Mejoras en extracción de URLs en smishing mediante text spotting
Barra lateral del artículo
Contenido principal del artículo
Resumen
Los Equipos de Respuesta ante Emergencias Informáticas (CERT) reciben comúnmente capturas de pantalla de Smishing, que tratan de suplantar a distintos tipos de organizaciones, con el objetivo de apropiarse de información personal de usuarios o malversar fondos de sus cuentas mediante enlaces maliciosos. Los CERTs buscan soluciones automatizadas que permitan recuperar URLs de capturas de pantalla. Para extraer texto pueden utilizarse métodos basados en el reconocimiento óptico de caracteres (OCR), pero su rendimiento es bajo debido a problemas como la baja calidad de la imagen o textos divididos en múltiples frases. Proponemos un proceso para la extracción de URL de Smishing basado en técnicas de Text Spotting, complementado con una reconstrucción de URL personalizada utilizando características resaltadas en la imagen. Aplicamos la metodología propuesta a un conjunto personalizado de 244 capturas y 262 URLs, obteniendo como resultado un aumento de la precisión de reconocimiento de 3,05% a 22,90%, tras lo cual puede continuarse procesando el texto extraído en Smishing.
Palabras clave:
Detalles del artículo
Citas
Al-Qahtani, A. F., Cresci, S., 2022. The covid-19 scamdemic: A survey of phishing attacks and their countermeasures during covid-19. IET Information Security 16 (5), 324–345. DOI: https://doi.org/10.1049/ise2.12073
Baek, J., Matsui, Y., Aizawa, K., 2021. What if we only use real datasets for scene text recognition? toward scene text recognition with fewer labels. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 3113–3122. DOI: https://doi.org/10.1109/CVPR46437.2021.00313
Bautista, D., Atienza, R., 2022. Scene text recognition with permuted autoregressive sequence models. In: European conference on computer vision. Springer, pp. 178–196. DOI: https://doi.org/10.1007/978-3-031-19815-1_11
Blanco-Medina, P., Fidalgo, E., Alegre, E., Gonzalez-Castro, V., 2022. A survey on methods, datasets and implementations for scene text spotting. IET Image Processing 16 (13), 3426–3445. DOI: https://doi.org/10.1049/ipr2.12574
Church, K., De Oliveira, R., 2013. What’s up with whatsapp? comparing mobile instant messaging behaviors with traditional sms. In: Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services. pp. 352–361. DOI: https://doi.org/10.1145/2493190.2493225
Jánez-Martino, F., Alaiz-Rodríguez, R., Gonzalez-Castro, V., Fidalgo, E., Alegre, E., 2023. A review of spam email detection: analysis of spammer strategies and the dataset shift problem. Artificial Intelligence Review 56 (2), 1145–1173. DOI: https://doi.org/10.1007/s10462-022-10195-4
Joshi, A., Fidalgo, E., Alegre, E., Fernandez-Robles, L., 2023. Deepsumm: Exploiting topic models and sequence to sequence networks for extractive text summarization. Expert Systems with Applications 211, 118442. DOI: https://doi.org/10.1016/j.eswa.2022.118442
Karatzas, D., Gomez-Bigorda, L., Nicolaou, A., Ghosh, S., Bagdanov, A., Iwamura, M., Matas, J., Neumann, L., Chandrasekhar, V. R., Lu, S., et al., 2015. Icdar 2015 competition on robust reading. In: 2015 13th international conference on document analysis and recognition (ICDAR). IEEE, pp. 1156–1160. DOI: https://doi.org/10.1109/ICDAR.2015.7333942
Maneriker, P., Stokes, J. W., Lazo, E. G., Carutasu, D., Tajaddodianfar, F., Gururajan, A., 2021. Urltran: Improving phishing url detection using transformers. In: MILCOM 2021-2021 IEEE Military Communications Conference (MILCOM). IEEE, pp. 197–204. DOI: https://doi.org/10.1109/MILCOM52596.2021.9653028
Mishra, S., Soni, D., 2022. Sms phishing dataset for machine learning andpattern recognition. In: International Conference on Soft Computing and Pattern Recognition. Springer, pp. 597–604. DOI: https://doi.org/10.1007/978-3-031-27524-1_57
Rahman, M. L., Timko, D., Wali, H., Neupane, A., 2023. Users really do respond to smishing. In: Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy. pp. 49–60. DOI: https://doi.org/10.1145/3577923.3583640
Sanchez-Paniagua, M., Fern ´ andez, E. F., Alegre, E., Al-Nabki, W., González-Castro, V., 2022. Phishing url detection: A real-case scenario through login urls. IEEE Access 10, 42949–42960. DOI: https://doi.org/10.1109/ACCESS.2022.3168681
Timko, D., Rahman, M. L., 2023. Commercial anti-smishing tools and their comparative effectiveness against modern threats. In: Proceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks. pp. 1–12. DOI: https://doi.org/10.1145/3558482.3590173
Ulfath, R. E., Sarker, I. H., Chowdhury, M. J. M., Hammoudeh, M., 2022. Detecting smishing attacks using feature extraction and classification techniques. In: Proceedings of the International Conference on Big Data, IoT, and Machine Learning: BIM 2021. Springer, pp. 677–689. DOI: https://doi.org/10.1007/978-981-16-6636-0_51
Vadrevu, P., Liu, J., Li, B., Rahbarinia, B., Lee, K. H., Perdisci, R., 2017. Enabling reconstruction of attacks on users via efficient browsing snapshots. In: NDSS. DOI: https://doi.org/10.14722/ndss.2017.23100
Wang, W., Xie, E., Li, X., Liu, X., Liang, D., Yang, Z., Lu, T., Shen, C., 2021. Pan++: Towards efficient and accurate end-to-end spotting of arbitrarilyshaped text. IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (9), 5349–5367. DOI: https://doi.org/10.1109/TPAMI.2021.3077555