Exploring Individuals’ Experiences with Security Attacks: A Text Mining and Qualitative Study

Abstract

Cyber-attacks have become increasingly prevalent with the widespread integration of technology into various aspects of our lives. The surge in social media platform usage has prompted users to share their firsthand experiences with cyber-attacks. Despite this, previous literature has not extensively investigated individuals' experiences with these attacks. This study aims to comprehensively explore and analyze the content shared by cyber-attack victims in Saudi Arabia, encompassing text, video, and audio formats. The primary objective is to investigate the factors influencing victims' perceptions of the security risks associated with these attacks. Following data collection, preparation, and cleaning, Latent Dirichlet Allocation (LDA) is employed for topic modeling, shedding light on potential factors impacting victims. Sentiment analysis is then utilized to examine the nuanced negative and positive perceptions of individuals. NVivo is deployed for data inspection, facilitating the presentation of insightful inferences. Hierarchical clustering is implemented to explore distinct clusters within the textual dataset. The study's results underscore the critical importance of spreading awareness among individuals regarding the various tactics employed by cyber attackers. Doi: 10.28991/ESJ-2024-08-01-010 Full Text: PDF

Keywords

• covid-19 outbreak
• security attacks
• qualitative study
• text mining.