头脑风暴优化与长短期记忆支持网络钓鱼网页分类

M. Zaher, N. M. Eldakhly
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引用次数: 0

摘要

网络钓鱼是当今数字世界中常见的一种网络攻击。如何以最大的性能精度和最小的计算复杂度进行网络钓鱼检测一直是人们关注的话题。近年来,为了提高网络钓鱼检测率和减少计算约束,建立了一种新的网络钓鱼检测技术。但是,一个解决方案不足以解决网络空间攻击者带来的所有问题。因此,这项工作的初始目标是分析检测网络钓鱼活动的不同深度学习(DL)技术的性能。本文介绍了一种基于长短期记忆的网络钓鱼网页分类(BSOLSTM-PWC)的网络安全头脑风暴优化方法。提出的BSOLSTM-PWC技术能够通过对网络钓鱼网页的识别和分类来实现网络安全。为此,BSOLSTM-PWC技术首先采用数据预处理技术将数据转换为实际格式。此外,BSOLSTM-PWC技术采用LSTM分类器对网络钓鱼网页进行识别和分类。此外,利用BSO算法对LSTM模型中涉及的超参数进行适当调整。为了报告BSOLSTM-PWC方法的改进结果,使用基准数据集进行了广泛的模拟分析。实验结果报告了BSOLSTM-PWC方法对现有方法的增强结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain Storm Optimization with Long Short Term Memory Enabled Phishing Webpage Classification for Cybersecurity
Phishing is a familiar kind of cyberattack in the present digital world. Phishing detection with maximum performance accuracy and minimum computational complexity is continuously a topic of much interest. A novel technology was established for improving the phishing detection rate and decreasing computational constraints recently. But, one solution has inadequate for addressing every problem due to attackers from cyberspace. Thus, the initial objective of this work is for analysing the performance of different deep learning (DL) techniques from detection phishing activity. This study introduces a novel Brain Storm Optimization with Long Short Term Memory Enabled Phishing Webpage Classification (BSOLSTM-PWC) for Cybersecurity. The proposed BSOLSTM-PWC technique enables to accomplish cybersecurity by the identification and classification of phishing webpages. To accomplish this, the BSOLSTM-PWC technique initially employs data pre-processing technique to transform the data into actual format. Besides, the BSOLSTM-PWC technique employs LSTM classifier for the identification and categorization of phishing webpages. Moreover, the BSO algorithm is utilized to appropriately adjust the hyperparameters involved in the LSTM model. For reporting the improved outcomes of the BSOLSTM-PWC method, a wide-ranging simulation analysis is made using benchmark dataset. The experimental outcomes reported the enhanced outcomes of the BSOLSTM-PWC method on existing methods.
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CiteScore
0.70
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