A New Non-Intrusive Authentication Method Based on the Orientation Sensor for Smartphone Users

Lin, Chien-Cheng; Chang, Chin-Chun; Liang, Deron; Yang, Ching-Han

doi:10.1109/sere.2012.37

Cited by 33 publications

(20 citation statements)

References 20 publications

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“…Even so, when there are obstacles during the scanning process, the decision on recognition may be disrupted. In general, the direction of movement is detected by the magnetometer, while the gait recognition is detected by the gyroscopic sensor and accelerometer [33,34]. For verification purposes, these authors used the same classifier, which is the K-nearest neighbor (K-NN).…”

Section: Physical Biometricsmentioning

confidence: 99%

A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

Mahadi¹,

Mohamed²,

IhsanMohamad³

et al. 2018

Recent Advances in Cryptography and Network Security

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Authentication is a way to enable an individual to be uniquely identified usually based on passwords and personal identification number (PIN). The main problems of such authentication techniques are the unwillingness of the users to remember long and challenging combinations of numbers, letters, and symbols that can be lost, forged, stolen, or forgotten. In this paper, we investigate the current advances in the use of behavioral-based biometrics for user authentication. The application of behavioral-based biometric authentication basically contains three major modules, namely, data capture, feature extraction, and classifier. This application is focusing on extracting the behavioral features related to the user and using these features for authentication measure. The objective is to determine the classifier techniques that mostly are used for data analysis during authentication process. From the comparison, we anticipate to discover the gap for improving the performance of behavioral-based biometric authentication. Additionally, we highlight the set of classifier techniques that are best performing for behavioral-based biometric authentication.

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Section: Physical Biometricsmentioning

confidence: 99%

A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

Mahadi¹,

Mohamed²,

IhsanMohamad³

et al. 2018

Recent Advances in Cryptography and Network Security

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“…The authors conclude that the non-intrusive mechanism can be used with the intrusive mechanism. For example, PIN or Password can be used with the biometric (finger-prints) to increase the security of smartphones [25].…”

Section: A Authentication In Smartphonesmentioning

confidence: 99%

A Survey on Security for Smartphone Device

Zaidi¹,

Shah²,

Kamran³

et al. 2016

ijacsa

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Abstract-The technological advancements in mobile connectivity services such as GPRS, GSM, 3G, 4G, Blue-tooth, WiMAX, and Wi-Fi made mobile phones a necessary component of our daily lives. Also, mobile phones have become smart which let the users perform routine tasks on the go. However, this rapid increase in technology and tremendous usage of the smartphones make them vulnerable to malware and other security breaching attacks. This diverse range of mobile connectivity services, device software platforms, and standards make it critical to look at the holistic picture of the current developments in smartphone security research. In this paper, our contribution is twofold. Firstly, we review the threats, vulnerabilities, attacks and their solutions over the period of 2010-2015 with a special focus on smartphones. Attacks are categorized into two types, i.e., old attack and new attacks. With this categorization, we aim to provide an easy and concise view of different attacks and the possible solutions to improve smartphone security. Secondly, we critically analyze our findings and estimate the market growth of different operating systems for the smartphone in coming years. Furthermore, we estimate the malware growth and forecast the world in 2020.

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“…The average response was 3.2, with a smaller standard deviation than the previous Authors URR AAR anomaly detection De Luca et al [21] 19% 21% yes Feng et al [24] 18.92% 14.02% no Lin et al [52] 6.85% 6.85% no Bo et al [10] 27.64% 24.99% yes Frank et al [27] 13% 13% no Our Work 21% 20% yes Table 6.9: Results in the Literature question. The users also considered swipes to be slightly less obtrusive than PINs (2.5 response on average).…”

Section: Comparisons To Pin Authenticationmentioning

confidence: 69%

“…Out of the authors discussed in the background section, there are five authors who evaluate a scheme based in some way on touch screen input. These authors are given in As stated above, Feng [24], Lin [52] and Frank [27] report results for a single touch action, however only under binary classification and without considering informed attackers. These results are better for both AAR and URR than our own, however that is not surprising given knowledge of the attacker's swipes.…”

Section: Comparison To Literaturementioning

confidence: 99%

“…This task can be imposed (either explicitly or implicitly as part of the user interface) at the point where authentication would typically happen, such as when the device is being unlocked or when an application is accessed. One example of this is the work of Lin et al [52]. In this work, the authors focus exclusively on the orientation sensor of the device.…”

Section: Task-based Authenticationmentioning

confidence: 99%

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Towards Effective Behavioural Biometrics for Mobile Devices

Bingham¹

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Mobile devices store immense amounts of personal and sensitive data, and so have become targets for attackers. The first line of defence against attacks is authentication-verifying the identity of an agent accessing the device. We examine behavioural biometrics as an effective authentication mechanism on mobile devices. Behavioural biometrics observe and model an agent's behaviours to establish their identity. We construct an authentication system based on profiling the device sensors (touch screen, accelerometer and gyroscope) during a swipe, defined as a quick, simple movement across a touch screen. In addition, we explore the relationship between problem setting and evaluation methodology in authentication systems, producing a list of requirements necessary for accurate evaluation. Finally, we perform a user study to ascertain the effectiveness of our behavioural biometric mechanism. We conclude that this system is resistant to attacks which trivially bypass standard mechanisms such as PINs, while also potentially lightening the usability load imposed by authentication.

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A New Non-Intrusive Authentication Method Based on the Orientation Sensor for Smartphone Users

Cited by 33 publications

References 20 publications

A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

A Survey on Security for Smartphone Device

Towards Effective Behavioural Biometrics for Mobile Devices

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