A security modeling and verification method of embedded software based on Z and MARTE

Hu, Xinwen; Zhuang, Yi; Zhang, Fuyuan

doi:10.1016/j.cose.2019.101615

Cited by 8 publications

(1 citation statement)

References 24 publications

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“…The process is divided into three main steps as given below. We can extract useful information from the device such as serial number, device name, access specifier and encryption status [9][10][11]. This process checks whether the device is rooted or not henceforth gaining some privileges.…”

Section: Static Analysis Of Appsmentioning

confidence: 99%

A Machine Learning approach for analyzing and detecting Android malicious applications

Raymond¹

2020

IJATCSE

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Android applications are available in the google cloud apps market. Starting from the normal functionalities like calling, messaging, and camera to advanced functionalities like online banking, online shopping, there is no limit to how we can make use of mobile phones. Just like there is no limit to functionalities of the mobile phone, there is no limit to the amount of information available on mobile phones. When you say information, it is confidential information including personal information, username, and password and card details. There are already many cases reported about information leakage by compromising a mobile phone. The important point to be noticed here is that the medium through which mobile phones are providing these functionalities, and that medium is called an application. There are millions of applications in the Play Store and App Store which come with different functionalities. The only way to stop this problem is by stopping the user from downloading malicious applications from the Play Store. But the main challenge in this solution leads to a question, which is "how the user will distinguish between a malicious application and benign application". There are millions of applications in play Store and considering play Store is a trusted media, it is very unlikely to raise any suspicion over an application to a normal user. We propose a novel approach to perform static and dynamic analysis of malicious payload and compare it with the genuine application.

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Section: Static Analysis Of Appsmentioning

confidence: 99%

A Machine Learning approach for analyzing and detecting Android malicious applications

Raymond¹

2020

IJATCSE

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MBIPV: a model-based approach for identifying privacy violations from software requirements

Zhuang

Qiao

2022

Softw Syst Model

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MDSSED: A safety and security enhanced model-driven development approach for smart home apps

Zhuang

Qiao

2023

Information and Software Technology

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A security modeling and verification method of embedded software based on Z and MARTE

Cited by 8 publications

References 24 publications

A Machine Learning approach for analyzing and detecting Android malicious applications

A Machine Learning approach for analyzing and detecting Android malicious applications

MBIPV: a model-based approach for identifying privacy violations from software requirements

MDSSED: A safety and security enhanced model-driven development approach for smart home apps

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