ORCID Profile
0000-0001-7535-5875
Current Organisations
CSIRO
,
CSIRO (Data61)
,
Macquarie University
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Publisher: Springer US
Date: 2009
DOI: 10.1007/B137171_12
Publisher: Springer International Publishing
Date: 2020
Publisher: Frontiers Media SA
Date: 24-09-2020
Publisher: ACM
Date: 26-04-2014
Publisher: ACM Press
Date: 2006
Publisher: IEEE
Date: 06-2013
Publisher: IEEE
Date: 07-2020
Publisher: Australian Water Association
Date: 2019
Publisher: IEEE
Date: 2006
Publisher: ACM
Date: 13-07-2016
Publisher: ACM
Date: 03-07-2018
Publisher: Emerald
Date: 09-08-2021
Abstract: This study aims to examine the effect of cybersecurity threat and efficacy upon click-through, response to a phishing attack: persuasion and protection motivation in an organizational context. In a simulated field trial conducted in a financial institute, via PhishMe, employees were randomly sent one of five possible emails using a set persuasion strategy. Participants were then invited to complete an online survey to identify possible protective factors associated with clicking and reporting behavior ( N = 2,918). The items of interest included perceived threat severity, threat susceptibility, response efficacy and personal efficacy. The results indicate that response behaviors vary significantly across different persuasion strategies. Perceptions of threat susceptibility increased the likelihood of reporting behavior beyond clicking behavior. Threat susceptibility and organizational response efficacy were also associated with increased odds of not responding to the simulated phishing email attack. This study again highlights human susceptibility to phishing attacks in the presence of social engineering strategies. The results suggest heightened awareness of phishing threats and responsibility to personal cybersecurity are key to ensuring secure business environments. The authors extend existing phishing literature by investigating not only click-through behavior, but also no-response and reporting behaviors. Furthermore, the authors observed the relative effectiveness of persuasion strategies used in phishing emails as they compete to manipulate unsafe email behavior.
Publisher: Wiley
Date: 07-09-2022
Publisher: ACM
Date: 14-11-2011
Publisher: ACM
Date: 12-11-2007
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: ACM
Date: 27-04-2013
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11618058_42
Publisher: Elsevier BV
Date: 12-2022
Publisher: Association for Computing Machinery (ACM)
Date: 12-2012
Abstract: High cognitive load arises from complex time and safety-critical tasks, for ex le, mapping out flight paths, monitoring traffic, or even managing nuclear reactors, causing stress, errors, and lowered performance. Over the last five years, our research has focused on using the multimodal interaction paradigm to detect fluctuations in cognitive load in user behavior during system interaction. Cognitive load variations have been found to impact interactive behavior: by monitoring variations in specific modal input features executed in tasks of varying complexity, we gain an understanding of the communicative changes that occur when cognitive load is high. So far, we have identified specific changes in: speech, namely acoustic, prosodic, and linguistic changes interactive gesture and digital pen input, both interactive and freeform. As ground-truth measurements, galvanic skin response, subjective, and performance ratings have been used to verify task complexity. The data suggest that it is feasible to use features extracted from behavioral changes in multiple modal inputs as indices of cognitive load. The speech-based indicators of load, based on data collected from user studies in a variety of domains, have shown considerable promise. Scenarios include single-user and team-based tasks think-aloud and interactive speech and single-word, reading, and conversational speech, among others. Pen-based cognitive load indices have also been tested with some success, specifically with pen-gesture, handwriting, and freeform pen input, including diagraming. After examining some of the properties of these measurements, we present a multimodal fusion model, which is illustrated with quantitative ex les from a case study. The feasibility of employing user input and behavior patterns as indices of cognitive load is supported by experimental evidence. Moreover, symptomatic cues of cognitive load derived from user behavior such as acoustic speech signals, transcribed text, digital pen trajectories of handwriting, and shapes pen, can be supported by well-established theoretical frameworks, including O'Donnell and Eggemeier's workload measurement [1986] Sweller's Cognitive Load Theory [Chandler and Sweller 1991], and Baddeley's model of modal working memory [1992] as well as McKinstry et al.'s [2008] and Rosenbaum's [2005] action dynamics work. The benefit of using this approach to determine the user's cognitive load in real time is that the data can be collected implicitly that is, during day-to-day use of intelligent interactive systems, thus overcomes problems of intrusiveness and increases applicability in real-world environments, while adapting information selection and presentation in a dynamic computer interface with reference to load.
Publisher: ACM
Date: 07-03-2017
Publisher: IEEE
Date: 06-2013
Publisher: ACM
Date: 29-01-2006
Publisher: IEEE
Date: 06-2013
Publisher: Association for Computing Machinery (ACM)
Date: 30-09-2020
DOI: 10.1145/3357459
Abstract: Personality detection is an important task in psychology, as different personality traits are linked to different behaviours and real-life outcomes. Traditionally it involves filling out lengthy questionnaires, which is time-consuming, and may also be unreliable if respondents do not fully understand the questions or are not willing to honestly answer them. In this article, we propose a framework for objective personality detection that leverages humans’ physiological responses to external stimuli. We exemplify and evaluate the framework in a case study, where we expose subjects to affective image and video stimuli, and capture their physiological responses using non-invasive commercial-grade eye-tracking and skin conductivity sensors. These responses are then processed and used to build a machine learning classifier capable of accurately predicting a wide range of personality traits. We investigate and discuss the performance of various machine learning methods, the most and least accurately predicted traits, and also assess the importance of the different stimuli, features, and physiological signals. Our work demonstrates that personality traits can be accurately detected, suggesting the applicability of the proposed framework for robust personality detection and use by psychology practitioners and researchers, as well as designers of personalised interactive systems.
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 29-06-2013
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 06-2013
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 09-2014
Publisher: ACM
Date: 28-04-2007
Publisher: ACM
Date: 17-03-2019
Publisher: ACM
Date: 02-05-2019
Publisher: Association for Computing Machinery (ACM)
Date: 14-01-2015
DOI: 10.1145/2687924
Abstract: This article presents a framework of adaptive, measurable decision making for Multiple Attribute Decision Making (MADM) by varying decision factors in their types, numbers, and values. Under this framework, decision making is measured using physiological sensors such as Galvanic Skin Response (GSR) and eye-tracking while users are subjected to varying decision quality and difficulty levels. Following this quantifiable decision making, users are allowed to refine several decision factors in order to make decisions of high quality and with low difficulty levels. A case study of driving route selection is used to set up an experiment to test our hypotheses. In this study, GSR features exhibit the best performance in indexing decision quality. These results can be used to guide the design of intelligent user interfaces for decision-related applications in HCI that can adapt to user behavior and decision-making performance.
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2019
Publisher: Institution of Engineering and Technology (IET)
Date: 2007
Location: Australia
No related grants have been discovered for Ronnie Taib.