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Technology-facilitated abuse, so-called “tech abuse,” through phones, trackers, and other emerging innovations, has a substantial impact on the nature of intimate partner violence (IPV). The current chapter examines the risks and harms posed to IPV victims/survivors from the burgeoning Internet of Things (IoT) environment. IoT systems are understood as “smart” devices such as conventional household appliances that are connected to the internet. Interdependencies between different products together with the devices' enhanced functionalities offer opportunities for coercion and control. Across the chapter, we use the example of IoT to showcase how and why tech abuse is a socio-technological issue and requires not only human-centered (i.e., societal) but also cybersecurity (i.e., technical) responses. We apply the method of “threat modeling,” which is a process used to investigate potential cybersecurity attacks, to shift the conventional technical focus from the risks to systems toward risks to people. Through the analysis of a smart lock, we highlight insufficiently designed IoT privacy and security features and uncover how seemingly neutral design decisions can constrain, shape, and facilitate coercive and controlling behaviors.

The purpose of this paper is to understand the role of perceived threat, brand congruence, and social support on consumer coping strategies for a preventative health service.

An online survey of 570 women aged over 50 in one Australian state was conducted (users and non-users of the service). The data were analyzed using structural equation modeling.

A competing models approach reveals that threat on its own is associated with avoidance coping; however, when brand congruence is high, there is an association with active coping. Social support appears to have a buffering effect on threat and is associated positively with active coping and negatively with avoidance coping.

The study findings suggest that threat appeals should be used with caution in increasing participation in transformative preventative health services due to its double-edged sword effect (increasing both avoidance and active coping). When consumers have social support, this results in active coping and buffers avoidance coping. This research offers useful insights for social marketing and transformative service research.

The main purpose of this research is to produce the most accurate theoretical framework of the potential threat of cyberterrorism to the national security, compared to conventional terrorism. So it aims to identify the theoretical framework that best explains the threat of cyberterrorism and conventional terrorism to national security derived from empirical data, using grounded theory, and to validate the developed grounded theory statistically by quantitative data.

This paper presents the results of the quantitative study survey. It provides in the beginning basic information about the data. To purify the data, reliability and exploratory factor analysis, as well as confirmatory factor analysis (CFA), were performed. Then, structural equation modelling was utilised to test the final model of the theory and to assess the overall goodness-of-fit between the proposed model and the collected data set.

The first study, as a qualitative exploratory study, gives a rich data set that provides the foundation of the development of the second study, as a quantitative confirmatory study. In the researcher’s previous qualitative study, it provides a better theoretical understanding of the potential threat of cyber and conventional terrorism to Saudi national security. Also, it provides the development of the grounded theory of the study (Figure 1). It also has led to the development of the conceptual framework and the hypotheses for the second phase of the study (i.e. survey).

It is original study based on empirical data collected from Saudi military and security officials and experts in the critical infrastructures.

By using a new feature extraction method on the Cert data set and using a hidden Markov model (HMM) to model and analyze the behavior of users to distinguish whether the behavior is normal within a continuous period.

Feature extraction of five parts of the time series by rules and sorting in chronological order. Use the obtained features to calculate the probability parameters required by the HMM model and establish a behavior model for each user. When the user has abnormal behavior, the model will return a very low probability value to distinguish between normal and abnormal information.

Generally, HMM parameters are obtained by supervised learning and unsupervised learning, but the hidden state cannot be clearly defined. When the hidden state is determined according to the data set, the accuracy of the model will be improved.

This paper proposes a new feature extraction method and analysis mode, which determines the shape of the hidden state according to the situation of the data set, making subsequent HMM modeling simple and efficient and in turn improving the accuracy of user behavior detection.

Frontline service employees (FSEs) face high demands of emotional labor when dealing with difficult, and sometimes even uncivil, customer behavior while attempting to deliver service with a smile. The purpose of this study is to investigate whether employees reciprocate uncivil customer behavior. The authors investigate two potential processes – ego threat and perceived interactional justice – and further address boundary conditions of this effect.

The data for this paper were collected in three studies: one field experiment and two online experiments using adult samples. Hypotheses were tested and data was analyzed using ANOVA and regression-based modeling approaches.

Findings from a field-experimental study and online experiments show that FSEs offer lower service levels to uncivil customers. The authors further find that this effect is mediated by a perceived ego threat and that employees’ regulation of emotion (ROE), as part of their emotional intelligence, attenuates the effect of perceived ego threats on service levels.

This study finds that perceived ego threat (but not perceived interactional justice) explains why employees respond negatively to uncivil customer behavior. Therefore, it offers an emotion-driven explanation of retaliatory behavior in frontline service contexts. Implications for theories focusing on service value co-destruction and customer incivility are discussed.

The findings from this research show that ROE attenuates the impact of perceived ego threat on employee retaliatory behavior. Managerial implications include developing and training employees on emotion regulation. Furthermore, managers should identify alternative ways for restoring an employee’s ego after the employee experiences uncivil customer behavior.

The authors propose and test two processes that can explain why employees reciprocate uncivil customer behavior to gain a deeper understanding of which processes, or a combination of the two, drive employee responses. Furthermore, the authors shed insights into boundary conditions and explore when employees are less likely to react to uncivil customer behavior while experiencing ego threat.

