Phishing, a prevalent cyber threat leveraging social engineering, poses significant challenges in the digital landscape. Despite advancements in security technologies, phishing continues to exploit human vulnerabilities, underscoring the need to understand how individuals detect such attacks. Existing research often assumes phishing detection occurs in isolation, overlooking real-world multitasking contexts where competing cognitive demands can hinder detection. This study fills this gap by examining phishing detection in the multitasking context and theorizing the relevant cognitive mechanisms and phishing-specific factors that influence phishing detection performance. Drawing on the memory-for-goals theory, we investigate the effects of working memory load (WML) from the primary task, goal activation (GA) towards phishing detection on performance, and message framing of phishing attacks. Findings from two online experiments reveal that increased WML impairs detection accuracy, while GA improves performance and reduces the negative impact of WML; furthermore, GA plays a more significant role in gain-framed phishing emails compared to loss-framed ones. Our research shifts the focus from message characteristics to the influence of multitasking on phishing detection. The results highlight the need for context-aware interventions that better align with real-world user behaviour, which provides a foundation for designing more effective phishing defences in multitasking digital environments.

Phishing attacks, often disguised as legitimate communications, exploit human psychology to compromise security. Research on phishing susceptibility suggests that the way phishing messages are framed acts as an external motivational cue, enticing individuals to click on malicious links. However, these studies often overlook the role of individuals’ internal motivational orientation and its interplay with cognitive constraints and message design. The paper investigates how a promotion vs. prevention focus interacts with message framing (gain vs. loss), working memory load (WML), and need for cognition (NFC) to shape phishing detection. Across two experiments, we find that prevention-focused individuals exhibit higher detection accuracy for phishing emails but lower detection accuracy for legitimate emails. For phishing emails, this motivational gap is more pronounced under conditions of low WML or among low-NFC individuals, particularly when emails include gain-framed content. For legitimate emails, the gap is especially salient under low WML and when evaluating messages with gain-framed content. Our findings advance phishing research by demonstrating the joint influence of motivation, cognition, and message design, and offer practical insights for personalized cybersecurity training that adapts to users’ cognitive and motivational profiles to reduce cyber risks.

Real-time wearable arrhythmia monitoring is an important means to avoid sudden cardiac death and treat cardiovascular disease effectively. However, because of the limited computing resources and power consumption of wearable devices, it is difficult for existing on-chip arrhythmia monitoring processors to achieve the good generalization performance required by inter-patient applications. Therefore, this paper proposes several innovative methods to balance the trade-off between algorithm generalization performance and limited resource and power consumption. Using multi-level event-driven architecture, the standby power consumption of the processor is reduced through event wake-up. The abnormal heartbeat detection algorithm uses reconstructed multi-cycle heartbeat segments as the classification object and highlights the correlation information between heartbeats to improve generalization performance. From the bioelectrical signal mechanism, a set of morphological and rhythmic features rich in electrocardiogram pathological information is proposed to minimize the scale of the classification neural network while ensuring the accuracy of the arrhythmia classification algorithm. The processor is implemented using FPGA and TSMC65lp CMOS process. The measurement results demonstrated that the power consumption is only 810 nW in abnormal detection mode, with an algorithmically abnormal segment sensitivity of 96.77%, and 900 nW in arrhythmia classification mode, with an algorithmically inter-patient accuracy of 93.3

This study investigates the synergistic impacts of online and offline social participation on older adults’ subjective well-being outcomes. By drawing on the socio-emotional selectivity perspective, we conduct a cohort analysis using the Canadian Longitudinal Study on Ageing data and find that while online participation alone may increase loneliness, engaging in offline participation will make online participation beneficial. Loneliness serves as a mediating mechanism such that engaging in both online and offline social participation can indirectly enhance satisfaction with life by reducing loneliness. We further find a significant moderating effect of social support, which mitigates the negative impact of loneliness on life satisfaction.