User authentication via touch pattern recognition based on isolation forest

Filippov, Alexander I.; Iuzbashev, Artem V.; Kurnev, Alexey S.

doi:10.1109/eiconrus.2018.8317378

Cited by 14 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That means, a good level of security against impostors. Our MLP achieved better FAR compared to: 3.1% with MLP in Buriro et al (2016), 7.5% with isolation forest in Filippov et al (2018) and 3.9% with HMM in Shen et al (2018). The HMM also achieved EER 4.71%, our approach achieved EER 1.9%.…”

Section: Discussionmentioning

confidence: 53%

“…As we can see in Table 2, the MLP classifierin Buriro et al (2016), the isolation forest in Filippov et al (2018) and the Hidden Markov Model (HMM) in Shen et al (2018) performed well, achieving 3.1%, 7.5% and 3.9% FAR, respectively. The HMM also achieve EER 4.71%.…”

Section: Related Workmentioning

confidence: 86%

“…They tested their mechanism on a data set of 30 volunteers and achieved a TAR of 95% with a FAR of 3.1% by using a MLP. Filippov et al (2018) built an authentication system using the data that BioPrivacy resulted from the interaction of 21 users with the touch screen of a smartphone. They gathered 2,000 features from seven different gesture types of swipe while users interacted with the smartphone.…”

Section: Ics 305mentioning

confidence: 99%

See 2 more Smart Citations

BioPrivacy: a behavioral biometrics continuous authentication system based on keystroke dynamics and touch gestures

Stylios

Skalkos

Kokolakis

et al. 2022

ICS

View full text Add to dashboard Cite

Purpose This research aims to build a system that will continuously. This paper is an extended version of SECPRE 2021 paper and presents a research on the development and validation of a behavioral biometrics continuous authentication (BBCA) system that is based on users keystroke dynamics and touch gestures on mobile devices. This paper aims to build a system that will continuously authenticate the user of a smartphone. Design/methodology/approach Session authentication schemes establish the identity of the user only at the beginning of the session, so they are vulnerable to attacks that tamper with communications after the establishment of the authenticated session. Moreover, smartphones themselves are used as authentication means, especially in two-factor authentication schemes, which are often required by several services. Whether the smartphone is in the hands of the legitimate user constitutes a great concern and correspondingly whether the legitimate user is the one who uses the services. In response to these concerns, BBCA technologies have been proposed on a large corpus of literature. This paper presents a research on the development and validation of a BBCA system (named BioPrivacy), which is based on the user’s keystroke dynamics and touch gestures, using a multi-layer perceptron (MLP). Also, this paper introduces a new BB collection tool and proposes a methodology for the selection of an appropriate set of BB. Findings The system achieved the best results for keystroke dynamics which are 97.18% accuracy, 0.02% equal error rate, 97.2% true acceptance rate and 0.02% false acceptance rate. Originality/value This paper develops a new BB collection tool, named BioPrivacy, by which behavioral data of users on mobile devices can be collected. This paper proposes a methodology for the selection of an appropriate set of BB. This paper presents the development of a BBCA system based on MLP.

show abstract

Section: Discussionmentioning

confidence: 53%

Section: Related Workmentioning

confidence: 86%

Section: Ics 305mentioning

confidence: 99%

See 1 more Smart Citation

BioPrivacy: a behavioral biometrics continuous authentication system based on keystroke dynamics and touch gestures

Stylios

Skalkos

Kokolakis

et al. 2022

ICS

View full text Add to dashboard Cite

show abstract

“…1. (Filippov et al 2018) [38] extracted features of interaction with a device's touch screen for user authentication. Isolated forest technique is employed to fit the touch pattern recognition model; 2.…”

Section: Methodsmentioning

confidence: 99%

RLTIR: Activity-Based Interactive Person Identification via Reinforcement Learning Tree

Yao

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

Identity recognition plays an important role in ensuring security in our daily life. Biometric-based (especially activity-based) approaches are favored due to its fidelity, universality, and resilience. However, most existing machine learningbased approaches rely on a traditional workflow where models are usually trained once for all, with limited involvement from end-users in the process and neglecting the dynamic nature of the learning process. This makes the models static and can not be updated in time, which usually leads to high false positive or false negative. Thus, in practice, an expert is desired to assist with providing high-quality observations and interpretation of model outputs. It is expedient to combine both advantages of human experts and the computational capability of computers to create a tight-coupling incremental learning process for better performance. In this study, we develop RLTIR, an interactive identity recognition approach based on reinforcement learning, to adjust the identification model by human's guidance. We first build a base tree-structured identity recognition model. And an expert is introduced in the model for giving feedback upon model outputs. Then, the model is updated according to strategies that are automatically learned under a designated reinforcement learning framework. To the best of our knowledge, it is the very first attempt to combine human expert knowledge with model learning in the area of identity recognition. The experimental results show that the reinforced interactive identity recognition framework outperforms baseline methods with regards to recognition accuracy and robustness.

show abstract

“…The intuition behind the behavioral approach is based on the distinctive user patterns commonly used in an authentication task. While users interact with their smartphones, the device implicitly captures their interaction with the device, including user touch patterns, environmental and sensory data [12], [13]. The collected user's behavioral data (biometrics) works without knowing or explicitly asking to enter specific data.…”

Section: ) Discontinuous Authentication Systemsmentioning

confidence: 99%

Adaptive Continuous Authentication System for Smartphones using Hyper Negative Selection and Random Forest Algorithms

Alharbi¹,

Marie²

2021

IJACSA

View full text Add to dashboard Cite

As smartphones have become a part of our daily lives, including payment and banking transactions; therefore, increasing current data and privacy protection models is essential. A continuous authentication model aims to track the smartphone user's interaction after the initial login. However, current continuous authentication models are limited due to dynamic changes in smartphone user behavior. This paper aims to enhance smartphone user privacy and security using continuous authentication based on touch dynamics by proposing a framework for smartphone devices based on user touch behavior to provide a more accurate and adaptive learning model. We adopt a hybrid model based on the Hyper Negative Selection Algorithm (HNSA) as an artificial immune system (AIS) and the random forest ensemble classifier to instantly classify a user behavior. With the new approach, a decision model could detect normal/abnormal user behavior and update a user profile continuously while using his/her smartphone. The proposed approach was compared with the v-detector and HNSA, where it shows a high average accuracy of 98.5%, a low false alarm rate, and an increased detection rate. The new model is significant as it could be integrated with a smartphone to increase user privacy instantly. It is concluded that the proposed approach is efficient and valuable for smartphone users to increase their privacy while dynamic user behaviors evolve to change.

show abstract

User authentication via touch pattern recognition based on isolation forest

Cited by 14 publications

References 6 publications

BioPrivacy: a behavioral biometrics continuous authentication system based on keystroke dynamics and touch gestures

BioPrivacy: a behavioral biometrics continuous authentication system based on keystroke dynamics and touch gestures

RLTIR: Activity-Based Interactive Person Identification via Reinforcement Learning Tree

Adaptive Continuous Authentication System for Smartphones using Hyper Negative Selection and Random Forest Algorithms

Contact Info

Product

Resources

About