Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization

Sensors

Victor Sanchez¹  Gabriel Sanchez-Perez²  Karina Toscano-Medina²  Jesus Olivares-Mercado²  Aldo Hernandez-Suarez²  Hector Perez-Meana²  Victor Martinez-Hernandez² 

[1] Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK;Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico;

关键词: security;    social sentiment sensor;    hackers;    social media;    statistics;    ℓ1 regression;    Twitter;    cyber-attacks;   

DOI  :  10.3390/s18051380

来源: DOAJ

【 摘 要 】

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 onℓ 1regularization.

【 授权许可】

Unknown