Topolojik Veri Analizi ve Makine Öğrenmesiyle QR Kod Görsellerinden Siber Tehditlerin Tespiti: Yenilikçi Bir Yaklaşım

Abstract

Bu çalışma, QR koda dönüştürülmüş siber tehdit verilerinin sınıflandırılmasında topolojik veri analizi (TDA) ile makine öğrenmesi algoritmalarını birleştiren yenilikçi bir yaklaşım sunmaktadır. Çalışmada, ağ trafiğine ilişkin meta verileri içeren tablosal bir veri seti kullanılmıştır. Tablosal veriler, öncelikle Quick Response (QR) kod görsellerine dönüştürülmüş ve ardından bu görseller 32×32 ve 64×64 piksel boyutlarına ölçeklendirilmiştir. Elde edilen QR kod görüntüleri üzerinden kübik kompleks temsilleri oluşturulmuş ve kalıcı homoloji yöntemi kullanılarak H0 ve H1 boyutlarına ait topolojik özellikler çıkarılmıştır. Bu özellikler, kalıcılık görüntüsü (PI) dönüşümü ile sabit boyutlu vektörlere dönüştürülmüş ve farklı PI çözünürlük seviyelerinde (10×10, 20×20, 40×40 ve 60×60) analiz edilmiştir. Elde edilen özellik vektörleri, eXtreme Gradient Boosting, Light Gradient  Boosting Machine, CatBoost, AdaBoost ve Gradient Boosting (GB) algoritmaları ile 5 katlı çapraz doğrulama yöntemiyle değerlendirilmiştir. Deneysel sonuçlara göre, 32×32 piksel boyutundaki QR veri seti ve 20×20 PI çözünürlük seviyesinde önerilen TDA-AdaBoost modeli %99,16 doğruluk değerine ulaşmıştır. Ayrıca, 64×64 piksel boyutundaki QR veri seti ve 10×10 PI çözünürlük seviyesinde TDA-GB modeli ile %95,10 doğruluk değeri elde edilmiştir. Bulgular, QR kod temsillerinden çıkarılan topolojik özelliklerin siber tehdit sınıflandırmasında güçlü bir temsil kabiliyeti sunduğunu ve önerilen yaklaşımın siber güvenlik alanında uygulanabilir bir alternatif oluşturduğunu göstermektedir.

Keywords

• Siber tehdit
• QR kod
• Topolojik veri analizi
• Kalıcılık görüntüsü
• Boosting tabanlı makine öğrenmesi
• Siber güvenlik

Cyber Threat Detection from QR Code Images via Topological Data Analysis and Machine Learning: A Novel Approach

Abstract

This study presents an innovative approach that combines topological data analysis (TDA) with machine learning algorithms to classify cyber threat data encoded as QR codes. This study used a tabular dataset containing metadata on network traffic. The tabular data were first converted into Quick Response (QR) code images, which were then resized to 32×32 and 64×64 pixel dimensions. cubical complex representations were generated from the resulting QR code images, and topological features corresponding to the H0 and H1 dimensions were extracted using the persistent homology method. These features were converted into fixed-dimensional vectors via the Persistence Image (PI) transformation and analyzed at different PI resolution levels (10×10, 20×20, 40×40, and 60×60). The feature vectors were evaluated using the eXtreme Gradient Boosting, Light Gradient Boosting Machine, CatBoost, AdaBoost, and Gradient Boosting (GB) algorithms, with 5-fold cross-validation. According to experimental results, the proposed TDA-AdaBoost model achieved an accuracy score of 99.16% with a 32×32 pixel QR dataset and a resolution of 20×20 PI. Furthermore, the TDA-GB model achieved an accuracy score of 95.10% with a 64×64 pixel QR dataset and a resolution of 10×10 PI. The findings demonstrate that topological features extracted from QR code representations offer strong representational capabilities for cyber threat classification and that the proposed approach constitutes a viable alternative in cybersecurity.

Keywords

• Cyber threat
• QR code
• Topological data analysis
• Persistence image
• Boosting-based machine learning
• Cybersecurity

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