Forest or trees? Evidence for global processing via an information board study into cues utilised in phishing identification

Anxiety alters how we perceive the world and can alter aspects of cognitive performance. Prominent theories of anxiety suggest that the effect of anxiety on cognition is due to anxious thoughts “overloading” limited cognitive resources, competing with other processes. If this is so, then a cognitive load manipulation should impact performance of a task in the same way as induced anxiety. Thus, we examined the impact of a load manipulation on a time perception task that we have previously shown to be reliably impacted by anxiety. In contrast with our prediction, across 3 studies we found that time perception was insensitive to our load manipulation. Our results do not therefore support the idea that anxiety impacts temporal cognition by overloading limited cognitive resources, at least as induced by a commonly used load manipulation. Thus, anxiety might affect temporal cognition in a unique way, via an evolutionary-preserved defense survival system, as suggested by animal-inspired theories of anxiety, rather than competing for limited attentional resources.

Every electronic message poses some threat of being a phishing attack. If recipients underestimate that threat, they expose themselves, and those connected to them, to identity theft, ransom, malware, or worse. If recipients overestimate that threat, then they incur needless costs, perhaps reducing their willingness and ability to respond over time. In two experiments, we examined the appropriateness of individuals’ confidence in their judgments of whether email messages were legitimate or phishing, using calibration and resolution as metacognition metrics. Both experiments found that participants had reasonable calibration but poor resolution, reflecting a weak correlation between their confidence and knowledge. These patterns differed for legitimate and phishing emails, with participants being better calibrated for legitimate emails, except when expressing complete confidence in their judgments, but consistently overconfident for phishing emails. The second experiment compared performance on the laboratory task with individuals’ actual vulnerability, and found that participants with better resolution were less likely to have malicious files on their home computers. That comparison raised general questions about the design of anti-phishing training and of providing feedback essential to self-regulated learning.

Research problem: Phishing is an email-based scam where a perpetrator camouflages emails to appear as a legitimate request for personal and sensitive information. Research question: How do individuals process a phishing email, and determine whether to respond to it? Specifically, this study examines how users' attention to “visual triggers” and “phishing deception indicators” influence their decision-making processes and consequently their decisions. Literature review: This paper draws upon the theory of deception and the literature on mediated cognition and learning, including the critical role of attention and elaboration in deception detection. From this literature, we developed a research model to suggest that overall cognitive effort expended in email processing decreases with attention to visual triggers and phishing deception indicators. The likelihood to respond to phishing emails increases with attention to visceral cues, but decreases with attention to phishing deception indicators and cognitive effort. Knowledge of email-based scams increases attention to phishing deception indicators, and directly decreases response likelihood. It also moderates the impact of attention to visceral triggers and that of phishing deception indicators on likelihood to respond. Methodology: Using a real phishing email as a stimulus, a survey of 321 members of a public university community in the Northeast US, who were intended victims of a spear phishing attack that took place, was conducted. The survey used validated measures developed in prior literature for the most part and tested results using the partial least-squares regression. Results and discussion: Our research model and hypotheses were supported by the data except that we did not find that cognitive effort significantly affects response likelihood. The implication of the study is that attention to visceral triggers, attention to phishing deception indicators, and phishing knowledge play critical roles in phishing detection. The limitations of the study were that the data were drawn from students, and the study explored one phishing attack, relied on some single-item measures, cognitive effort measure, and a one-round survey. Future research would examine the impact of a varying degree of urgency and a varying level of phishing deception indicators, and actual victims of phishing attacks.

This experiment investigated the impacts of difficult processing in the context of a health narrative by integrating two distinct literatures: cognitive load and processing fluency. Difficult processing was manipulated via an auditory cue induction of reactive cognitive control (a manipulation of cognitive load) and language difficulty (a manipulation of processing fluency). We examined whether cognitive load can impact persuasion-relevant outcomes, and similarly whether processing fluency can impact memory. Results revealed that the processing fluency manipulation impacted memory, suggesting subjective difficulty was indicative of resource availability, as well as narrative persuasion. Additionally, the processing fluency and cognitive load manipulations interacted such that when the language was difficult, the presence of auditory cues enhanced memory. Our results show that cognitive load and processing fluency interact to shape message processing and effects.

