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Classification of Phishing Email Using Random Forest Machine Learning Technique

Author

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  • Andronicus A. Akinyelu
  • Aderemi O. Adewumi

Abstract

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.

Suggested Citation

  • Andronicus A. Akinyelu & Aderemi O. Adewumi, 2014. "Classification of Phishing Email Using Random Forest Machine Learning Technique," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-6, April.
  • Handle: RePEc:hin:jnljam:425731
    DOI: 10.1155/2014/425731
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    Cited by:

    1. Zhengyang Fan & Wanru Li & Kathryn Blackmond Laskey & Kuo-Chu Chang, 2024. "Investigation of Phishing Susceptibility with Explainable Artificial Intelligence," Future Internet, MDPI, vol. 16(1), pages 1-18, January.
    2. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    3. Qi Wu & Qiang Li & Dong Guo & Xiangyu Meng, 2022. "Exploring the vulnerability in the inference phase of advanced persistent threats," International Journal of Distributed Sensor Networks, , vol. 18(3), pages 15501329221, March.

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