Publications

@article{castano_phikita_2023,

title = {PhiKitA: Phishing Kit Attacks Dataset for Phishing Websites Identification},

author = {Felipe Castaño and Eduardo Fidalgo and Rocío Alaiz-Rodríguez and Enrique Alegre},

url = {https://ieeexplore.ieee.org/abstract/document/10103863},

year  = {2023},

date = {2023-01-01},

journal = {IEEE Access},

volume = {11},

pages = {40779–40789},

abstract = {This paper introduces PhiKitA, a novel dataset containing phishing kits and phishing websites generated from these kits. The dataset is used to investigate phishing kit detection, phishing website identification, and the source of phishing websites. The study applied MD5 hashes, fingerprints, and graph representation DOM algorithms to analyze the dataset. The results show that the graph representation algorithm achieved an accuracy of 92.50% for phishing detection, while MD5 hash representation achieved a 39.54% F1 score, indicating its limited effectiveness in distinguishing phishing sources.},

note = {Publisher: IEEE},

keywords = {Cybersecurity, Dataset, phishing detection},

pubstate = {published},

tppubtype = {article}

}

@article{paz_centeno_oculus-crawl_2017,

title = {Oculus-Crawl, a software tool for building datasets for computer vision tasks},

author = {Iván de Paz-Centeno and Eduardo Fidalgo and Enrique Alegre and Wesam Al-Nabki},

url = {https://ruc.udc.es/dspace/handle/2183/25870},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {XXXVIII Jornadas de Automática},

pages = {991–998},

abstract = {This paper introduces Oculus-Crawl, a modular and scalable tool designed to automatically collect images from Google and Yahoo Images search engines. The tool enables efficient dataset creation for computer vision tasks, featuring capabilities for storing and sharing large datasets along with their metadata. Testing demonstrated its efficiency by successfully collecting over 11,500 images with their metadata in under 14 minutes.},

note = {Publisher: Servicio de Publicaciones de la Universidad de Oviedo},

keywords = {Computer vision, crawler, Dataset, images, search engine},

pubstate = {published},

tppubtype = {article}

}