论文标题
基于卷积神经网络的高精度网络钓鱼检测
High Accuracy Phishing Detection Based on Convolutional Neural Networks
论文作者
论文摘要
网络钓鱼的持续增长以及网站网站的不断增长导致全世界的个人和组织越来越受到各种网络攻击。因此，需要更有效的网络钓鱼检测才能改善网络防御。因此，在本文中，我们提出了一种基于深度学习的方法，以使网站钓鱼站点的高精度检测。所提出的方法利用卷积神经网络（CNN）进行高精度分类，以区分真正的站点和网络钓鱼地点。我们使用从6,157个真实和4,898个网络钓鱼网站获得的数据集评估模型。根据广泛的实验的结果，我们的基于CNN的模型被证明在检测未知的网络钓鱼位点非常有效。此外，基于CNN的方法的性能要比在同一数据集上评估的传统机器学习分类器更好，而F1分数为0.976，达到98.2％的网络钓鱼检测率。本文介绍的方法与基于深度学习的网站钓鱼网站检测的最新技术相比。
The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites. Based on the results of extensive experiments, our CNN based models proved to be highly effective in detecting unknown phishing sites. Furthermore, the CNN based approach performed better than traditional machine learning classifiers evaluated on the same dataset, reaching 98.2% phishing detection rate with an F1-score of 0.976. The method presented in this paper compares favourably to the state-of-the art in deep learning based phishing website detection.
