Android security assessment: A review, taxonomy and research gap study

Garg, Shivi; Baliyan, Niyati

doi:10.1016/j.cose.2020.102087

Cited by 24 publications

(14 citation statements)

References 57 publications

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“…They are data extraction, data preprocessing, feature selection, model training, model evaluation, and model deployment [9]. Supervised learning, unsupervised learning, semisupervised learning, reinforcement learning, and deep learning are the different subcategories of ML [28]. The supervised learning approach uses a labelled dataset to train the model to solve classification and regression problems depend on the output variable type (continuous or discreet).…”

Section: Machine Learning Processmentioning

confidence: 99%

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Android Mobile Malware Detection Using Machine Learning: A Systematic Review

2021

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With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and malicious advertising. Among numerous countermeasures, machine learning (ML)-based methods have proven to be an effective means of detecting these attacks, as they are able to derive a classifier from a set of training examples, thus eliminating the need for an explicit definition of the signatures when developing malware detectors. This paper provides a systematic review of ML-based Android malware detection techniques. It critically evaluates 106 carefully selected articles and highlights their strengths and weaknesses as well as potential improvements. Finally, the ML-based methods for detecting source code vulnerabilities are discussed, because it might be more difficult to add security after the app is deployed. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in the field and to identify potential future research and development directions.

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Section: Machine Learning Processmentioning

confidence: 99%

“…Under the static analysis, four aspects were proposed [28] which are analysis techniques, sensitivity analysis, data structure, and code representation. Under the analysis techniques, Symbolic execution, taint analysis, program slicing, abstract interpretation, type checking, and code instrumentation were identified.…”

Section: Static Dynamic and Hybrid Analysismentioning

confidence: 99%

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

2021

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“…As far as we know, there are no recent secondary studies focused on the intersection of the three domains of our scope (i.e., privacy, software quality, and Android applications). The related work is either outdated [6,7] or has a different scope, e.g., by focusing on security instead of privacy [9,10] or missing the overall set of quality assessment techniques we have covered [46,47]. We analyse these studies next.…”

Section: Related Workmentioning

confidence: 99%

“…Further, the contributions are scattered in different domains such as software engineering, cybersecurity, or computer networks, resulting in a growing but dispersed body of knowledge. Some previous works have provided insight into the topic but were carried out more than five years ago [6,7] or addressed security instead of privacy [9,10].…”

Section: Introductionmentioning

confidence: 99%

Privacy Assessment in Android Apps: A Systematic Mapping Study

et al. 2021

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Android apps are daily installed by billions of users worldwide, who grant access to an extensive set of sensitive personal data. Different techniques have been developed over the years to understand how apps protect or harm their users’ privacy. However, these results have been produced in different research domains and addressing privacy from different perspectives, resulting in a growing but scattered body of knowledge. To bridge this gap, we have carried out a systematic mapping study to provide practitioners and researchers with an overview of the state-of-the-art technique, published between 2016 and 2020, to assess privacy in Android apps. In this paper, we highlight the most relevant findings, identify and analyse the most pressing gaps, and discuss the promising research directions.

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“…Primary security services like authentication and authorization are usually implemented within access control systems (ACSs). Frequently, ACSs are only used as an entrance barrier to a system, but others adopt a continuous monitoring approach, where some kind of detection procedures are used to both determine the occurrence of undesired activities or behaviors [7], [8] during the lifetime operation of devices as well as to carry out some kind of countermeasures.…”

Section: Introductionmentioning

confidence: 99%

ARANAC: A Bring-Your-Own-Permissions Network Access Control Methodology for Android Devices

et al. 2021

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In this paper, we introduce a new methodology for network access control for Android devices based on app risk assessment. Named ARANAC (which stands for Application Risk Assessment based Network Access Control), this methodology is specially tailored for scenarios using the Bring-Your-Own-Device (BYOD) policy, where the adoption of some solutions can lead to problems in security and privacy for both the employees and the business organization. ARANAC mainly relies on the analysis of an aggregate of permissions declared in the manifests of installed applications on users' devices. The access control scheme combines three operational modules: i) a device monitoring tool, ii) a novel permission-based risk model, and iii) an anomaly-based detection machine learning module based on a methodology (called MSNM, from Multivariate Statistical Network Monitoring) that provides both detection and diagnostic capabilities. ARANAC's novelty is in the combination of four features. Firstly, it is privacy-aware, and thus, it does not require detailed information about installed applications but only an aggregate of permissions. Secondly, it builds a normality model by combining expert knowledge with data, capturing the behavior of a complete population of mobile devices. Thirdly, it is dynamic, as permissions are updated in real time, allowing the network to re-assess access control on a continuous basis. Finally, its diagnostic capabilities allow for giving recommendations to final users so that they are capable of mitigating their risks when accessing networks. We evaluated the approach with more than 80 Android devices at a university campus network and obtained interesting results regarding security risks in the usual deployment of device apps.INDEX TERMS Android permissions, bring-your-own-device, mobile security, network access control, risk assessment.

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Android security assessment: A review, taxonomy and research gap study

Cited by 24 publications

References 57 publications

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

Privacy Assessment in Android Apps: A Systematic Mapping Study

ARANAC: A Bring-Your-Own-Permissions Network Access Control Methodology for Android Devices

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