BeCAPTCHA: Behavioral bot detection using touchscreen and mobile sensors benchmarked on HuMIdb

Acien, Alejandro; Morales, Aythami; Fiérrez, Julián; Vera-Rodríguez, Rubén; Delgado-Mohatar, Oscar

doi:10.1016/j.engappai.2020.104058

Cited by 33 publications

(24 citation statements)

References 32 publications

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“…In contrast, on the one hand, gesturebased related studies take into account unimodal systems [7,16,17,18,19,20,21]; on the other hand, related studies based on DL models for multimodal behavioral biometrics do not have a specific focus on human gestures [26,27]; ii) we analyze the information captured by the touchscreen in combination with simultaneous background sensor data to exploit the complementarity between taskdependent features and background sensors features (accelerometer, gravity sensor, gyroscope, linear accelerometer, magnetometer). Interaction database), a novel and public database comprising more than 5GB from a wide range of mobile sensor data acquired under unsupervised scenario for user passive authentication [30].…”

Section: System Overviewmentioning

confidence: 99%

“…The Human Machine Interaction database (HuMIdb) is a freely available database including data acquired by 14 mobile sensors during natural human-mobile interaction of 600 subjects with a total of 179 different device models [30]. The acquisition data was completed across five sessions separated by a 24-hour gap at least, in order to account for intra-subject variability.…”

Section: Humidb Databasementioning

confidence: 99%

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Mobile behavioral biometrics for passive authentication

Stragapede

Vera-Rodríguez

Tolosana

et al. 2022

Pattern Recognition Letters

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Section: System Overviewmentioning

confidence: 99%

Section: Humidb Databasementioning

confidence: 99%

Mobile behavioral biometrics for passive authentication

Stragapede

Vera-Rodríguez

Tolosana

et al. 2022

Pattern Recognition Letters

Self Cite

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“…Recent studies have gathered large amounts of data by making collection apps available on public app stores [2,29]. This is a step in the right direction in terms of dataset sizes but presents other challenges.…”

Section: Prevalence Of Evaluation Pitfallsmentioning

confidence: 99%

“…This is a step in the right direction in terms of dataset sizes but presents other challenges. For instance, in the case of [29] there is data from 2218 users collected on 2418 different devices and in [2] there is data from 600 users on 278 distinct devices. There is likely a large variation in the unique device models used as well, especially considering the large fragmentation of the Android ecosystem.…”

Section: Prevalence Of Evaluation Pitfallsmentioning

confidence: 99%

“…If users gradually get used to the experimental settings (i.e., their behavior exhibits reduced variation), then Early sessions will perform worse than Late sessions when the user has acclimatized after many repetitions. We apply our 2 given empirical per-user EER standard deviation and EER mean measured at 𝑛, 𝜎 𝑛 and 𝜇 𝑛 , we estimate σ𝑚 using 𝑛=40 as σ𝑚 = authentication pipeline on both early and late sets, making several splits with 𝑠 ranging from 3 to 15. We report the results in Figure 5, showing no significant difference between the performance of early and late sessions.…”

Section: Number Of Sessions and Swipesmentioning

confidence: 99%

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FETA: Fair Evaluation of Touch-based Authentication

Georgiev¹,

Eberz²,

Turner³

et al. 2022

Preprint

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In this paper, we investigate common pitfalls affecting the evaluation of authentication systems based on touch dynamics. We consider different factors that lead to misrepresented performance, are incompatible with stated system and threat models or impede reproducibility and comparability with previous work. Specifically, we investigate the effects of (i) small sample sizes (both number of users and recording sessions), (ii) using different phone models in training data, (iii) selecting non-contiguous training data, (iv) inserting attacker samples in training data and (v) swipe aggregation. We perform a systematic review of 30 touch dynamics papers showing that all of them overlook at least one of these pitfalls. To quantify each pitfall's effect, we design a set of experiments and collect a new longitudinal dataset of touch dynamics from 470 users over 31 days comprised of 1,166,092 unique swipes. We make this dataset and our code available online. Our results show significant percentage-point changes in reported mean EER for several pitfalls: including attacker data (2.55%), non-contiguous training data (3.8%), phone model mixing (3.2%-5.8%). We show that, in a common evaluation setting, cumulative effects of these evaluation choices result in a combined difference of 8.9% EER. We also largely observe these effects across the entire ROC curve. Furthermore, we validate the pitfalls on four distinct classifiers -SVM, Random Forest, Neural Network, and kNN. Based on these insights, we propose a set of best practices that, if followed, will lead to more realistic and comparable reporting of results in the field.

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