Filtering Turkish Spam Using LSTM From Deep Learning Techniques

Ersin Enes Eryılmaz, Durmuş Özkan Şahin, E. Kılıç
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引用次数: 9

Abstract

E-mails are used effectively by people or communities who want to do propaganda, advertisement, and phishing because of their ease of use and low cost. People or communities who want to achieve their goals send unnecessary and spam to the e-mail accounts they never knew. These mails cause serious financial and moral damages to internet users and also engage in internet traffic. Unsolicited e-mails (spam) are a method sent to the recipient without their consent and generally for malicious or promotional purposes. In this study, spam was detected with Keras deep learning library on the Turkish dataset. Turkish email dataset contains 800 e-mails, half of which are spam e-mails. With the deep learning algorithm long short term memory (LSTM), a 100% accuracy rate has been achieved in the Turkish e-mail dataset.
从深度学习技术中使用LSTM过滤土耳其垃圾邮件
电子邮件被想要进行宣传、广告和网络钓鱼的人们或社区有效地使用,因为它们易于使用和成本低。想要实现目标的人们或社区会向他们不认识的电子邮件帐户发送不必要的垃圾邮件。这些邮件给网民造成了严重的经济和精神上的损失,也造成了网络流量。未经请求的电子邮件(垃圾邮件)是未经收件人同意而发送给收件人的一种方法,通常是出于恶意或促销目的。在本研究中,使用Keras深度学习库在土耳其数据集上检测垃圾邮件。土耳其电子邮件数据集包含800封电子邮件,其中一半是垃圾邮件。利用长短期记忆(LSTM)深度学习算法,在土耳其电子邮件数据集中实现了100%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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