CCBLA: a Lightweight Phishing Detection Model Based on CNN, BiLSTM, and Attention Mechanism

Phishing, in which social engineering techniques such as emails and instant messaging are employed and malicious links are disguised as normal URLs to steal sensitive information, is currently a major threat to networks worldwide. Phishing detection systems generally adopt feature engineering as one of the most important approaches to detect or even prevent phishing attacks. However, the accuracy of feature engineering systems is heavily dependent on the prior knowledge of features. In addition, extracting comprehensive features from different dimensions for high detection accuracy is time-consuming. To address these issues, this paper proposes a lightweight model that combines convolutional neural network (CNN), bi-directional long short-term memory (BiLSTM), and the attention mechanism for phishing detection. The proposed model, called the char-convolutional and BiLSTM with attention mechanism (CCBLA) model, employs deep learning to automatically extract features from target URLs and uses the attention mechanism to weight the importance of the selected features under different roles during phishing detection. The results of experiments conducted on two datasets with different scales show that CCBLA is accurate in phishing attack detection with minimal time consumption.