TY - JOUR
T1 - Social sentiment sensor in twitter for predicting cyber-attacks using ℓ1 regularization
AU - Hernandez-Suarez, Aldo
AU - Sanchez-Perez, Gabriel
AU - Toscano-Medina, Karina
AU - Martinez-Hernandez, Victor
AU - Perez-Meana, Hector
AU - Olivares-Mercado, Jesus
AU - Sanchez, Victor
N1 - Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2018/5
Y1 - 2018/5
N2 - 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 ℓ1 regularization.
AB - 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 ℓ1 regularization.
KW - Cyber-attacks
KW - Hackers
KW - Security
KW - Social media
KW - Social sentiment sensor
KW - Statistics
KW - Twitter
KW - ℓ regression
UR - http://www.scopus.com/inward/record.url?scp=85046145458&partnerID=8YFLogxK
U2 - 10.3390/s18051380
DO - 10.3390/s18051380
M3 - Artículo
C2 - 29710833
SN - 1424-8220
VL - 18
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 5
M1 - 1380
ER -