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Journals / Bilgisayar Bilimleri / 2017 / Cilt: 2 - Sayı: 1

Effective Classification of Phishing Web Pages Based on New Rules by Using Extreme Learning Machines

Mustafa KAYTAN
Davut HANBAY
Pages
15–36
Publication Date
2017-06-01
DOI
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AI Summary & Key Takeaways

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

The average classification accuracy was measured as 95.05%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Internet is an essential part of our life.
📊
Key Findings & Data:The model has 30 inputs and 1 output.
💡
Conclusion & Impact:The average classification accuracy was measured as 95.05%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Machine Learning · Extreme Learning Machine · Phishing

Abstract

Internet is an essential part of our life. Internet users can beaffectedfrom different types of cyber threats. Thus cyber threats may attack financial data, private information, online banking and e-commerce. Phishing is a type of cyber threats that is targeting to get private information such as credit cards information and social security numbers. There is not a specific solution that can detect whole phishing attacks. In this study, we proposed an intelligent model for detecting phishing web pages based on Extreme Learning Machine. Types of web pages are different in terms of their features. Hence, we must use a specific web page features set to prevent phishing attacks. We proposed a model based on machine learning techniques to detect phishing web pages.We have suggested some new rules to have efficient features. The model has 30 inputs and 1 output. In this application, the 10-fold cross-validation test has been performed. The average classification accuracy was measured as 95.05%.

Özet

Internet is an essential part of our life. Internet users can beaffectedfrom different types of cyber threats. Thus cyber threats may attack financial data, private information, online banking and e-commerce. Phishing is a type of cyber threats that is targeting to get private information such as credit cards information and social security numbers. There is not a specific solution that can detect whole phishing attacks. In this study, we proposed an intelligent model for detecting phishing web pages based on Extreme Learning Machine. Types of web pages are different in terms of their features. Hence, we must use a specific web page features set to prevent phishing attacks. We proposed a model based on machine learning techniques to detect phishing web pages.We have suggested some new rules to have efficient features. The model has 30 inputs and 1 output. In this application, the 10-fold cross-validation test has been performed. The average classification accuracy was measured as 95.05%.

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Cite This Article

KAYTAN, M., HANBAY, D. (2017). Effective Classification of Phishing Web Pages Based on New Rules by Using Extreme Learning Machines. Bilgisayar Bilimleri, Cilt: 2 - Sayı: 1, 15–36. https://ajindex.com/articles/743246

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