Malicious language identification based on neural network

Mingyang Ma; Hanyi Luo

doi:10.1117/12.2680805

23 May 2023 Malicious language identification based on neural network

Mingyang Ma, Hanyi Luo

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264532 (2023) https://doi.org/10.1117/12.2680805
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Malicious language identification has been a critical issue in current network environment, automatically investigating malicious sentences has been concerned by security protection of neural language processing However, existing research about malicious investigation is almost based on the keywords detection and the system cannot detect the cryptic information in networks, which needs human to sign the malicious keyword and cost lots of computation costs. In this paper, we describe the usage of a neural network for language identification of malicious information. The neural network model was trained on a dataset of malicious and non-malicious information samples and then simulated on a set of unseen malicious text samples. From our extensive experimental results, we can conclude that the proposed model was able to precisely identify malicious language and achieve a high score approximately about 0.874. The results indicate that neural network models can be a useful tool for detecting malicious language.

Citation Download Citation

Mingyang Ma and Hanyi Luo "Malicious language identification based on neural network", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264532 (23 May 2023); https://doi.org/10.1117/12.2680805

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