Context: Protection motivation theory (PMT) is the most frequently used theory in understanding cyber security behaviors. However, most studies have used a cross-sectional design with symmetrical analysis techniques such as structure equation modeling (SEM) and regression. A data-driven approach, such as predictive modeling, is lacking and can potentially evaluate and validate the predictive power of PMT for cybersecurity behaviors. Objective: The objective of this study is to assess the explanatory and predictive power of PMT for cyber security behaviors related to computers and smartphone. Method: An online survey was employed to collect data from 1027 participants. The relationship of security behaviors with threat appraisal (severity and vulnerability) and coping appraisal (response efficacy, self-efficacy and response cost) components were tested via explanatory and predictive modeling. Explanatory modeling was employed via SEM, whereas three machine learning algorithms, namely Decision Tree (DT), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) were used for predictive modeling. Wrapper feature selection was employed to understand the most important factors of PMT in predictive modeling. Results: The results revealed that the threat severity from the threat appraisal component of PMT significantly influenced computer security and smartphone security behaviors. From the coping appraisal, response efficacy and self-efficacy significantly influenced computer and smartphone security behaviors. The ML analysis showed that the highest predictive power of PMT for computer security was 76 % and for smartphone security 68 % by KNN algorithm. The wrapper feature selection approach revealed that the most important features in predicting security behaviors are self-efficacy, response efficacy and intention to secure devices. Thus, the findings indicate the complementarity of the cross-sectional and data driven methods.

Phishing attacks are among the most prevalent attack mechanisms employed by attackers. The consequences of successful phishing include (and are not limited to) financial losses, impact on reputation, and identity theft. The paper presents a systematic literature review featuring 248 articles (from the beginning of 2018 until March 2023) across the main digital libraries to identify, (1) the existing mitigation strategies against phishing attacks, and the underlying technologies considered in the development of these strategies; (2) the most considered phishing vectors in the development of the mitigation strategies; (3) anti-phishing guidelines and recommendations for organizations and end-users respectively; and (4) gaps and open issues that exist in the state of the art. The paper advocates for the need to consider the abilities of human users during the design and development of the mitigation strategies as only technology-centric solutions will not suffice to cater to the challenges posed by phishing attacks.

Individuals seek information for informed decision-making, and they consult a variety of information sources nowadays. However, studies show that information from multiple sources can lead to information overload, which then creates negative psychological and behavioral responses. Drawing on the Stimulus-Organism-Response (S-O-R) framework, we propose a model to understand the effect of information seeking, information sources, and information overload (Stimuli) on information anxiety (psychological organism), and consequent behavioral response, information avoidance during the global health crisis (COVID-19). The proposed model was tested using partial least square structural equation modeling (PLS-SEM) for which data were collected from 321 Finnish adults using an online survey. People found to seek information from traditional sources such as mass media, print media, and online sources such as official websites and websites of newspapers and forums. Social media and personal networks were not the preferred sources. On the other hand, among different information sources, social media exposure has a significant relationship with information overload as well as information anxiety. Besides, information overload also predicted information anxiety, which further resulted in information avoidance.

Designing a cybersecurity course for a big cohort of students from the different educational background is a challenging job. Examined in this study are the perceptions, preferences and performance of students who have participated in a strategic blended learning initiative aimed at preparing students for their working lives. For this purpose, both self-reported and observational data were collected from 115 students who voluntarily registered for the pilot run of the course. Self-reported data was used to measure students’ preferences as well as perceptions related to satisfaction, engagement, convenience, interaction and views on learning. Observational data measuring students’ performance was directly extracted from the collaborative learning platform on which the course was hosted. The results show that overall students liked the blended design of the course. They were satisfied with the format of the course, they felt engaged, and most of them secured good grades. Moreover, no significant difference in perceptions and preferences were found when controlled for gender, educational discipline, and overall performance, showing that the blended design of the course was accepted across the board.

The on-going AI revolution has disrupted several industry sectors and will keep having an unprecedented impact on all areas of society. This is predicted to force a major proportion of the workforce to re-educate itself during the next few decades. Consequently, this has led to a growing demand for multidisciplinary AI education also for students outside computer science. Therefore, a 25 credit (ECTS) cross-disciplinary study module on AI, targeting students in all faculties, was designed. We present findings from the design and implementation of the study module as well as students' initial perceptions towards AI at the beginning of the study module. Enrollment for the first implementation of the study module began in autumn 2019. The student distribution (N=144) between faculties was the following: natural sciences (n=37), social sciences (n=23), law (n=17), education (n=17), economics (n=16), medicine (n=10), humanities (n=10) and open university (n=14). Based on a survey distributed to students (N=34), the primary reason for enrolling to study AI was interest towards the subject, followed by the need of AI skills at work and relevance of AI in society.

The use of online educational systems called E-learning has improved both teaching and learning. While researchers have examined several factors that affect the adoption and acceptance of E-learning among the students, the role of perceived security has not yet been examined. Using the Technology Acceptance Model (TAM) as the base, this paper investigates the impact of perceived security on E-learning acceptance among university students. Using a cross-sectional design, data were collected from 313 university students using an online survey. The analysis with SmartPLS v2.0 confirms that perceived security positively affects intention to use E-learning through the mediator (perceived usefulness). Further, a positive impact of perceived security was also found on perceived usefulness and perceived ease of use. In the end, we have given recommendations for the stakeholders-university, faculty, and students.

Machine learning (ML) techniques are changing both the offensive and defensive aspects of cybersecurity. The implications are especially strong for privacy, as ML approaches provide unprecedented opportunities to make use of collected data. Thus, education on cybersecurity and AI is needed. To investigate how AI and cybersecurity should be taught together, we look at previous studies on cybersecurity MOOCs by conducting a systematic literature review. The initial search resulted in 72 items and after screening for only peer-reviewed publications on cybersecurity online courses, 15 studies remained. Three of the studies concerned multiple cybersecurity MOOCs whereas 12 focused on individual courses. The number of published work evaluating specific cybersecurity MOOCs was found to be small compared to all available cybersecurity MOOCs. Analysis of the studies revealed that cybersecurity education is, in almost all cases, organised based on the topic instead of used tools, making it difficult for learners to find focused information on AI applications in cybersecurity. Furthermore, there is a gab in academic literature on how AI applications in cybersecurity should be taught in online courses.

The purpose of this study is to examine genderwise differences in information security awareness (ISA) among university students. 614 usable responses were collected using survey from the students of eight different disciplines in a university. ISA is considered as combination of knowledge and behavior, and called computed ISA. Knowledge and behavior is assessed using vocabulary test and scenario based questions, whereas perceived ISA (PISA) is measured on 5-point Liker type question. The application of t-test and Cohen's d values show that both the genders differ significantly from each other in their Knowledge, Behavior, computed ISA and PISA. In comparison to female students, male students are found better in terms of aforementioned variables. The study also shows that unlike male students, female students prefer to utilize their social circle to accumulate information security knowledge. Moreover, the male students prefer to learn security related issues by self-exploration, whereas, the female students prefer formal educational methods for similar learning.