Landslide early warning systems (EWS) are critical to disaster preparedness but are frequently limited by uncertainty of prediction and variability in user trust. This research presents a new dual-modality method combining virtual reality (VR) simulation and electroencephalography (EEG) to evaluate behavioral and neurophysiological reactions to probabilistic landslide warnings. Eighty drivers experienced a VR driving situation with different warning accuracy (70% vs. 95%) and lighting conditions (day vs. night), collecting behavioral measures (e.g., collisions, speed, trajectory deviance) and EEG-based cognitive measures (e.g., alpha/theta, alpha/gamma, beta/gamma ratios) as dependent measures. Results revealed that decreased warning accuracy caused elevated collision rates, route deviances, and beta/gamma EEG activity, representing higher cognitive stress. Higher ratios of alpha/theta and alpha/gamma were related to performance in driving and were more prominent under higher accuracy and daylight. These results stress the promise that neuroadaptive VR systems hold to improve disaster training by dynamically calibrating feedback according to the cognitive states of users, therefore providing useful insights into the intelligent, human-oriented EWS technology design within the fields of system, man, and cybernetics.

Purpose The increased use of Information Systems (IS) as a working tool for employees increases the number of accounts and passwords required. Despite being more aware of password entropy, users still often participate in deviant password behaviors, known as “password workarounds” or “shadow security.” These deviant password behaviors can put individuals and organizations at risk, resulting in a data breach. This paper aims to engage IS users and Subject Matter Experts (SMEs), focused on designing, developing and empirically validating the Password Workaround Cybersecurity Risk Taxonomy (PaWoCyRiT) – a 2x2 taxonomy constructed by aggregated scores of perceived cybersecurity risks from Password Workarounds (PWWAs) techniques and their usage frequency. Design/methodology/approach This research study was a developmental design conducted in three phases using qualitative and quantitative methods: (1) A set of 10 PWWAs that were identified from the literature were validated by SMEs along with their perspectives on the PWWAs usage and risk for data breach; (2) A pilot study was conducted to ensure reliability and validity and identify if any measurement issues would have hindered the results and (3) The main study data collection was conducted with a large group of IS users, where also they reported on coworkers' engagement frequencies related to the PWWAs. Findings The results indicate that statistically significant differences were found between SMEs and IS users in their aggregated perceptions of risks of the PWWAs in causing a data breach, with IS users perceiving higher risks. Engagement patterns varied between the two groups, as well as factors like years of IS experience, gender and job level had statistically significant differences among groups. Practical implications The PaWoCyRiT taxonomy that the we have developed and empirically validated is a handy tool for organizational cyber risk officers. The taxonomy provides organizations with a quantifiable means to assess and ultimately mitigate cybersecurity risks. Social implications Passwords have been used for a long time to grant controlled access to classified spaces, electronics, networks and more. However, the dramatic increase in user accounts over the past few decades has exposed the realization that technological measures alone cannot ensure a high level of IS security; this leaves the end-users holding a critical role in protecting their organization and personal information. Thus, the taxonomy that the authors have developed and empirically validated provides broader implications for society, as it assists organizations in all industries with the ability to mitigate the risks of data breaches that can result from PWWAs. Originality/value The taxonomy the we have developed and validated, the PaWoCyRiT, provides organizations with insights into password-related risks and behaviors that may lead to data breaches.

Although there have been numerous technological advancements in the last several years, there continues to be a real threat as it pertains to social engineering, especially phishing, spear-phishing, and Business Email Compromise (BEC). While the technologies to protect corporate employees and network borders have gotten better, there are still human elements to consider. No technology can protect an organization completely, so it is imperative that end users are provided with the most up-to-date and relevant Security Education, Training, and Awareness (SETA). Phishing, spear-phishing, and BEC are three primary vehicles used by attackers to infiltrate corporate networks and manipulate end users into providing them with valuable company information. Many times, this information can be used to hack the network for ransom or impersonate employees so that the attacker can steal money from the company. Analysis of successful attacks show not only a lack of technology adoption by many organizations, but also the end user's susceptibility to attacks. One of the primary mediums in which attackers enjoy success is through business email. This dissertation study was aimed at researching several phishing mitigation methods, including phishing training and campaign methods, as well as any human characteristics which create a successful cyberattack through business email. Phase 1 of this study validated the approach and measures through 27 cybersecurity experts’ opinions. Phase 2 was a pilot study that produced a procedure for data collection and analysis and gathered 172 data points across three groups containing 86 users. Phase 3, the main study, used the established data approach and gathered 1,104 data points across three groups containing 552 users. The results of the experiments were analyzed using Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) to address the research questions. Several significant findings are documented, including results that showed there were no statistical differences in phishing training methods. This study indicates that current training methods, such as annual awareness or continuous customized training appear to provide little to no added value compared to no training at all. In addition, this study indicates that phishing campaign methods have a significant impact on phishing success, specifically a Red Team campaign. Lastly, recommendations for future research and opinions for industry stakeholders on ways to strengthen their cybersecurity posture are provided.

