A Bi-Directional LSTM Model with Attention for Malicious URL Detection
Fangli Ren, Zhengwei Jiang, Jian Liu · 2019
Malicious URLs have become an important attack vector used by attackers to perpetrate cybercrimes, how to effectively detect malicious URLs is an important and urgent problem to be solved. Due to current feature based malicious URLs detection models need manual feature engineering, and deep learning based models have their limit on processing long sequences, which reduces the detection performance. We proposed an attentional based BiLSTM model AB-BiLSTM for the Malicious URLs detection in this paper. Firstly, the URLs were preprocessed and converted into word vectors by using pre-trained Word2Vec, then BiLSTM combined with an attention mechanism was trained to extract URL sequences features and classify them. The model was tested on collected dataset, the experimental results show that our proposed model can achieve the accuracy of 98.06%, the precision rate of 96.05, the recall rate of 95.79% and the F1 Score of 95.92%, which achieved better performance than other comparison traditional machine learning based and deep learning based models.