Abstract
This paper aims to present the design and implementation of a prototype that recognizes grooming attacks in the context of COP (child online protection) using Natural Language Processing and Machine Learning hybrid model, via Convolutional Neural Networks (CNN). The solution uses a vector representation of words as the semantic model and the implementation of the model was made using TensorFlow, evaluating the classification of grooming for a text (dialogue) prepared asynchronously in a controlled environment according to methodologies, techniques, frameworks and multiple proposed techniques with his development described. The model predicts a high number of false positives, therefore low precision and F-score, but a high 88.4% accuracy and 0.81 AUROC (Area under the Receiver Operating Characteristic).
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Acknowledgment
This work was supported by Universidad de Caldas and Universidad Tecnológica de Pereira in their research groups GITIR and SIRIUS.
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Muñoz, F., Isaza, G., Castillo, L. (2021). SMARTSEC4COP: Smart Cyber-Grooming Detection Using Natural Language Processing and Convolutional Neural Networks. In: Dong, Y., Herrera-Viedma, E., Matsui, K., Omatsu, S., González Briones, A., Rodríguez González, S. (eds) Distributed Computing and Artificial Intelligence, 17th International Conference. DCAI 2020. Advances in Intelligent Systems and Computing, vol 1237. Springer, Cham. https://doi.org/10.1007/978-3-030-53036-5_2
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DOI: https://doi.org/10.1007/978-3-030-53036-5_2
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