Security and privacy challenge in Bring Your Own Device environment: A Systematic Literature Review

Oktavia, Tanty; Yanti, Yanti; Prabowo, Harjanto; Meyliana, Meyliana

doi:10.1109/icimtech.2016.7930328

Cited by 10 publications

(9 citation statements)

References 14 publications

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“…As discussed by Oktavia (2016) there are implications BYOD policies bring to the security and privacy of the organisation. Privacy in this context is defined as the behaviour of the organisation to protect their information assets and their customer personally identifiable information.…”

Section: Mfa Bring Your Own Device (Byod)mentioning

confidence: 99%

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A Hybrid Model for the Evaluation of End User Experience in a Bring Your Own Device Environment Using Multifactor Authentication

2020

Proceedings of the 17th International Conference on Applied Computing (AC2020)

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There is a growing trend of Bring Your Own Device (BYOD) adoption in companies; the controls such as policies, companies attitude to risk, technological solutions that offer an additional layer of security help companies to protect themselves against risks such as data leakage or social engineering. We propose that companies focus is absolutely warranted to ensure the confidentiality and privacy of its assets but user experience must not be forgotten with this rising trend. In this paper we took an existing model Gupta (2018) used to measure user's intention to adopt BYOD and modified and utilised it to further investigate User Experience (UE) in relation to Multifactor Authentication (MFA) and BYOD. We implemented the model in a similar fashion as Gupta's study and created a number of hypothesis to evaluate the user's response and determine their experience to be positive, negative or neutral. The results indicated that there was not a correlation between perceived ease of use of MFA and perceived usefulness or user experience. While surprising this too was observed by Gupta and is an area further study is required. There were strong correlations observed however in positive perceived ease of use and personal innovativeness on users' positive attitude towards MFA which in turn could likely impact user experience.

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Section: Mfa Bring Your Own Device (Byod)mentioning

confidence: 99%

“…The critical components according to this study into BYOD view the main challenges to be privacy and security related. This research advocates for the implementation of adequate BYOD policies as well segregation of the corporate space from personal data with the goal of protecting corporate data (Oktavia et al, 2016).…”

Section: Mfa Bring Your Own Device (Byod)mentioning

confidence: 99%

A Hybrid Model for the Evaluation of End User Experience in a Bring Your Own Device Environment Using Multifactor Authentication

2020

Proceedings of the 17th International Conference on Applied Computing (AC2020)

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“…Currently, few reviews and survey research have been conducted on BYOD security implementation (Akin-Adetoro & Kabanda, 2015; Oktavia et al, 2016;Wang et al, 2014). For instance, Garba et al (2015) conducted a review study on BYOD with a focus on information security and privacy challenges.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for detecting and combating bring your own device (BYOD) security threats and attacks: a systematic mapping review

2023

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Bring your own device (BYOD) paradigm that permits employees to come with their own mobile devices to join the organizational network is rapidly changing the organizational operation method by enhancing exibility, productivity, and e ciency. Despite these bene ts, security issues remain a concern in organizational settings. A considerable number of studies have been conducted and published in this domain without a detailed review of the security solution mechanisms. Moreover, some reviews conducted focused more on the conventional approaches such as mobile content management, and application content management.Hence, the implementation of security in BYOD using the conventional method is ineffective. Thus, machine learning approaches seem to be the promising approach, which provides a solution to the security problem in the BYOD environment. This study presents a comprehensive systematic mapping review that focused on the application of the machine learning approach for the mitigation of security threats and attacks in the BYOD environment by highlighting the current trends in the existing studies. Five academic databases were searched and a total of 753 of the primary studies published between 2012 and 2021 were initially retrieved. These studies were screened based on their title, abstract and full text to check their eligibility and relevance for the study. However, forty primary studies were included and analyzed in the systematic mapping review (SMR). Based on the analysis and bubble plot mapping, signi cant research trends were identi ed on security threats and attacks, machine learning approaches, datasets usage, and evaluation metrics. The SMR result demonstrates the rise in the number of investigations regarding malware and unauthorized access to existing security threats and attacks. The SMR study indicates that supervised learning approaches such as SVM, DT, and RF are the most employed learning model by the previous research. Thus, there is an open research issue in the application of unsupervised learning approaches such as clustering and deep learning approaches. Therefore, the SMR has set the pace for creating new ground research in the machine learning implementation in the BYOD environment, which will offer invaluable insight into the study eld, and researchers can employ it to nd a research gap in the research domain.

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“…The context column shows that the found related reviews address broader research areas such as IoT , Bring Your Own Device (BYOD) (Oktavia et al, 2017) and big data (Nelson and Olovsson, 2016). Although smart toys are considered a sub-area of IoT or BYOD, these more general reviews fail to address smart toys as this is a very specific subject within IoT and BYOD and they refer to more general aspects.…”

Section: Related Workmentioning

confidence: 99%

“…Authors (and year) Context Period Data source 2 Papers Nunes et al (2015) Toys for clinical treatment 2010-2015 X,DL,WoS,SL,GS 13 Nelson and Olovsson (2016) Privacy and security in big data 2012-2015 GS,SC 82 Oktavia et al (2017) Privacy and security in BYOD 2000-2017 S,GS,X 5 Nunes et al 2017Toys for clinical treatment N/D 1 X,DL,GS 16 Privacy in IoT 2010-2017 DL,X,SD,WoS 90 de Albuquerque and Kelner (2018) Smart * Stems were used to increase the chances of finding relevant papers. 1 For the 'and' relationship between these two terms, we used the 'within' (W/ n) operator, where query terms must be within a specified number of terms n, with = n 5.…”

Section: Inclusion and Exclusion Criteriamentioning

confidence: 99%

Proposta de ferramenta de controle parental para brinquedos inteligentes baseada em regras de privacidade

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Security and privacy challenge in Bring Your Own Device environment: A Systematic Literature Review

Cited by 10 publications

References 14 publications

A Hybrid Model for the Evaluation of End User Experience in a Bring Your Own Device Environment Using Multifactor Authentication

A Hybrid Model for the Evaluation of End User Experience in a Bring Your Own Device Environment Using Multifactor Authentication

Machine learning approach for detecting and combating bring your own device (BYOD) security threats and attacks: a systematic mapping review

Proposta de ferramenta de controle parental para brinquedos inteligentes baseada em regras de privacidade

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