Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 29 March 2018 / Approved: 29 March 2018 / Online: 29 March 2018 (07:47:48 CEST)

How to cite: 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 ℓ1 Regularization. Preprints 2018, 2018030247 (doi: 10.20944/preprints201803.0247.v1). 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 ℓ1 Regularization. Preprints 2018, 2018030247 (doi: 10.20944/preprints201803.0247.v1).

Abstract

In recent years, online social media information has been 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.

Subject Areas

security; social sentiment sensor; hackers; social media; statistics; L1 regression; twitter; cyber attacks

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