecommunications and Information Technology, 2026, nr 3

Autor:

Deghem, Boubakar Seddik ; Aid, Lahcene ; Mostefaoui, Kadda

Data wydania:

2026, nr 3

Typ zasobu:

artykuł

Tytuł publikacji grupowej:

2026, nr 3, JTIT-artykuły

Opis:

kwartalnik

Abstrakt:

Network traffic classification has become an essential component of modern network management, cyber security, and quality-of-service provisioning. The widespread adoption of encryption technologies has reduced the effectiveness of traditional traffic identification techniques, leading to the use of ML approaches based on flow-level characteristics. Although several machine learning and deep learning models were evaluated, the black-box nature complicates the practical deployment in security critical environments. This research proposes a framework for ML-based network traffic classification of explainable artificial intelligence (XAI). The CIC-Darknet2020 dataset is divided into four classes: non-TOR, non-VPN, TOR and VPN. Additionally, machine learning algorithms, including ID3, k-nearest neighbors (KNN), random forest, CatBoost, XGBoost, and LSTM are used as a deep learning approach. The evaluation is carried out through stratified split of trains and 10-fold cross-validation, while XGBoost is classified for explainability analysis. By ensuring model transparency, SHAP (SHapley Additive exPlanations) identifies the most influential features contributing to classification predictions. Furthermore, a novel category SHAP analysis is introduced by grouping higher-level behavioral categories, including temporal, statistical, rate-based, and TCP-related features. The results revealed that temporal traffic characteristics and transport layer behavioral features influence classification outcomes, particularly for encrypted traffic classes. Moreover, the framework demonstrates that high classification performance and model interpretability are achievable simultaneously within a category-level study that enhances the transparency, trustworthiness, and practical applicability of machine learning-based network traffic classification systems.

Tom:

105

Numer:

3

Wydawca:

National Institute of Telecommunications

Identyfikator zasobu:

oai:bc.itl.waw.pl:2483

DOI:

10.26636/jtit.2026.3.2724

eISSN:

1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności

Licencja:

CC BY 4.0

Właściciel praw:

Biblioteka Naukowa Instytutu Łączności

Temat i słowa kluczowe:

deep learning explainable AI machine learning network traffic classification

Kolekcje, do których przypisany jest obiekt:

Data ostatniej modyfikacji:

5 paź 2026

Data dodania obiektu:

5 paź 2026

Liczba wyświetleń treści obiektu:

0

Wszystkie dostępne wersje tego obiektu:

https://bc.itl.waw.pl/publication/2806

Wyświetl opis w formacie RDF:

RDF

Wyświetl opis w formacie OAI-PMH:

OAI-PMH

Nazwa wydania Data
Deghem, Boubakar Seddik; Aid, Lahcene; Mostefaoui, Kadda, Explainable Network Traffic Classification Using XGBoost and SHAP: Interpreting Feature Contributions Across Traffic Classes, Journal of Telecommunications and Information Technology, 5 paź 2026

Obiekty Podobne
Network Traffic Classification in an NFV Environment using Supervised ML Algorithms, Journal of Telecommunications and Information Technology, 2021, nr 3

Latkoski, Pero Ilievski, Gjorgji

2021, nr 3
artykuł
2024, nr 3, JTIT-artykuły
2024, nr 3, JTIT-artykuły
A Generalized Learning Approach to Deep Neural Networks, Journal of Telecommunications and Information Technology, 2024, nr 3

Ponti, Francesca Frezza, Fabrizio Simeoni, Patrizio Parisi, Raffaele

2024, nr 3
artykuł
2025, nr 3, JTIT-artykuły
2025, nr 3, JTIT-artykuły
A Comprehensive Study on Path Loss Estimation Using Deep Hybrid Learning in 5G Networks, Journal of Telecommunications and Information Technology, 2025, nr 3

Yeaser, Kazi Md Abrar Hassan, Kazi Md Abir

2025, nr 3
artykuł
2024, nr 3, JTIT-artykuły
2024, nr 3, JTIT-artykuły
A Collaborative Approach to Detecting DDoS Attacks in SDN Using Entropy and Deep Learning, Journal of Telecommunications and Information Technology, 2024, nr 3

Narayan, D. G. Heena, W Amit, K

2024, nr 3
artykuł
Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-learned Features, Journal of Telecommunications and Information Technology, 2022, nr 4

Dzwonkowski, Mariusz Czaplewski, Bartosz Panas, Damian

2022, nr 4
artykuł
2026, nr 2, JTIT-artykuły
2026, nr 2, JTIT-artykuły
Evaluating AES Payload Encryption for Securing MQTT-based Smart Home Networks with Machine Learning-based Intrusion Detection, Journal of Telecommunications and Information Technology, 2026, nr 2

Gajewski, Mariusz Sałabun, Wojciech

2026, nr 2
artykuł
2025, nr 4, JTIT-artykuły
2025, nr 4, JTIT-artykuły
AI-based Violent Incident Detection in Surveillance Videos to Enhance Public Safety, Journal of Telecommunications and Information Technology, 2025, nr 4

Merit, Khaled Beladgham, Mohammed

2025, nr 4
artykuł
2024, nr 4, JTIT-artykuły
2024, nr 4, JTIT-artykuły
Enhancing Biometric Security with Bimodal Deep Learning and Feature-Level Fusion of Facial and Voice Data, Journal of Telecommunications and Information Technology, 2024, nr 4

Merit, Khaled Beladgham, Mohammed

2024, nr 4
artykuł
Więcej
×

Cytowanie

Styl cytowania:

Dane kontaktowe

Adres

Instytut Łączności – Państwowy Instytut Badawczy ul. Szachowa 1 04-894 Warszawa

Telefon

(+48) 5128 696

E-Mail

biblioteka@il-pib.pl

Odwiedź nas!

Ta strona wykorzystuje pliki 'cookies'. Więcej informacji