Paper
21 December 2023 A key code detection model based on semantic convolutional memory fusion network
Beibei Su, Xingjie Huang, Jing Zhang, Yaozhong Dong
Author Affiliations +
Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129703F (2023) https://doi.org/10.1117/12.3012088
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China
Abstract
The leakage of source code will lead to a serious information security crisis. It is of great significance for electric power mobile micro-applications to detect key codes and then protect them. Aiming at the poor representation ability of JavaScript code, we suggested a key code detection model based on a semantic convolutional memory fusion network. The fusion network was constructed by combining TextCNN and Attention-based BiLSTM to extract code features. The lexical unit sequence that is abstracted from the abstract syntax tree will be input to the fusion network for identification and classification. According to the experimental results, the proposed model shows improvement in the representation ability and overall performance in key code detection.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Beibei Su, Xingjie Huang, Jing Zhang, and Yaozhong Dong "A key code detection model based on semantic convolutional memory fusion network", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129703F (21 December 2023); https://doi.org/10.1117/12.3012088
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KEYWORDS
Semantics

Feature extraction

Data modeling

Neural networks

Convolution

Performance modeling

Statistical modeling

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