LSTM Based Phishing Detection for Big Email Data

In recent years, cyber criminals have successfully invaded many important information systems by using phishing mail, causing huge losses. The detection of phishing mail from big email data has been paid public attention. However, the camouflage technology of phishing mail is becoming more and more complex, and the existing detection methods are unable to confront with the increasingly complex deception methods and the growing number of emails. In this article, we proposed an LSTM based phishing detection method for big email data. The new method includes two important stages, sample expansion stage and testing stage under sufficient samples. In the sample expansion stage, we combined KNN with K-Means to expand the training data set, so that the size of training samples can meet the needs of in-depth learning. In the testing stage, we first preprocess these samples, including generalization, word segmentation and word vector generation. Then, the preprocessed data is used to train a LSTM model. Finally, on the basis of the trained model, we classify the phishing emails. By experiment, we evaluate the performance of the proposed method, and experimental results show that the accuracy of our phishing detection method can reach 95 percent.

Automated summary

Phishing emails are becoming more complex, making existing detection methods inadequate. This article introduces an LSTM-based phishing detection method that achieves 95% accuracy through sample expansion and testing stages.