Most research supports a non-selective (or exhaustive) account of activation whereby multiple meanings of a word are initially activated (Degani and Tokowicz Quarterly Journal of Experimental Psychology, 63, 1266-1303, 2010). But what happens to the non-selected meaning of an ambiguous word (e.g., bark) and how is the decision made to select one meaning over the other? A great deal of research by Gernsbacher and colleagues (e.g., Gernsbacher and Faust 1991a) suggests that the non-selected meaning is “discarded” via active suppression. The present paper examines meaning-selection in ambiguous words using a word to elicit meaning context (rather than a sentence). Additionally, a manipulation of cognitive load (Experiment 2) was employed to examine these processes. Results support a suppression account of meaning selection. An updated conceptualization of ambiguity resolution is proposed.

Moral dumbfounding occurs when people defend a moral judgement even though they cannot provide a reason in support of this judgement. It manifests as an admission of not having reasons, or the use of unsupported declarations ("it's just wrong") or tautological reasons ("because it's incest") as justifications for a judgment. It typically occurs for harmless taboos. It is cited as evidence for intuitionist or dual-process theories of moral judgement over rationalist approaches, however the phenomenon remains poorly understood. We test a conflict in dual-processes explanation of moral dumbfounding, where moral dumbfounding is an example of conflict between a habitual response (making a judgement) and a response that results from deliberation (providing a reason for the judgement). One prediction of this explanation is that under specific manipulations (e.g., cognitive load), responses should vary in predictable ways. The dumbfounding paradigm involves three possible responses: (a) providing reasons for a judgement (System 2/deliberative); (b) accepting the counter-arguments and rating the behaviour as "not wrong" (System 1/habitual); (c) a dumbfounded response (System 1/habitual). Cognitive load manipulations have been shown to inhibit deliberative responding. We present 4 studies in which dumbfounded responding was investigated under cognitive load manipulations. We hypothesised that rates of providing reasons would be reduced under cognitive load. The identification of reasons was inhibited in Studies 1 and 3, but not in Studies 2 and 4. The results do not provide strong evidence that moral dumbfounding be explained as conflict in dual-processes. Future research should investigate this further, addressing methodological limitations identified.

Phishing is a kind of attack in which criminals use spoofed emails and fraudulent web sites to trick financial organization and customers. Criminals try to lure online users by convincing them to reveal the username, passwords, credit card number and updating account information or fill billing information. One of the main problems of phishing email detection is the unknown “zero-day” phishing attack, (we define zero-day attacks as attacks that phisher mount using hosts that do not appear in blacklists and not trained on the old data sample and it is a noise data), which increases the level of difficulty to detect phishing email. Nowadays, phishers are creating different representation techniques to create unknown “zero-day” phishing email to breach the defenses of those detectors. Our proposed is a novel framework called phishing dynamic evolving neural fuzzy framework (PDENF), which adapts the evolving connectionist system (ECoS) based on a hybrid (supervised/unsupervised) learning approach. PDENF adaptive online is enhanced by offline learning to detect dynamically the phishing email included unknown zero-day phishing e-mails before it get to user account. PDENF is suggested to work for high-speed “life-long” learning with low memory footprint and minimizes the complexity of the rule base and configuration with few number of rules creation for email classification. We expect to achieves high performance, including high level of true positive, true negative, sensitivity, precision, F-measure and overall accuracy compared with other approaches.

Phishing is networked theft in which the main motive of phishers is to steal any person’s private information, its financial details like account number, credit card details, login information, payment mode information by creating and developing a fake page or a fake web site, which look completely authentic and genuine. Nowadays email phishing has become a big threat to all, and is increasing day by day. Moreover detection of phishing emails have been considered an important research issue as phishing emails have been increasing day by day. Various techniques have been introduced and applied to deal with such a big issue. The major objective of this research paper is giving a detailed description on the classification of phishing emails using the natural language processing concepts. NLP (natural language processing) concepts have been applied for the classification of emails, along with that accuracy rate of various classifiers have been calculated. The paper is presented in four sections. An introduction about phishing its types, its history, statistics, life cycle, motivation for phishers and working of email phishing have been discussed in the first section. The second section covers various technologies of phishing- email phishing and also description of evaluation metrics. An overview of the various proposed solutions and work done by researchers in this field in form of literature review has been presented in the third section. The solution approach and the obtained results have been defined in the fourth section giving a detailed description about NLP concepts and working procedure.

Dakota State University has a unique computing environment. Every undergraduate student and professor has a school assigned tablet PC. In addition, it is the State of South Dakota's premier four year technology university. The university also hosts email, website, and website services for the state's K-12 program in addition to its own. The high use of the mobile computing environment serves as a reason why we chose it for our phishing experiment. For example, recently one staff member was able to be fooled into providing domain login and password information through a phishing email. This caused over 300,000 emails to be sent from that account and the university was therefore blacklisted from Microsoft and its associated affiliates such as Hotmail and MSN. This paper is an attempt to measure the viability of a phishing attack in a mobile computing environment as well as using an adaptation of a popular spam filtering technique to separate phishing emails from spam and phishing emails from legitimate email.

People are often overconfident about their ability to explain how everyday phenomena and artifacts work (devices, natural processes, historical events, etc.). However, the metacognitive mechanisms involved in this bias have not been fully elucidated. The aim of this study was to establish whether the ability to perform deliberate and analytic processes moderates the effect of informational cues such as the social desirability of knowledge on the Illusion of Explanatory Depth (IOED). To this purpose, the participants’ cognitive load was manipulated as they provided initial estimates of causal understanding of national historical events in the standard IOED paradigm. The results showed that neither the social desirability of specific causal knowledge nor the cognitive load manipulations had direct effects on the IOED. However, subsequent exploratory analyses indicated that high cognitive load was related to lower performance on concurrent memory tasks, which in turn was associated with a higher IOED magnitude. Higher analytical processing was also related to lower IOED. Implications for both dual-process models of metacognition and the design of task environments that help to reduce this bias are discussed.

In the present research, we investigated whether, because of differences in cognitive style, liberals and conservatives would differ in the process of categorizing individuals into a perceptually ambiguous group. In 3 studies, we examined whether conservatives were more likely than liberals to rely on gender inversion cues (e.g., feminine = gay) when categorizing male faces as gay vs. straight, and the accuracy implications of differential cue usage. In Study 1, perceivers made dichotomous sexual orientation judgments (gay-straight). We found that perceivers who reported being more liberal were less likely than perceivers who reported being more conservative to use gender inversion cues in their deliberative judgments. In addition, liberals took longer to categorize targets, suggesting that they may have been thinking more about their judgments. Consistent with a stereotype correction model of social categorization, in Study 2 we demonstrated that differences between liberals and conservatives were eliminated by a cognitive load manipulation that disrupted perceivers' abilities to engage in effortful processing. Under cognitive load, liberals failed to adjust their initial judgments and, like conservatives, consistently relied on gender inversion cues to make judgments. In Study 3, we provided more direct evidence that differences in cognitive style underlie ideological differences in judgments of sexual orientation. Specifically, liberals were less likely than conservatives to endorse stereotypes about gender inversion and sexual orientation, and this difference in stereotype endorsement was partially explained by liberals' greater need for cognition. Implications for the accuracy of ambiguous category judgments made with the use of stereotypical cues in naturalistic settings are discussed.

Maternal childhood adversity, OXTR genotype and cognitive load impact on perceptual and behavioral responses to infant crying

An organisation's security is fundamentally reliant on its people. Regardless of the sophistication of its cybersecurity infrastructure, the absence of comprehensive training and awareness can lead to vulnerabilities. Traditional phishing awareness training typically involves sending simulated phishing emails to employees, allowing organisations to monitor actions such as link clicks, email reporting, and responses. While this method offers valuable insights into employee behaviour, it often struggles to engage users effectively. This conventional approach may not create a dynamic learning environment conducive to better retention of vital security practices. Furthermore, users generally do not receive immediate feedback regarding their interactions with phishing links, leaving organisations more susceptible to social engineering attacks. This research seeks to address the issue by developing an interactive gamified phishing simulator that employs reinforcement learning (RL). The methodology for this study consists of two key components. First, a literature review was conducted to assess existing phishing awareness techniques and explore how RL can be applied effectively. This review examined the integration of RL within cybersecurity education and explored the impact of gamification on user behaviour. For the RL agent, a dataset comprising both phishing and legitimate emails was compiled. The agent was then trained to discern phishing emails from legitimate ones based on various email features. Then the agent presents users with email challenges and delivers real-time feedback on their selections. The simulator incorporates a reward and badge system that promotes active participation and ongoing learning. This approach aims to overcome the limitations traditionally associated with static phishing training by fostering continuous learning, ultimately reducing user susceptibility. The effectiveness of the proposed simulator was evaluated based on its classification accuracy of phishing and legitimate emails.

