The phenomenon of phishing has now been a common threat, since many individuals and webpages have been observed to be attacked by phishers. The common purpose of phishing activities is to obtain user’s personal information for illegitimate usage. Considering the growing intensity of the issue, this study is aimed at developing a new hybrid rule-based solution by incorporating six different algorithm models that may efficiently detect and control the phishing issue. The study incorporates 37 features extracted from six different methods including the black listed method, lexical and host method, content method, identity method, identity similarity method, visual similarity method, and behavioral method. Furthermore, comparative analysis was undertaken between different machine learning and deep learning models which includes CART (decision trees), SVM (support vector machines), or KNN (-nearest neighbors) and deep learning models such as MLP (multilayer perceptron) and CNN (convolutional neural networks). Findings of the study indicated that the method was effective in analysing the URL stress through different viewpoints, leading towards the validity of the model. However, the highest accuracy level was obtained for deep learning with the given values of 97.945 for the CNN model and 93.216 for the MLP model, respectively. The study therefore concludes that the new hybrid solution must be implemented at a practical level to reduce phishing activities, due to its high efficiency and accuracy.

中文翻译：

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使用卷积神经网络的基于混合规则的网络钓鱼 URL 检测解决方案

网络钓鱼现象现在已经成为一种普遍的威胁，因为已经观察到许多个人和网页受到网络钓鱼者的攻击。网络钓鱼活动的常见目的是获取用户的个人信息以供非法使用。考虑到问题日益严重，本研究旨在通过结合六种不同的算法模型来开发一种新的基于混合规则的解决方案，这些模型可以有效地检测和控制网络钓鱼问题。该研究结合了从六种不同方法中提取的 37 个特征，包括黑名单方法、词汇和宿主方法、内容方法、身份方法、身份相似性方法、视觉相似性方法和行为方法。此外，还对不同的机器学习和深度学习模型进行了比较分析，其中包括 CART（决策树）、-最近邻）和深度学习模型，如 MLP（多层感知器）和 CNN（卷积神经网络）。研究结果表明，该方法可以有效地从不同的角度分析 URL 压力，从而提高了模型的有效性。然而，深度学习获得了最高的准确度，CNN 模型的给定值为 97.945，MLP 模型的给定值为 93.216。因此，研究得出的结论是，新的混合解决方案必须在实际层面实施，以减少网络钓鱼活动，因为它具有高效率和准确性。