Vulnerability analysis of iPhone 6

Yang, Wencheng; Hu, Jiankun; Fernandes, Clinton; Sivaraman, Vijay; Wu, Qianhong

doi:10.1109/pst.2016.7907000

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…Fingerprint recognition was also the biometric that the majority of participants had heard of and knowingly used, as seen in Figure 3, Figure 4 and Table 1. However, these fingerprint sensors in our personal devices are easily compromised, as demonstrated by both Yang et al [27] and Kanchikere and Sudha [28]. This highlights that a lack of awareness of other biometric methods is potentially damaging to the personal security of users.…”

Section: Survey Resultsmentioning

confidence: 99%

The language of biometrics: Analysing public perceptions

Buckley

Nurse

2019

Journal of Information Security and Applications

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There is an increasing shift in technology towards biometric solutions, but one of the biggest barriers to widespread use is the acceptance by the users. In this paper we investigate the understanding, awareness and acceptance of biometrics by the general public. The primary research method was a survey, which had 282 respondents, designed to gauge public opinion around biometrics. Additionally, qualitative data was captured in the form of the participants' definition of the term biometrics. We applied thematic analysis as well as an automated Word Vector analysis to this data to provide a deeper insight into the perceptions and understanding of the term. Our results demonstrate that while there is generally a reasonable level of understanding of what biometrics are, this is typically limited to the techniques that are most familiar to participants (e.g., fingerprints or facial recognition). Most notably individuals' awareness overlooks emerging areas such as behavioural biometrics (e.g., gait). This was also apparent when we compared participants' views to definitions provided by official, published sources (e.g., ISO, NIST, OED, DHS). Overall, this article provides unique insight into the perceptions and understanding of biometrics as well as areas where users may lack knowledge on biometric applications.

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Section: Survey Resultsmentioning

confidence: 99%

The language of biometrics: Analysing public perceptions

Buckley

Nurse

2019

Journal of Information Security and Applications

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“…From the lifted latent fingerprint, a mold was created by using a printed circuit board (PCB) by a researcher from Chaos Computer Club (CCC). By filling the art glue into the mold on the PCB [23], he subsequently generated a rubber fingerprint film, by which Touch ID of the iPhone can be fooled.…”

Section: Attacks To User Interface and Countermeasuresmentioning

confidence: 99%

Security and Accuracy of Fingerprint-Based Biometrics: A Review

et al. 2019

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Biometric systems are increasingly replacing traditional password-and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper. recognition as an example. In the stage of enrollment, a user presents their finger to the fingerprint sensor and a fingerprint image is acquired by the sensor module. Certain features of the acquired fingerprint image are extracted, and further adapted or transformed to generate template data for the purpose of comparison in the verification stage. In the verification stage, the fingerprint image of a query is collected by the sensor module. The feature representations of the query fingerprint image go through the same process as in the enrollment stage, so as to obtain query data. The query data are then compared with the template data so that a matching outcome is attained.

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“…Apple strongly focuses on having done additional design work to improve its safety. The iOS security model consists of the following architectural design [23][24][25][26][27] System Architecture apple iOS is one of the best operating systems in terms of the boot loading process. Each step in the booting ensures the system is trusted cryptographically and signed by Apple itself.…”

Section: Ios Security Modelmentioning

confidence: 99%

“…Encryption and Data Protection On iOS, the encryption and protection facilities are based on hardware and software technologies. Every iOS has a path between the phone's main memory and the ROM-based storage with the help of a crypto-based key called AES 256 based key to achieve a high level of encryption and security [27]. This SHA-1 cryptographic algorithm is implemented at the hardware level to improve safety for implementing the security overhead.…”

Section: Ios Security Modelmentioning

confidence: 99%

AI-Enabled Cybernetic Analytics of Security Models for Smart Serious Games-Based Mobile Operating Systems

Ali¹,

Jamil²,

Whangbo³

et al. 2022

Artificial Intelligence Trends &Amp; Technologies

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Smart serious games are games which are primarily designed for a serious job such as education and training rather than entertainment. Mobile Phones are an essential enabler of smart serious games. Over the past few years, there has been an exponential growth in the security of the cyber-physical system of multiple operating systems. Security challenges have emerged from access scenarios in conventional operating systems. However, in Mobile Operating Systems, there is a lack of systematic study about mobile security measurement. Artificial Intelligence (AI) is one of the critical features which exists inside mobile operating systems. AI enhances the working of Mobile OS functionality by providing thinking on user behavior and extending the functionality. We explore some key security models for different mobile Operating System providers. In particular, the comparison of security policies implemented by multiple mobile operating system vendors. It's challenging to deal with and study the related data models and measurements for mobile operating system platforms. This paper collects, analyzes, and provides effective decisions about the existing research on multiple mobile operating systems. We consider the built-in security, Authentication, Data Protection, Device protection, Application Security, Device Wipe, Mobile Device Management, Cooperate Managed e-mail, Active Sync Support, Device Firewall, Security Certifications, and Virtualizations. AI supports all these features to help OS to extend its functionality for effective game management. We have focused on these components that clearly understand the Mobile operating system structure and support services. General, as well as specific features, are explored about the different platforms of the operating systems. The systematic research study is applied to these platforms to capture the response, and based on these responses, and we developed results based on the feedback and research responses. This comparison aims to make the right choice for mobile users to device the best mobile with a high-security Operating System installed for mobiles partaking serious games in cyber-physical environment.

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Vulnerability analysis of iPhone 6

Cited by 13 publications

References 7 publications

The language of biometrics: Analysing public perceptions

The language of biometrics: Analysing public perceptions

Security and Accuracy of Fingerprint-Based Biometrics: A Review

AI-Enabled Cybernetic Analytics of Security Models for Smart Serious Games-Based Mobile Operating Systems

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