Next Article in Journal
Building-in-Briefcase: A Rapidly-Deployable Environmental Sensor Suite for the Smart Building
Next Article in Special Issue
Using Stigmergy to Distinguish Event-Specific Topics in Social Discussions
Previous Article in Journal
Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches
Open AccessArticle

Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using 1 Regularization

1
Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico
2
Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1380; https://doi.org/10.3390/s18051380
Received: 29 March 2018 / Revised: 20 April 2018 / Accepted: 26 April 2018 / Published: 29 April 2018
(This article belongs to the Special Issue Social Sensing)
In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on 1 regularization. View Full-Text
Keywords: security; social sentiment sensor; hackers; social media; statistics; 1 regression; Twitter; cyber-attacks security; social sentiment sensor; hackers; social media; statistics; 1 regression; Twitter; cyber-attacks
Show Figures

Figure 1

MDPI and ACS Style

Hernandez-Suarez, A.; Sanchez-Perez, G.; Toscano-Medina, K.; Martinez-Hernandez, V.; Perez-Meana, H.; Olivares-Mercado, J.; Sanchez, V. Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using 1 Regularization. Sensors 2018, 18, 1380.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop