PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks
Joint Authors
Zhang, Feng
Luo, Xi
Zhang, Shigeng
Wang, Weiping
Source
Security and Communication Networks
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-29
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Information Technology and Computer Science
Abstract EN
Through well-designed counterfeit websites, phishing induces online users to visit forged web pages to obtain their private sensitive information, e.g., account number and password.
Existing antiphishing approaches are mostly based on page-related features, which require to crawl content of web pages as well as accessing third-party search engines or DNS services.
This not only leads to their low efficiency in detecting phishing but also makes them rely on network environment and third-party services heavily.
In this paper, we propose a fast phishing website detection approach called PDRCNN that relies only on the URL of the website.
PDRCNN neither needs to retrieve content of the target website nor uses any third-party services as previous approaches do.
It encodes the information of an URL into a two-dimensional tensor and feeds the tensor into a novelly designed deep learning neural network to classify the original URL.
We first use a bidirectional LSTM network to extract global features of the constructed tensor and give all string information to each character in the URL.
After that, we use a CNN to automatically judge which characters play key roles in phishing detection, capture the key components of the URL, and compress the extracted features into a fixed length vector space.
By combining the two types of networks, PDRCNN achieves better performance than just using either one of them.
We built a dataset containing nearly 500,000 URLs which are obtained through Alexa and PhishTank.
Experimental results show that PDRCNN achieves a detection accuracy of 97% and an AUC value of 99%, which is much better than state-of-the-art approaches.
Furthermore, the recognition process is very fast: on the trained PDRCNN model, the average per URL detection time only cost 0.4 ms.
American Psychological Association (APA)
Wang, Weiping& Zhang, Feng& Luo, Xi& Zhang, Shigeng. 2019. PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks. Security and Communication Networks،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1210325
Modern Language Association (MLA)
Wang, Weiping…[et al.]. PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks. Security and Communication Networks No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1210325
American Medical Association (AMA)
Wang, Weiping& Zhang, Feng& Luo, Xi& Zhang, Shigeng. PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks. Security and Communication Networks. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1210325
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210325