Efficient Detection of Phishing Attacks with Hybrid Neural Networks

被引：0

作者：

Zhang, Xiaoqing ^{[1
]}

Shi, Dongge ^{[1
]}

Zhang, Hongpo ^{[1
]}

Liu, Wei ^{[2
]}

Li, Runzhi ^{[1
]}

机构：

[1] Zhengzhou Univ, Collaborat Innovat Ctr Internet Healthcare, Zhengzhou 10459, Henan, Peoples R China

[2] Zhengzhou Univ, Software & Appl Sci & Technol Inst, Zhengzhou, Henan, Peoples R China

来源：

2018 IEEE 18TH INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT) | 2018年

基金：

中国国家自然科学基金;

关键词：

phishing attack; convolutional neural network; autoencoder; reconstruct;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Many machine learning techniques and social engineering methods have been adopted and devised to combat phishing threats. In this paper, a novel hybrid deep learning model is proposed to identify phishing attacks. It incorporates two components: an autoencoder (AE) and a convolutional neural network (CNN). The AE is adopted to reconstruct features that enhances correlation relationship among the features explicitly. The results from the experiments show that the model is able to detect phishing attacks with a mean accuracy over 97.68%, yet it has high generalization ability and can detect phishing attacks in the receivable time scale.

引用

页码：844 / 848

页数：5

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