Publications

@article{castano_automating_2024,

title = {Automating cybersecurity TTP classification based on nnstructured attack descriptions},

author = {Felipe Castano and Amaia Gil Lerchundi and Raúl Orduna Urrutia and Eduardo Fidalgo and Rocío Alaiz Rodríguez},

url = {https://idus.us.es/items/1566b428-106f-4ace-8d17-0835566c60bf},

year  = {2024},

date = {2024-01-01},

journal = {Jornadas Nacionales de Investigación en Ciberseguridad (JNIC)(9ª. 2024. Sevilla)(2024), pp. 46-50.},

abstract = {This paper introduces WAVE-27K, a large dataset of unstructured CTI descriptions covering 27 MITRE techniques and 7 tactics. It contains 22,539 single-technique samples and 5,262 multi-technique samples, making it the largest dataset in its category. A BERT-based model trained on WAVE-27K achieved a 97% micro F1-score, demonstrating its quality for machine learning applications in cybersecurity.},

note = {Publisher: Universidad de Sevilla. Escuela Técnica Superior de Ingeniería Informática},

keywords = {BERT, CTI, cyber threat intelligence, machile learning, SOC operations},

pubstate = {published},

tppubtype = {article}

}

@article{garcia_ordas_azibo_2014,

title = {aZIBO: a new descriptor based in shape moments and rotational invariant features},

author = {Marïa Teresa Garcïa Ordás and Enrique Alegre and Víctor González Castro and Diego Garcïa Ordás},

url = {https://ieeexplore.ieee.org/abstract/document/6977127},

year  = {2014},

date = {2014-01-01},

journal = {2014 22nd International Conference on Pattern Recognition},

pages = {2395–2400},

abstract = {This work introduces a new shape descriptor called ZIBO (absolute Zernike moments with Invariant Boundary Orientation), combining global Zernike moments and a rotationally invariant version of the Edge Gradient Co-occurrence Matrix (EGCM). The descriptors were applied to three datasets (Kimia99, MPEG2, MPEG7) and evaluated using kNN with City block and Chi-square distance metrics. The combination of global and local descriptors achieved better results than the baseline ZMEG method. Specifically, the ZIBO descriptor obtained success rates of 78.29% on MPEG7 and 81.00% on MPEG2, outperforming ZMEG by 2.43% and 3.75%, respectively.},

note = {Publisher: IEEE},

keywords = {EGCM, Image classification, machile learning, shape descriptors, zernike moments},

pubstate = {published},

tppubtype = {article}

}