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Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2026 / Cilt: 41 Sayı: 1

Signal-level network traffic classification using darknet-based CNNs: A new methodological approach

Darknet tabanlı CNN'ler ile sinyal seviyesinde ağ trafiği sınıflandırması: Yeni bir yöntemsel yaklaşım

Munip Geylani(BİTLİS EREN ÜNİVERSİTESİ)0000-0002-1971-6952
Musa Çıbuk(BİTLİS EREN ÜNİVERSİTESİ)0000-0001-9028-2221
Ayhan Akbal(FIRAT ÜNİVERSİTESİ)0000-0001-5385-9781
Pages
383–400
Publication Date
2026-03-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The highest performance was achieved in the classification experiment using Darknet53 and the diagonal zigzag visualization technique, with an accuracy rate of 96.24%.The results demonstrate that network traffic can be effectively classified at the signal level, without decoding packet contents and without compromising data privacy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Network traffic classification is a significant research area that has been extensively studied by researchers.
📊
Key Findings & Data:The highest performance was achieved in the classification experiment using Darknet53 and the diagonal zigzag visualization technique, with an accuracy rate of 96.24%.The results demonstrate that network traffic can be effectively classified at the signal level, without decoding packet contents and without compromising data privacy.
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Conclusion & Impact:In this respect, the study offers a strong alternative to traditional classification methods.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Ağ trafiği sınıflandırması · sinyal seviyesinde ağ trafiği sınıflandırma · fiziksel katman

Abstract

Network traffic classification is a significant research area that has been extensively studied by researchers. Existing studies are mostly conducted at the data link layer or higher layers, typically using the decoded form of the traffic. In this study, a novel classification approach is proposed at the physical layer level. In the proposed method, network traffic is represented through electrical signals, and the classification process is performed directly on these raw signals. To this end, signals were collected from the physical connection between a computer and a network switch using an oscilloscope, and a new dataset consisting of six different traffic types was constructed by labeling these signals. The created signal dataset was visualized using horizontal, spiral, diagonal zigzag, and spectrogram techniques, and was then used to train Darknet-based CNN architectures. The highest performance was achieved in the classification experiment using Darknet53 and the diagonal zigzag visualization technique, with an accuracy rate of 96.24%.The results demonstrate that network traffic can be effectively classified at the signal level, without decoding packet contents and without compromising data privacy. In this respect, the study offers a strong alternative to traditional classification methods.

Özet

Ağ trafiği sınıflandırması, araştırmacılar tarafından yoğun biçimde çalışılan önemli bir alandır. Mevcut çalışmaların genellikle veri bağı katmanı ve üzerindeki katmanlarda, trafiğin çözümlenmiş (decode edilmiş) hali üzerinde yapıldığı görülmektedir. Bu çalışmada ise ağ trafiği sınıflandırmasına fiziksel katman düzeyinde, yeni bir sınıflandırma yaklaşımı sunulmaktadır. Önerilen yöntemde, ağ trafiği elektriksel sinyaller aracılığıyla temsil edilmekte ve sınıflandırma işlemi bu ham sinyaller üzerinden gerçekleştirilmektedir. Bu amaçla, bilgisayar ile ağ anahtarlama cihazı arasındaki fiziksel bağlantıdan, osiloskop yardımıyla sinyaller toplanmış ve etiketlenerek 6 farklı trafik türünü içeren yeni bir veri seti oluşturulmuştur. Oluşturulan sinyal veri seti, yatay, spiral, diyagonal zikzak ve spektrogram teknikleriyle görselleştirilerek Darknet tabanlı CNN mimarilerinin eğitilmesinde kullanılmıştır. Darknet53 ve diyagonal zikzak görselleştirme tekniğiyle yapılan sınıflandırma deneyinde %96,24 doğruluk oranı ile en yüksek performans elde edilmiştir. Elde edilen sonuçlar, paket içerikleri çözülmeden ve veri mahremiyeti ihlal edilmeden, ağ trafiğinin sinyal düzeyinde dahi yüksek doğrulukla sınıflandırılabileceğini göstermektedir. Bu yönüyle çalışma, geleneksel yöntemlere güçlü bir alternatif sunmaktadır.

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Cite This Article

Geylani, M., Çıbuk, M., Akbal, A. (2026). Signal-level network traffic classification using darknet-based CNNs: A new methodological approach. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 41 Sayı: 1, 383–400. https://doi.org/10.17341/gazimmfd.1761166

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