An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Computational Intelligence and Soft Computing Pub Date : 2023-09-20 DOI:10.1155/2023/6648970
Umair Maqsood, Saif Ur Rehman, Tariq Ali, Khalid Mahmood, Tahani Alsaedi, Mahwish Kundi
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引用次数: 1

Abstract

The use of short message service (SMS) and e-mail have increased too much over the last decades. 80% of people do not read e-mails while 98% of cell phone users daily read their SMS. However, these communication media are unsafe and can produce malicious attacks called spam. The e-mails that pretend to be from a trusted company to provide “financial or personal information” are phishing e-mails. These e-mails contain some links; users might download malicious software on their computers when they click on them. Most techniques and models are developed to automatically detect these “SMS and e-mails” but none of them achieved 100% accuracy. In previous studies using machine learning (ML), spam detection using a small dataset has resulted in lower accuracy. To counter this problem, in this paper, multiple classifiers of ML and a classifier of deep learning (DL) were applied to the SMS and e-mail dataset for spam detection with higher accuracy. After conducting experiments on the real dataset, the researchers concluded that the proposed system performed better and more accurately than previously existing models. Specifically, the support vector machine (SVM) classifier outperformed all others. These results suggest that SVM is the optimal choice for classification purposes.
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基于深度学习的短信和垃圾邮件检测智能框架
在过去的几十年里,短信服务和电子邮件的使用增加了太多。80%的人不看电子邮件,而98%的手机用户每天都看短信。然而,这些通信媒体是不安全的,并可能产生称为垃圾邮件的恶意攻击。这些电子邮件假装来自一个值得信赖的公司,提供“财务或个人信息”,是网络钓鱼电子邮件。这些电子邮件包含一些链接;当用户点击恶意软件时,他们的电脑上可能会下载恶意软件。大多数技术和模型都是为了自动检测这些“短信和电子邮件”而开发的,但它们都没有达到100%的准确性。在以前使用机器学习(ML)的研究中,使用小数据集进行垃圾邮件检测导致准确性较低。为了解决这一问题,本文将机器学习的多个分类器和深度学习的分类器(DL)应用于短信和电子邮件数据集,以提高垃圾邮件检测的准确性。在对真实数据集进行实验后,研究人员得出结论,所提出的系统比以前现有的模型表现得更好、更准确。具体来说,支持向量机(SVM)分类器优于所有其他分类器。这些结果表明支持向量机是用于分类目的的最佳选择。
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来源期刊
Applied Computational Intelligence and Soft Computing
Applied Computational Intelligence and Soft Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
6.10
自引率
3.40%
发文量
59
审稿时长
21 weeks
期刊介绍: Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.
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