Robust Performance Metrics for Authentication Systems

Sugrim, Shridatt; Liu, Can; McLean, Meghan C.; Lindqvist, Janne

doi:10.14722/ndss.2019.23351

Cited by 28 publications

(13 citation statements)

References 49 publications

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“…The segregation of research material (e.g., prototypes) from publications is common and makes reproducibility and comparisons challenging (Sugrim et al, 2019;Vines et al, 2014;Wicherts et al, 2006). While individual researchers of the HCI and security communities are gradually implementing elements of the Open Science movement (Innovation, 2016), there are still many contributions that are often not publicly available.…”

Section: Accessibility Of Research Materialsmentioning

confidence: 99%

Prototyping Usable Privacy and Security Systems: Insights from Experts

Mathis

Vaniea

Khamis

2021

International Journal of Human–Computer Interaction

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Iterative design, implementation, and evaluation of prototype systems is a common approach in Human-Computer Interaction (HCI) and Usable Privacy and Security (USEC); however, research involving physical prototypes can be particularly challenging. We report on twelve interviews with established and nascent USEC researchers who prototype security and privacy-protecting systems and have published work in top-tier venues. Our interviewees range from professors to senior PhD candidates, and researchers from industry. We discussed their experiences conducting USEC research that involves prototyping, opinions on the challenges involved, and the ecological validity issues surrounding current evaluation approaches. We identify the challenges faced by researchers in this area such as the high costs of conducting field studies when evaluating hardware prototypes, the scarcity of open-source material, and the resistance to novel prototypes. We conclude with a discussion of how the USEC community currently supports researchers in overcoming these challenges and places to potentially improve support.

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Section: Accessibility Of Research Materialsmentioning

confidence: 99%

Prototyping Usable Privacy and Security Systems: Insights from Experts

Mathis

Vaniea

Khamis

2021

International Journal of Human–Computer Interaction

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“…To evaluate the machine learning algorithms, performance metrics are used [11]. The following equations are used to evaluate the performance of support vector machine and Naïve Bayes classifiers.…”

Section: Performance Metricsmentioning

confidence: 99%

SenticNet-Based Feature Weighting Scheme for Sentiment Classification

Kalaivani¹,

Rakshana²,

Mounika³

et al. 2021

Mobile Computing and Sustainable Informatics

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Sentiment analysis aims to study the peoples' perceptions, sentiments and emotions in comments, blogs, review and so on. The performance of classifier can be improved by the use of efficient feature weighting schemes. The existing feature weighting schemes are based on frequency. They do not consider the meaning of the words for weighting. This leads to the degradation in the performance of machine learning algorithms. To address this issue, in the current study, SenticNet is used. SenticNet is a sentiment lexicon resource extracted from ConceptNet consisting of the polarity value in a range from − 1 to + 1. Classifiers like Naive Bayes and support vector machine are used to classify the reviews. The performance of SenticNet and existing feature weighting schemes are evaluated for sentiment classification. From the experiments conducted, it is found that SenticNet-based feature weighting scheme outperforms the existing schemes like binary, TF and TF-IDF.

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“…Finally, we point to other works in literature analyzing the security of biometric authentication systems. Sugrim et al [45] survey and evaluate a range of performance metrics used in biometric authentication schemes. They seek to motivate scheme designers to leverage robust metrics to provide a complete description of the system, including a proposal of the new metric: Frequency Count Score (FCS).…”

Section: Related Workmentioning

confidence: 99%

On the Resilience of Biometric Authentication Systems against Random Inputs

Zhao¹,

Asghar²,

Kâafar³

2020

Proceedings 2020 Network and Distributed System Security Symposium

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We assess the security of machine learning based biometric authentication systems against an attacker who submits uniform random inputs, either as feature vectors or raw inputs, in order to find an accepting sample of a target user. The average false positive rate (FPR) of the system, i.e., the rate at which an impostor is incorrectly accepted as the legitimate user, may be interpreted as a measure of the success probability of such an attack. However, we show that the success rate is often higher than the FPR. In particular, for one reconstructed biometric system with an average FPR of 0.03, the success rate was as high as 0.78. This has implications for the security of the system, as an attacker with only the knowledge of the length of the feature space can impersonate the user with less than 2 attempts on average. We provide detailed analysis of why the attack is successful, and validate our results using four different biometric modalities and four different machine learning classifiers. Finally, we propose mitigation techniques that render such attacks ineffective, with little to no effect on the accuracy of the system.

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Robust Performance Metrics for Authentication Systems

Cited by 28 publications

References 49 publications

Prototyping Usable Privacy and Security Systems: Insights from Experts

Prototyping Usable Privacy and Security Systems: Insights from Experts

SenticNet-Based Feature Weighting Scheme for Sentiment Classification

On the Resilience of Biometric Authentication Systems against Random Inputs

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