The Online Identity Detection via Keyboard Dynamics

Monaro, Merylin; Businaro, Marta; Spolaor, Riccardo; Li, Qian Qian; Conti, Mauro; Gamberini, Luciano; Sartori, Giuseppe

doi:10.1007/978-3-030-02683-7_24

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…The difference observed in RTs between faking and honest respondents is statistically significant only when test takers endorse items; it is not present when items are rejected. Similar evidence has been produced by researchers investigating the behavioral responses of honest and faking subjects using more complex measures, such as mouse tracking (28, 29) and keystroke dynamics (30, 31). Moreover, time pressure is a technique that has been shown to be effective in identifying malingering respondents (32).…”

Section: Introductionsupporting

confidence: 55%

Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times

et al. 2019

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Background and Purpose. The use of machine learning (ML) models in the detection of malingering has yielded encouraging results, showing promising accuracy levels. We investigated the possible application of this methodology when trained on behavioral features, such as response time (RT) and time pressure, to identify faking behavior in self-report personality questionnaires. To do so, we reintroduced the article of Roma et al. (2018), which highlighted that RTs and time pressure are useful variables in the detection of faking; we then extended the number of participants and applied an ML analysis. Materials and Methods. The sample was composed of 175 subjects, of whom all were graduates (having completed at least 17 years of instruction), male, and Caucasian. Subjects were randomly assigned to four groups: honest speeded, faking-good speeded, honest unspeeded, and faking-good unspeeded. A software version of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) was administered. Results. Results indicated that ML algorithms reached very high accuracies (around 95%) in detecting malingerers when subjects are instructed to respond under time pressure. The classifiers’ performance was lower when the subjects responded with no time restriction to the MMPI-2-RF items, with accuracies ranging from 75% to 85%. Further analysis demonstrated that T -scores of validity scales are ineffective to detect fakers when participants were not under temporal pressure (accuracies 55–65%), whereas temporal features resulted to be more useful (accuracies 70–75%). By contrast, temporal features and T -scores of validity scales are equally effective in detecting fakers when subjects are under time pressure (accuracies higher than 90%). Discussion. To conclude, results demonstrated that ML techniques are extremely valuable and reach high performance in detecting fakers in self-report personality questionnaires over more the traditional psychometric techniques. Validity scales MMPI-2-RF manual criteria are very poor in identifying under-reported profiles. Moreover, temporal measures are useful tools in distinguishing honest from dishonest responders, especially in a no time pressure condition. Indeed, time pressure brings out malingerers in clearer way than does no time pressure condition.

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Section: Introductionsupporting

confidence: 55%

Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times

et al. 2019

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“…The efficiency of unexpected questions in detecting faked identities was proved in two additional studies in which the authors applied this technique to analyse the keystroke dynamics while participants were engaged in typing their personal information on the computer keyboard (Monaro et al, 2018(Monaro et al, , 2019. Like for the mouse dynamics, liars took more time to type their responses, especially to unexpected questions.…”

Section: Introductionmentioning

confidence: 99%

The detection of faked identity using unexpected questions and choice reaction times

Monaro

Zampieri

Sartori

et al. 2020

Psychological Research

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The identification of faked identities, especially within the Internet environment, still remains a challenging issue both for companies and researchers. Recently, however, latency-based lie detection techniques have been developed to evaluate whether the respondent is the real owner of a certain identity. Among the paradigms applied to this purpose, the technique of asking unexpected questions has proved to be useful to differentiate liars from truth-tellers. The aim of the present study was to assess whether a choice reaction times (RT) paradigm, combined with the unexpected question technique, could efficiently detect identity liars. Results demonstrate that the most informative feature in distinguishing liars from truth-tellers is the Inverse Efficiency Score (IES, an index that combines speed and accuracy) to unexpected questions. Moreover, to focus on the predictive power of the technique, machine-learning models were trained and tested, obtaining an out-of-sample classification accuracy of 90%. Overall, these findings indicate that it is possible to detect liars declaring faked identities by asking unexpected questions and measuring RTs and errors, with an accuracy comparable to that of well-established latency-based techniques, such as mouse and keystroke dynamics recording.

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“…Accuracy rate reached 93.75% . Other similar experiments show accuracy rates above 90% (Monaro et al, 2019;Monaro, Spolaor, et al, 2017).…”

Section: Keystroke Dynamics and Deception Detectionmentioning

confidence: 75%

Keyboard dynamics discrepancies between baseline and deceptive eyewitness narratives

Tomas

Tsimperidis

Demarchi

et al. 2020

Applied Cognitive Psychology

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Information manipulation and cognitive load imposition make the production of deceptive narratives difficult. But little is known about the production of deception, and how its mechanisms may help distinguish truthful from deceitful narratives. This study focuses on the measurement of keystroke dynamics while typing truthful and deceptive eyewitness testimonies after a baseline assessment. While typing their narrative, some participants would undergo an auditory cognitive load. Results show that liars typed their story slower, and in less time than the truthful participants when compared to their respective baselines. The imposition of the auditory cognitive load showed adverse results, enhancing the amount of keystrokes and the time necessary to type the narrative. Classification shows better results for deceptive narrative when no auditory cognitive load is imposed. These results are discussed in terms of expanding current models to include the cognition of linguistic production and writing strategies.

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The Online Identity Detection via Keyboard Dynamics

Cited by 5 publications

References 22 publications

Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times

Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times

The detection of faked identity using unexpected questions and choice reaction times

Keyboard dynamics discrepancies between baseline and deceptive eyewitness narratives

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