Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework

Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework

Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliab...

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Bibliographic Details
Main Authors: Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, Lee Giles
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Scholarly
digital
library
paper
PDF documents
malware
Online Access:https://ieeexplore.ieee.org/document/8788686/

Internet

https://ieeexplore.ieee.org/document/8788686/

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