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Dawei Ma

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Proceedings Article | 1 June 2023 Paper

Phishing URL recognition based on ordered neurons long short-term memory neural network and attention mechanism feature fusion

Qiaojuan Jia, Ming Liu, Xiaoyu Jin, Dawei Ma

Proceedings Volume 12718, 127181C (2023) https://doi.org/10.1117/12.2681573

KEYWORDS: Feature fusion, Performance modeling, Feature extraction, Neural networks, Matrices, Neurons, Data processing, Machine learning, Deep learning

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Aiming at the increasingly serious problems of phishing websites and the poor characteristics of identifying phishing URL, a phishing URL recognition model based onordered neurons long short-term memory neural network( ON-LSTM) and attention mechanism feature fusion is proposed. Firstly, the URL is divided based on the word level and character level of sensitive words and special characters, and then the URL vector after word segmentation is input into the onlstm model to realize feature extraction. In order to enrich the feature information, the feature fusion layer adopts the feature splicing method to fuse the features of sensitive word level and character level, and introduces the attention mechanism to further enhance the feature ability and depth of the module to extract the URL. Finally, the softmax function of the fully connected neural network is used to realize the classification and recognition of URL. The experimental results show that the phishing URL recognition model based on the fusion of ON-LSTM and attention mechanism features has better ability to identify phishing URLs, with an accuracy of 98.95% and an F1 value of 98.92%. Compared with the comparison model, this method has better recognition performance.

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