Intelligent Methods for Accurately Detecting Phishing Websites
Intelligent Methods for Accurately Detecting Phishing Websites
复制标题
准确检测钓鱼网站的智能方法
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Mohammad Almseidin
中科院分区：
文献类型：
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作者：
Almaha Abuzuraiq;M. Alkasassbeh;Mohammad Almseidin
随着技术的不断发展，出现了大量具有不同目的的网站。但在这个庞大的集合中存在一种特殊类型，即所谓的网络钓鱼网站，其目的是欺骗用户。检测网络钓鱼网站的主要挑战是发现所使用的技术。网络钓鱼者不断改进他们的策略并创建可以保护自己免受多种形式检测方法侵害的网页。因此，非常有必要开发可靠、主动和现代的网络钓鱼检测方法来对抗网络钓鱼者使用的自适应技术。本文通过将不同的网络钓鱼检测方法分为三个主要组来进行回顾。然后，所提出的模型分两个阶段提出。在第一阶段，应用不同的机器学习算法来验证所选数据集并对其应用特征选择方法。因此，仅利用 48 个特征中的 20 个特征与随机森林相结合即可实现最佳准确率，达到 98.11%。而在第二阶段，相同的数据集被应用于各种模糊逻辑算法。应用模糊逻辑算法的实验结果也令人难以置信。其中应用只有五个特征的FURIA算法，准确率达到99.98%。最后对机器学习算法和模糊逻辑算法的应用结果进行了比较和讨论。使用模糊逻辑算法的性能超过使用机器学习算法的情况。
With increasing technology developments, there is a massive number of websites with varying purposes. But a particular type exists within this large collection, the so-called phishing sites which aim to deceive their users. The main challenge in detecting phishing websites is discovering the techniques that have been used. Where phishers are continually improving their strategies and creating web pages that can protect themselves against many forms of detection methods. Therefore, it is very necessary to develop reliable, active and contemporary methods of phishing detection to combat the adaptive techniques used by phishers. In this paper, different phishing detection approaches are reviewed by classifying them into three main groups. Then, the proposed model is presented in two stages. In the first stage, different machine learning algorithms are applied to validate the chosen dataset and applying features selection methods on it. Thus, the best accuracy was achieved by utilizing only 20 features out of 48 features combined with Random Forest is 98.11%. While in the second stage, the same dataset is applied to various fuzzy logic algorithms. As well the experimental results from the application of Fuzzy logic algorithms were incredible. Where in applying the FURIA algorithm with only five features the accuracy rate was 99.98%. Finally, comparison and discussion of the results between applying machine learning algorithms and fuzzy logic algorithms is done. Where the performance of using fuzzy logic algorithms exceeds the use of machine learning algorithms.