Emotional stability, awareness, and attention may likely be enhanced by meditation and related techniques. Since meditation practitioners may need focus and engagement, virtual reality (VR) may be helpful. Even though there has been some research on the usefulness of VR for meditation, very few studies have looked at the effectiveness of VR on audible mantra repetition (AuMR). Our research addresses this limitation by investigating the efficacy of AuMR, which is assigned to promote better cognitive health and overall brain well-being in VR. Fortyone individuals were randomly divided into two groups, test and control. The test group was engaged in a ten-minute VR-based AuMR session, while the control group did nothing in the same virtual reality setting for ten minutes. Both groups completed self-reported questionnaires before and after the intervention and electroencephalography (EEG) and heart rate variability (HRV) measurements. We evaluated EEG band power ratios such as alpha-to-beta (AB) ratio and frontal-alpha-to-temporal-theta (FATT) ratio to find the effects of VR-aided meditation. The findings of the ANOVA test demonstrated a substantial decrease in the self-reported stress, anxiety, and depression parameters. Furthermore, comparing the test group to the control group revealed a significant increase in the FATT ratio and a significant decrease in the AB ratio. We also observed significant changes in the HRV values of the test group. The study offers sufficient evidence to suggest the feasibility of AuMR in VR for cognitive wellness.

There is growing interest in developing virtual reality (VR) applications for mental health therapies. However, the investigation of the effectiveness of meditation in VR environments for mental health issues like stress remains mostly unexplored. This study seeks to fill this knowledge gap by investigating the influence of VR-guided Zen meditation (VRZM) on stress levels. 40 individuals were randomly divided into two between-subjects groups: one engaged in VRZM (N = 20), while the other received just a VR immersive environment without the Zen meditation's audio (VR; N = 20). The study explored the impact of VRZM on stress via EEG patterns and the Depression Anxiety Stress Scale - 21 (DASS - 21). The results indicated significantly reduced depression, anxiety, and stress levels in the VRZM group but not in the VR group. Moreover, VRZM induced a pronounced increase in the frontal alpha-to-temporal theta ratio, indicating enhanced relaxation, contrasting with no significant change in the VR group. The results suggested the effectiveness of VRZM meditation in promoting calmness and its potential efficacy in mental health interventions. We highlight the implications of VRZM for alleviating mental health problems like stress.

Android is a platform that hosts roughly 99% of known mobile malware to date and is thus the focus of much research efforts in mobile malware detection. One of the main tools used in this effort is supervised machine learning. While a decade of work has made a lot of progress in detection accuracy, there is an obstacle that each stream of research is forced to overcome, feature selection, i.e., determining which attributes of Android are most effective as inputs into machine learning models. This research tackles the feature selection problem by providing the community with an exhaustive analysis of the three primary types of Android features used by researchers: Permissions, Intents and API Calls. We applied a wide spectrum of feature selection techniques including eleven different algorithms which consisted of filter methods, wrapper methods and embedded methods. Results were evaluated with three different supervised learning classifiers, Random Forest, Support Vector Machine and Neural Network, on a dataset with over 119K Android apps and over 400 features. The results showed that using a combination of Permissions, Intents and API Calls produced higher accuracy than using any of those alone or in any other combination. The results also showed that feature selection should be performed on the combined dataset, not by feature type and then combined and that the negative effects of not doing so are more pronounced the larger the feature set.

The Terra and Aqua satellites are the flagships of the NASA Earth Observing System and carry suites of sensors designed to provide measurements of the climate system suitable for many research applications. Each satellite carries a Moderate-Resolution Imaging Spectroradiometer (MODIS), which are very complex imaging radiometers operating in both the visible and infrared parts of the electromagnetic spectrum. One of the primary variables that is derived from some of the infrared measurements of MODIS is sea-surface temperature (SST). There are two spectral intervals located where the atmosphere is relatively transparent, at about 4 and 11 micrometer wavelengths, where SST measurements can be made in cloud-free conditions, although the contamination of the shorter wavelength measurements by reflected sunlight limits these to the night-time part of each orbit. The atmospheric correction algorithms used to derive SSTs are described, along with radiometric and sub-surface measurements used to determine the error characteristics of the retrieved fields