Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers
Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers
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DOI：
10.1155/2020/8694796
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发表时间：
2020-09-24
期刊：
影响因子：
2.3
通讯作者：
Hafeez, Abdul
中科院分区：
文献类型：
--
作者：
Wang, Shan;Khan, Sulaiman;Hafeez, Abdul
随着电子设备数量的增加和通信系统的发展，安全性成为具有挑战性的问题之一。用户通过不同的异构设备（如智能传感器、执行器和许多其他设备）相互交互，以处理、监控和传达真实的生活的不同场景。这种通信需要一种安全的媒介，通过这种媒介，用户可以以安全可靠的方式进行通信，以便他们的信息不会丢失。该研究是一个奋进对网络钓鱼检测使用随机森林和BLSTM分类。实验结果表明，该算法在网络钓鱼检测中具有较好的应用前景，体现了该算法在信息安全领域的适用性。实验结果表明，基于BLSTM的网络钓鱼检测模型是突出的，以确保网络安全，通过产生95.47%的识别率相比，传统的基于RF的模型，产生87.53%的识别率。基于BLSTM的模型的这种高识别率反映了所提出的模型用于网络钓鱼检测的适用性。
With the increase in the number of electronic devices and developments in the communication system, security becomes one of the challenging issues. Users are interacting with each other through different heterogeneous devices such as smart sensors, actuators, and many other devices to process, monitor, and communicate different scenarios of real life. Such communication needs a secure medium through which users can communicate in a secure and reliable way so that their information may not be lost. The proposed study is an endeavor toward the detection of phishing by using random forest and BLSTM classifiers. The experimental results of the proposed study are promising in phishing detection, and the study reflects the applicability of the proposed algorithms in the information security. The experimental results show that the BLSTM-based phishing detection model is prominent in ensuring the network security by generating a recognition rate of 95.47% compared to the conventional RF-based model that generates a recognition rate of 87.53%. This high recognition rate for the BLSTM-based model reflects the applicability of the proposed model for phishing detection.