Predicting individuals’ vulnerability to social engineering in social networks

Abstract

The popularity of social networking sites has attracted billions of users to engage and share their information on these networks. The vast amount of circulating data and information expose these networks to several security risks. Social engineering is one of the most common types of threat that may face social network users. Training and increasing users' awareness of such threats is essential for maintaining continuous and safe use of social networking services. Identifying the most vulnerable users in order to target them for these training programs is desirable for increasing the effectiveness of such programs. Few studies have investigated the effect of individuals' characteristics on predicting their vulnerability to social engineering in the context of social networks. To address this gap, the present study developed a novel model to predict user vulnerability based on several perspectives of user characteristics. The proposed model includes interactions between different social network-oriented factors such as level of involvement in the network, motivation to use the network, and competence in dealing with threats on the network. The results of this research indicate that most of the considered user characteristics are factors that influence user vulnerability either directly or indirectly. Furthermore, the present study provides evidence that individuals' characteristics can identify vulnerable users so that these risks can be considered when designing training and awareness programs.

Social Engineering Attacks Prevention: A Systematic Literature Review

“…Research on behavioral against social engineering attacks has been studied thoroughly by [14], [16]. They proposed a user-centric framework [14] and a prediction approach of an individual's vulnerability [16].…”

“…Social engineering attacks prevention methods are health campaign strategies, health campaign tactics, television advertisements, informational pamphlets, social media [10], ethics of social engineering penetration testing [11], a human as a security sensor framework [12], a personality information processing model [13], characteristic user framework [14], Game-based analysis [15], and predicting individuals' vulnerability [16], computer security policy [17], cyber security practices [18].…”

A Study on the Social Networking Sites Security Awareness Among Users in Tabuk City

“…Most respondents were also aware of the privacy features of social networking sites, irrespective of gender (11) . Female users are more aware than male users (11) , but they are more susceptible to attacks (12) Table 8 shows the computed multivariate test on the responses of the respondents as to age. As shown on the table under Wilks' Lambda, it revealed that the value is 0.865 with an F-value of 1.298 and a probability value of 0.099 since the p-value is more significant than the 0.05 margin of error; hence, the null hypothesis is accepted.…”

“…Hence, the null hypothesis is rejected, resulting in a significant relationship on social networking security awareness when grouped according to educational attainment. According to Albladi & Weir, the educational level has no substantial effect on users' vulnerability, as shown by the regression study of the susceptibility to attacks (12) . Table 10 the computed multivariate test on the respondents' responses to using social media.…”

Measuring Awareness of Social Engineering in the Educational Sector in the Kingdom of Saudi Arabia

“…In the last decades, users have interacted with many platforms on the Internet, which lead to them being attacked by hackers using social engineering attacks [13] and their data being shared on the Internet [14]. Some studies indicate that social engineering relies on human nature and vulnerabilities to hack into organizational systems.…”