A Novel DEA-ELM Hybrid Method for Web Phishing Detection

Yasin Sönmez; Süleyman Dal

doi:10.54365/adyumbd.1752606

TR EN

Web Kimlik Avı Tespiti için Yeni Bir DEA-ELM Hibrit Yöntemi

Öz

Oltalama saldırıları, kullanıcıların hassas bilgilerini çalmak için aldatıcı web sayfaları kullanan yaygın bir siber güvenlik tehdididir. Bu çalışmada, oltalama web sitesi tespiti için Diferansiyel Evrim Algoritmaları (DEA) ile Aşırı Öğrenme Makineleri (ELM) çerçevesini entegre eden yeni bir sınıflandırma modeli öneriyoruz. Yaklaşım, özellikler arası sinyal iyileştirme için bir DEA mekanizması sunar ve bunu DEA aracılığıyla optimize edilmiş bir ELM ile birleştirir. Önerilen DEA-ELM modeli, Web Sayfası Kimlik Avı Tespit veri kümesi üzerinde değerlendirilmiştir. Random Forest, Lojistik Regresyon, Destek Vektör Makinesi (SVM) ve Karar Ağacı gibi geleneksel makine öğrenmesi modelleri %93 ile %97 arasında doğruluk sağlarken, önerilen DEA-ELM modeli %99,86 doğruluk oranı elde etmiştir; ayrıca yüksek kesinlik, geri çağırma ve F1 skoru metrikleri sunmuştur. %93 ile %97 arasında doğruluk elde eden Rastgele Orman, Lojistik Regresyon, Destek Vektör Makinesi ve Karar Ağacı gibi geleneksel makine öğrenimi modelleriyle karşılaştırıldığında, önerilen DEA-ELM modeli yüksek hassasiyet, geri çağırma ve F1 puanı ölçümleri sunmuştur. Bu sonuçlar, ölçeklenebilir, doğru ve gerçek zamanlı kimlik avı tespit sistemleri oluşturmada DEA için optimize edilmiş ELM'nin DEA analiziyle birleştirilmesinin potansiyelini doğrulamaktadır.

Anahtar Kelimeler

Kimlik Avı Tespiti , Siber Güvenlik , Makine Öğrenmesi

A Novel DEA-ELM Hybrid Method for Web Phishing Detection

Abstract

Phishing attacks are a pervasive cybersecurity threat, using deceptive web pages to steal users' sensitive information. Detecting phishing sites with high precision and efficiency is crucial for building effective countermeasures. In this study, we propose a novel classification model that integrates a Differential Evolution Algorithms (DEA) with Extreme Learning Machines (ELM) framework for phishing website detection. The approach introduces a DEA mechanism for inter-feature signal enhancement and couples it with an ELM, optimized through a DEA. The proposed DEA-ELM model was evaluated on the Web Page Phishing Detection dataset, Compared to traditional machine learning models such as Random Forest, Logistic Regression, Support Vector Machine (SVM), and Decision Tree, which achieved accuracies between 93% and 97%, the proposed DEA-ELM model achieved a remarkable 99.86% accuracy, along with high precision, recall, and F1-score metrics. These results confirm the potential of DEA-optimized ELM combined with DEA analysis in creating scalable, accurate, and real-time phishing detection systems. The model also provides a reproducible framework by using publicly available data and open-source feature extraction scripts. Future work may explore hybrid feature selection strategies, larger-scale deployment, and online learning extensions.

Keywords

Phishing Detection , Cyber Security , Machine Leerning

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Sistem Yazılımı

Bölüm

Araştırma Makalesi

Yazarlar

Yasin Sönmez
Türkiye

Süleyman Dal ^*
0000-0002-4564-8076
Türkiye

Yayımlanma Tarihi

24 Aralık 2025

Gönderilme Tarihi

28 Temmuz 2025

Kabul Tarihi

31 Ekim 2025

Yayımlandığı Sayı

Yıl 1970 Cilt: 12 Sayı: 27

DOI

https://doi.org/10.54365/adyumbd.1752606

IZ

https://izlik.org/JA93FA77UF

APA

Sönmez, Y., & Dal, S. (2025). A Novel DEA-ELM Hybrid Method for Web Phishing Detection. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 12(27), 390-402. https://doi.org/10.54365/adyumbd.1752606

AMA

1.Sönmez Y, Dal S. A Novel DEA-ELM Hybrid Method for Web Phishing Detection. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2025;12(27):390-402. doi:10.54365/adyumbd.1752606

Chicago

Sönmez, Yasin, ve Süleyman Dal. 2025. “A Novel DEA-ELM Hybrid Method for Web Phishing Detection”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 12 (27): 390-402. https://doi.org/10.54365/adyumbd.1752606.

EndNote

Sönmez Y, Dal S (01 Aralık 2025) A Novel DEA-ELM Hybrid Method for Web Phishing Detection. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 12 27 390–402.

IEEE

[1]Y. Sönmez ve S. Dal, “A Novel DEA-ELM Hybrid Method for Web Phishing Detection”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, c. 12, sy 27, ss. 390–402, Ara. 2025, doi: 10.54365/adyumbd.1752606.

ISNAD

Sönmez, Yasin - Dal, Süleyman. “A Novel DEA-ELM Hybrid Method for Web Phishing Detection”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 12/27 (01 Aralık 2025): 390-402. https://doi.org/10.54365/adyumbd.1752606.

JAMA

1.Sönmez Y, Dal S. A Novel DEA-ELM Hybrid Method for Web Phishing Detection. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2025;12:390–402.

MLA

Sönmez, Yasin, ve Süleyman Dal. “A Novel DEA-ELM Hybrid Method for Web Phishing Detection”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, c. 12, sy 27, Aralık 2025, ss. 390-02, doi:10.54365/adyumbd.1752606.

Vancouver

1.Yasin Sönmez, Süleyman Dal. A Novel DEA-ELM Hybrid Method for Web Phishing Detection. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 01 Aralık 2025;12(27):390-402. doi:10.54365/adyumbd.1752606

Web Kimlik Avı Tespiti için Yeni Bir DEA-ELM Hibrit Yöntemi

Öz

Anahtar Kelimeler

A Novel DEA-ELM Hybrid Method for Web Phishing Detection

Abstract

Keywords

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster