基于LSTM的钓鱼邮件检测系统

提出了一种基于LSTM的钓鱼邮件检测方式.该方式主要由两部分构成:分别为数据扩充部分及模型训练部分.数据扩展部分中，通过KNN与K-means算法扩大训练数据集，保证数据的数量能够满足深度学习算法的需要.在模型训练部分中，通过对数据进行预处理并将其转化为词向量矩阵，最后将转化完词向量通过训练得到LSTM神经网络模型.最终，可以根据训练好的LSTM模型将邮件分为正常邮件以及钓鱼邮件.通过实验对提出的算法进行了评估，实验结果显示提出的算法准确率可以达到95%. 

Phishing Mail Detection System Based on LSTM Neural Network

A long short-term memory (LSTM)-based phishing email detection method was proposed.This method was arranged mainly with two parts:data expansion part and model training part.In the data extension part, KNN and K-means algorithms were used to extend the training data set to make the number of data sets capable support deep learning algorithms. In the model training part,the data were preprocessed and transformed into a word vector matrix.And then the word vector matrix was trained to form LSTM neural network model.Finally,the mail can be divided into normal mail and phishing mail according to the trained LSTM model.Experiments were carried out to evaluate the proposed algorithm.The experimental results show that the proposed algorithm can achieve the accuracy up to 95%.