Automated detection of unstructured context-dependent sensitive information using deep learning

Par Conseil national de recherches du Canada

DOI	Trouver le DOI : https://doi.org/10.1016/j.iot.2021.100444
Auteur	Rechercher : Ahmed, Hadeer Identifiant ORCID : https://orcid.org/0000-0001-9568-1563; Rechercher : Traore, Issa; Rechercher : Saad, Sherif; Rechercher : Mamun, Mohammad¹
Affiliation	Conseil national de recherches du Canada. Technologies numériques
Format	Texte, Article
Sujet	machine learning; deep learning; NLP; data leak prevention; unstructured data; sensitive data
Résumé	With the rapid growth of cloud computing and remote workforces, organizations are increasingly aware of the risk of data leaks and data exfiltration. Handling such risk has become more challenging because organizations today usually deal with big data. In particular, organizations must deal with massive amounts of unstructured data. As a result, modern data leak prevention (DLP) solutions must support automated methods to detect and identify confidential and sensitive information in both structured and unstructured data. In this paper, we demonstrate the benefits of using deep learning to identify unstructured context-dependent sensitive information, in contrast to traditional machine learning methods and rule-based methods.
Date de publication	2021-08-14
Maison d’édition	Elsevier
Dans	Internet of Things 16, 100444 (décembre 2021).
Langue	anglais
Publications évaluées par des pairs	Oui
Exporter la notice	Exporter en format RIS
Signaler une correction	Signaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement	80a99e7b-7412-4657-a1fa-f6d3ef70df3f
Enregistrement créé	2021-09-10
Enregistrement modifié	2021-09-14

Date de modification :: 2024-07-02