A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

被引:20
|
作者
Aldakheel, Eman Abdullah [1 ]
Zakariah, Mohammed [2 ]
Gashgari, Ghada Abdalaziz [3 ]
Almarshad, Fahdah A. [4 ]
Alzahrani, Abdullah I. A. [5 ]
机构
[1] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Comp Sci, Riyadh 11671, Saudi Arabia
[2] King Saud Univ, Coll Comp & Informat Sci, Dept Comp Sci, Riyadh 12372, Saudi Arabia
[3] Univ Jeddah, Coll Comp Sci & Engn, Dept Cybersecur, Ar Rabwah Jeddah 23449, Saudi Arabia
[4] Prince Sattam Bin Abdul Aziz Univ, Coll Comp Engn & Sci, Dept Informat Syst, Al Kharj 11942, Saudi Arabia
[5] Shaqra Univ, Coll Sci & Humanities Al Quwaiiyah, Dept Comp Sci, Shaqra 11961, Saudi Arabia
关键词
phishing detection system; deep learning; convolutional neural network; PhishTank data set; URL analysis; machine-learning;
D O I
10.3390/s23094403
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Organizations and individuals worldwide are becoming increasingly vulnerable to cyberattacks as phishing continues to grow and the number of phishing websites grows. As a result, improved cyber defense necessitates more effective phishing detection (PD). In this paper, we introduce a novel method for detecting phishing sites with high accuracy. Our approach utilizes a Convolution Neural Network (CNN)-based model for precise classification that effectively distinguishes legitimate websites from phishing websites. We evaluate the performance of our model on the PhishTank dataset, which is a widely used dataset for detecting phishing websites based solely on Uniform Resource Locators (URL) features. Our approach presents a unique contribution to the field of phishing detection by achieving high accuracy rates and outperforming previous state-of-the-art models. Experiment results revealed that our proposed method performs well in terms of accuracy and its false-positive rate. We created a real data set by crawling 10,000 phishing URLs from PhishTank and 10,000 legitimate websites and then ran experiments using standard evaluation metrics on the data sets. This approach is founded on integrated and deep learning (DL). The CNN-based model can distinguish phishing websites from legitimate websites with a high degree of accuracy. When binary-categorical loss and the Adam optimizer are used, the accuracy of the k-nearest neighbors (KNN), Natural Language Processing (NLP), Recurrent Neural Network (RNN), and Random Forest (RF) models is 87%, 97.98%, 97.4% and 94.26%, respectively, in contrast to previous publications. Our model outperformed previous works due to several factors, including the use of more layers and larger training sizes, and the extraction of additional features from the PhishTank dataset. Specifically, our proposed model comprises seven layers, starting with the input layer and progressing to the seventh, which incorporates a layer with pooling, convolutional, linear 1 and 2, and linear six layers as the output layers. These design choices contribute to the high accuracy of our model, which achieved a 98.77% accuracy rate.
引用
收藏
页数:27
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