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

Abstract

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 1 regularization.

Social Media Data Mining for Proactive Cyber Defense

“…This is reinforced by Ref. [16], who find a correlation between negative social media sentiment and cyberattacks. It is possible for positive tweets to contain information about a vulnerability but positive tweets that contain security terms are more likely to be used in a promotional sense such as advertising an article, product or service which isn't as pertinent to an analyst.…”

“…In Hernandez-Suarez et al [16] they aim to predict cyberattacks using a social sentiment sensor in Twitter. That is done by scraping tweets and putting them through a machine learning model which classifies their sentiment as positive, negative or security.…”

“…We first filter tweets based on user accounts. In Hernandez-Suarez et al [16] well identified Twitter accounts related to Hacktivists, security researchers and enthusiasts are identified. For our account filtering, we expand upon this list by adding more recent and pertinent accounts.…”

Ontology-based Model for Security Assessment: Predicting Cyberattacks Through Threat Activity Analysis

“…The definition of log comprises not only information about the actions of the protected infrastructure, but also the analysis of social networks [11][10], like Twitter [9]; Dark Web [12], etc.…”

A Worldwide Analysis of Cyber Security And Cyber Crime using Twitter

“…In paper [3], the author recommends that the traditional solutions along with the use of analytic models, A Worldwide Analysis of Cyber Security And Cyber Crime using Twitter Kartikay Sharma, Siddharth Bhasin, Piyush Bharadwaj machine learning and big data could be improved by providing relevant awareness to control or limit consequences of threats. In paper [4], a methodology for tracking social data that can trigger cyber-attacks is developed. Their main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.…”