The changeable structures and variability of email attacks render current email filtering solutions useless. Consequently, the need for new techniques to harden the protection of users' security and privacy becomes a necessity. The variety of email attacks, namely spam, damages networks' infrastructure and exposes users to new attack vectors daily. Spam is unsolicited email which targets users with different types of commercial messages or advertisements. Porn-related content that contains explicit material or commercials of exploited children is a major trend in these messages as well. The waste of network bandwidth due to the numerous number of spam messages sent and the requirement of complex hardware, software, network resources, and human power are other problems associated with these attacks. Recently, security researchers have noticed an increase in malicious content delivered by these messages, which arises security concerns due to their attack potential. More seriously, phishing attacks have been on the rise for the past couple of years. Phishing is the act of sending a forged e-mail to a recipient, falsely mimicking a legitimate establishment in an attempt to scam the recipient into divulging private information such as credit card numbers or bank account passwords (James, 2005). Recently phishing attacks have become a major concern to financial institutions and law enforcement due to the heavy monetary losses involved. According to a survey by Gartner group, in 2006 approximately 3.25 million victims were spoofed by phishing attacks and in 2007 the number increased by almost 1.3 million victims. Furthermore, in 2007, monetary losses, related to phishing attacks, were estimated by $3.2 billion. All the aforementioned concerns raise the need for new detection mechanisms to subvert email attacks in their various forms. Despite the abundance of applications available for phishing detection, unlike spam classification, there are only few studies that compare machine learning techniques in predicting phishing emails (Abu-Nimeh et al., 2007). We describe a new version of Bayesian Additive Regression Trees (BART) and apply it to phishing detection. A phishing dataset is constructed from 1409 raw phishing emails and 5152 legitimate emails, where 71 features (variables) are used in classifiers' training and testing. The variables consist of both textual and structural features that are extracted from raw emails. The performance of six classifiers, on this dataset, is compared using the area under the curve (AUC) (Huang & Ling, 2005). The classifiers include Logistic Regression (LR), Classification and Regression Trees (CART), Bayesian Additive Regression Trees (BART), Support Vector Machines (SVM), Random O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

IntroductionEmergency medical workers face unique problems that are specific to their work environment because they play an effective role in happy and sad moments of life and death, accidents and disasters, and these people face a high level of tension. The purpose of this study is to investigate the impact of crisis management training on clinical decision-making and management of stress factors in the personnel of emergency medical services (EMS).MethodsIn this randomized controlled trial with parallel groups study, 64 personnel of the emergency medical center of Shahrekord University of Medical Sciences were selected in two groups of 32 people, control and intervention in 2024. The training content included how to deal with the crisis and crisis management. 32 training sessions were held in two weeks. After the necessary training, the effectiveness of the training on the personnel in line with crisis management was checked through a questionnaire and its relationship with process improvement. Clinical decision-making was investigated. Demographic characteristics, nursing stressors of Gary Taft and Anderson and clinical decision-making questionnaire were used. The data was analyzed with spss23 software.ResultsThe average age of the study subjects in the intervention and control groups was 31.86 and 30.98 years. Before the intervention, there was no significant difference in the average of clinical decision making in the two groups (p > 0.05), but after the intervention, there was a statistically significant difference (p < 0.05). Also, in the intervention group, there was a significant difference in the decision-making score before and after the intervention (p < 0.05). In the two groups’ intervention and control, the most common type of decision-making was analytical-intuitive decision-making (71.88 and 65.62%).ConclusionThe results of the present study showed that crisis management training interventions can reduce job stress and tension in prehospital emergency personnel and improve their decision-making process.