FDN-SA: Fuzzy deep neural-stacked autoencoder-based phishing attack detection in social engineering

Abstract

Phishing attacks have emerged as a major social engineering threat that affects businesses, governments, and general internet users. This work proposes a social engineering phishing detection technique based on Deep Learning (DL). Initially, website data is taken from the dataset. Then, the features of Natural Language Processing (NLP) like bag of words, n-gram, hashtags, sentence length, Term Frequency- Inverse Document Frequency of records (TF-IDF), and all caps are extracted and then web feature extraction is carried out. Later, the feature fusion is done using the Neyman similarity with Deep Belief Network (DBN). Afterwards, oversampling is used for data augmentation to enhance the number of training samples. Lastly, the detection of phishing attacks is performed by employing the proposed Fuzzy Deep Neural-Stacked Autoencoder (FDN-SA). Here, the proposed FDN-SA is developed by combining a Deep Neural Network (DNN), and Deep Stacked Autoencoder (DSA). Further, the investigation of FDN-SA is accomplished based on the accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) and is observed to compute values of 0.920, 0.925, and 0.921, respectively.