Within critical-infrastructure industries, bow-tie analysis is an established way of eliciting requirements for safety and reliability concerns. Because of the ever-increasing digitalisation and coupling between the cyber and physical world, security has become an additional concern in these industries. The purpose of this paper is to evaluate how well bow-tie analysis performs in the context of security, and the study’s hypothesis is that the bow-tie notation has a suitable expressiveness for security and safety.

This study uses a formal, controlled quasi-experiment on two sample populations – security experts and security graduate students – working on the same case. As a basis for comparison, the authors used a similar experiment with misuse case analysis, a well-known technique for graphical security modelling.

The results show that the collective group of graduate students, inexperienced in security modelling, perform similarly as security experts in a well-defined scope and familiar target system/situation. The students showed great creativity, covering most of the same threats and consequences as the experts identified and discovering additional ones. One notable difference was that these naïve professionals tend to focus on preventive barriers, leading to requirements for risk mitigation or avoidance, while experienced professionals seem to balance this more with reactive barriers and requirements for incident management.

Our results are useful in areas where we need to evaluate safety and security concerns together, especially for domains that have experience in health, safety and environmental hazards, but now need to expand this with cybersecurity as well.

The purpose of this study is to increase awareness of current supply chain (SC) security-related issues by providing an extensive analysis of existing SC security solutions and their limitations. The security of SCs has received increasing attention from researchers, due to the emerging risks associated with their distributed nature. The increase in risk in SCs comes from threats that are inherently similar regardless of the type of SC, thus, requiring similar defence mechanisms. Being able to identify the types of threats will help developers to build effective defences.

In this work, we provide an analysis of the threats, possible attacks and traceability solutions for SCs, and highlight outstanding problems. Through a comprehensive literature review (2015–2021), we analysed various SC security solutions, focussing on tracking solutions. In particular, we focus on three types of SCs: digital, food and pharmaceutical that are considered prime targets for cyberattacks. We introduce a systematic categorization of threats and discuss emerging solutions for prevention and mitigation.

Our study shows that the current traceability solutions for SC systems do not offer a broadened security analysis and fail to provide extensive protection against cyberattacks. Furthermore, global SCs face common challenges, as there are still unresolved issues, especially those related to the increasing SC complexity and interconnectivity, where cyberattacks are spread across suppliers.

This is the first time that a systematic categorization of general threats for SC is made based on an existing threat model for hardware SC.

An increase in the number of disruptive and violent events on college and university campuses instigated this review of the methods used to interrupt the trend, with the goal of identifying a preliminary model for systematic management of such threats. The intent is to instigate research, review and discussion in order to decrease the number and severity of threatening incidents on college campuses.

Thorough review of plans from primary and secondary education, plans in use in higher education, literature on risk and threat assessment, literature on “whistle blowers”, and of violent events on college campuses was used to construct a model.

It was found that, in terms of managing and reducing threats to people who study, live and work in post‐secondary educational institutions, insufficient attention has been given to the unique needs of this setting and therefore efforts to mitigate threats have been insufficient. The investigation resulted in the development of a model of assessment and management of threats on university and college campuses.

College campus threat assessment research is very much in its infancy and will certainly develop over time. This paper is the first step in an effort to develop and ultimately test the plausibility of a model. Future research should be pursued to determinewhether the model holds up under a majority of situations on college campuses. Those involved in threat mitigation in university settings should be queried to determine their agreement with the proposed framework and for assistance in refining it.

This paper presents suggestions for the systematic management of threats and mitigation in university settings.

Phishing is one of the major threats affecting businesses worldwide in current times. Organizations and customers face the hazards arising out of phishing attacks because of anonymous access to vulnerable details. Such attacks often result in substantial financial losses. Thus, there is a need for effective intrusion detection techniques to identify and possibly nullify the effects of phishing. Classifying phishing and non-phishing web content is a critical task in information security protocols, and full-proof mechanisms have yet to be implemented in practice. The purpose of the current study is to present an ensemble machine learning model for classifying phishing websites.

A publicly available data set comprising 10,068 instances of phishing and legitimate websites was used to build the classifier model. Feature extraction was performed by deploying a group of methods, and relevant features extracted were used for building the model. A twofold ensemble learner was developed by integrating results from random forest (RF) classifier, fed into a feedforward neural network (NN). Performance of the ensemble classifier was validated using k-fold cross-validation. The twofold ensemble learner was implemented as a user-friendly, interactive decision support system for classifying websites as phishing or legitimate ones.

Experimental simulations were performed to access and compare the performance of the ensemble classifiers. The statistical tests estimated that RF_NN model gave superior performance with an accuracy of 93.41 per cent and minimal mean squared error of 0.000026.

The research data set used in this study is publically available and easy to analyze. Comparative analysis with other real-time data sets of recent origin must be performed to ensure generalization of the model against various security breaches. Different variants of phishing threats must be detected rather than focusing particularly toward phishing website detection.

The twofold ensemble model is not applied for classification of phishing websites in any previous studies as per the knowledge of authors.