论文标题
使用Bert检测垃圾邮件
Spam Detection Using BERT
论文作者
论文摘要
电子邮件和短信是当今通信中最受欢迎的工具，随着电子邮件和短信用户的增加增加，垃圾邮件的数量也增加了。垃圾邮件是任何一种不必要的，未经请求的数字通信，批量发送，垃圾邮件和短信会通过不必要地淹没网络链接而导致重大资源浪费。尽管大多数垃圾邮件邮件都来自希望推销产品的广告客户，但有些邮件的意图更为恶意，例如网络钓鱼电子邮件，旨在欺骗受害者，以放弃敏感信息，例如网站登录或信用卡信息，这种类型的网络犯罪被称为网络钓鱼。为了对策垃圾邮件，进行了许多研究和努力来构建能够将消息和电子邮件滤出来垃圾邮件或火腿的垃圾邮件检测器。 In this research we build a spam detector using BERT pre-trained model that classifies emails and messages by understanding to their context, and we trained our spam detector model using multiple corpuses like SMS collection corpus, Enron corpus, SpamAssassin corpus, Ling-Spam corpus and SMS spam collection corpus, our spam detector performance was 98.62%, 97.83%, 99.13% and 99.28% respectively.关键字：垃圾邮件检测器，BERT，机器学习，NLP，Transformer，Enron语料库，Spamassassins Corpus，SMS垃圾邮件检测语料库，Ling-Spam语料库。
Emails and SMSs are the most popular tools in today communications, and as the increase of emails and SMSs users are increase, the number of spams is also increases. Spam is any kind of unwanted, unsolicited digital communication that gets sent out in bulk, spam emails and SMSs are causing major resource wastage by unnecessarily flooding the network links. Although most spam mail originate with advertisers looking to push their products, some are much more malicious in their intent like phishing emails that aims to trick victims into giving up sensitive information like website logins or credit card information this type of cybercrime is known as phishing. To countermeasure spams, many researches and efforts are done to build spam detectors that are able to filter out messages and emails as spam or ham. In this research we build a spam detector using BERT pre-trained model that classifies emails and messages by understanding to their context, and we trained our spam detector model using multiple corpuses like SMS collection corpus, Enron corpus, SpamAssassin corpus, Ling-Spam corpus and SMS spam collection corpus, our spam detector performance was 98.62%, 97.83%, 99.13% and 99.28% respectively. Keywords: Spam Detector, BERT, Machine learning, NLP, Transformer, Enron Corpus, SpamAssassin Corpus, SMS Spam Detection Corpus, Ling-Spam Corpus.
