Treadmill attack on gait-based authentication systems

Kumar, Rajesh; Phoha, Vir V.; Jain, Anshumali

doi:10.1109/btas.2015.7358801

Cited by 35 publications

(71 citation statements)

References 13 publications

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“…This dataset consists of walking patterns collected through smartphone accelerometer from 18 users who were either faculty, staff or students [19]. Android's type linear acceleration was used that recorded the accelerations (with no gravity component) in the sensor's own frame of reference.…”

Section: Phone Acceleration-based Gait Biometricmentioning

confidence: 99%

“…The sampling rate of the accelerometer was set to normal which produced around 46 samples per second. The steps of data preprocessing, feature analysis, generation of genuine and impostor samples for training and testing were replicated exactly as advised in [19]. This dataset was the smallest in terms of volume, as the average number of total samples per user per session were 16.…”

Section: Phone Acceleration-based Gait Biometricmentioning

confidence: 99%

Section: Choice Of Classifiersmentioning

confidence: 99%

“…For each dataset, we replicated the experimental setup advised by respective authors in their papers [19], [6], [2]. For the PABG dataset, the authors studied five classifiers: Bayes Network, Logistic Regression, Multilayer Perceptrons, Random Forests, and Support Vector Machines [19]. Similarly, four classifiers (k Nearest Neighbors, Logistic Regression, Multilayer Perceptrons, and Random Forests) were used to implement CAS on W ABG and W RBG [6], and two classifiers (kNN with Euclidean, and Random Forests) were used to implement CAS for SP M P [2].…”

Section: Choice Of Classifiersmentioning

confidence: 99%

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Continuous authentication using one-class classifiers and their fusion

Kumar

Kundu

Phoha

2018

2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)

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While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances.Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (M CC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of M CC. Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.

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Section: Phone Acceleration-based Gait Biometricmentioning

confidence: 99%

Section: Phone Acceleration-based Gait Biometricmentioning

confidence: 99%

Section: Choice Of Classifiersmentioning

confidence: 99%

Section: Choice Of Classifiersmentioning

confidence: 99%

See 2 more Smart Citations

Continuous authentication using one-class classifiers and their fusion

Kumar

Kundu

Phoha

2018

2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)

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“…Muaaz et al [8] Gait recognition Active imposter (imitation), 20% EER Xu et al [14] Device pairing Active imposter (imitation), passive imposter, MitM Kumar et al [53] Gait recognition Treadmill attack Trippel et al [54] Injection of false acceleration…”

Section: Paper Applications Attackingmentioning

confidence: 99%

Security Properties of Gait for Mobile Device Pairing

Brüsch

Nguyen

Schürmann

et al. 2020

IEEE Trans. on Mobile Comput.

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Gait has been proposed as a feature for mobile device pairing across arbitrary positions on the human body. Results indicate that the correlation in gait-based features across different body locations is sufficient to establish secure device pairing. However, the population size of the studies is limited and powerful attackers with e.g. capability of video recording are not considered. We present a concise discussion of security properties of gait-based pairing schemes including quantization, classification and analysis of attack surfaces, of statistical properties of generated sequences, an entropy analysis, as well as possible threats and security weaknesses. For one of the schemes considered, we present modifications to fix an identified security flaw. As a general limitation of gait-based authentication or pairing systems, we further demonstrate that an adversary with video support can create key sequences that are sufficiently close to on-body generated acceleration sequences to breach gait-based security mechanisms.

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