AppIS: Protect Android Apps Against Runtime Repackaging Attacks

Song, Lina; Tang, Zhanyong; Li, Zhen; Gong, Xiaoqing; Liu, Chen; Fang, Dingyi; Wang, Zheng

doi:10.1109/icpads.2017.00015

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…Several studies have been carried out on Android permission requests [26], Android user privacy [27], attacks on Android and other mobile platforms [28,29], including malware attacks [30], synchronous channels attack [31], side-channel attacks [32], runtime repackaging attacks [33], and Man-in-the-Middle attacks [34] accuracy. This section of the paper provides a brief examination of some of the related works carried out.…”

Section: Permission-based Detection and Feature Extractionmentioning

confidence: 99%

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Ashawa

Morris

2021

JCP

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The evolution of mobile technology has increased correspondingly with the number of attacks on mobile devices. Malware attack on mobile devices is one of the top security challenges the mobile community faces daily. While malware classification and detection tools are being developed to fight malware infection, hackers keep deploying different infection strategies, including permissions usage. Among mobile platforms, Android is the most targeted by malware because of its open OS and popularity. Permissions is one of the major security techniques used by Android and other mobile platforms to control device resources and enhance access control. In this study, we used the t-Distribution stochastic neighbor embedding (t-SNE) and Self-Organizing Map techniques to produce a visualization method using exploratory factor plane analysis to visualize permissions correlation in Android applications. Two categories of datasets were used for this study: the benign and malicious datasets. Dataset was obtained from Contagio, VirusShare, VirusTotal, and Androzoo repositories. A total of 12,267 malicious and 10,837 benign applications with different categories were used. We demonstrate that our method can identify the correlation between permissions and classify Android applications based on their protection and threat level. Our results show that every permission has a threat level. This signifies those permissions with the same protection level have the same threat level.

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Section: Permission-based Detection and Feature Extractionmentioning

confidence: 99%

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Ashawa

Morris

2021

JCP

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“…RE techniques have been used with mobile applications for many purposes not just for detecting anti-patterns. Song et al (2017) used RE for improving the security of Android applications. While Zhou et al (2018) used the RE technique to detect logging classes and to remove logging calls and unnecessary instructions.…”

Section: Related Workmentioning

confidence: 99%

Reverse engineering approach for improving the quality of mobile applications

Elsayed

ElDahshan

El-Sharawy

et al. 2019

PeerJ Computer Science

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BackgroundPortable-devices applications (Android applications) are becoming complex software systems that must be developed quickly and continuously evolved to fit new user requirements and execution contexts. Applications must be produced rapidly and advance persistently in order to fit new client requirements and execution settings. However, catering to these imperatives may bring about poor outline decisions on design choices, known as anti-patterns, which may possibly corrupt programming quality and execution. Thus, the automatic detection of anti-patterns is a vital process that facilitates both maintenance and evolution tasks. Additionally, it guides developers to refactor their applications and consequently enhance their quality.MethodsWe proposed a general method to detect mobile applications’ anti-patterns that can detect both semantic and structural design anti-patterns. The proposed method is via reverse-engineering and ontology by using a UML modeling environment, an OWL ontology-based platform and ontology-driven conceptual modeling. We present and test a new method that generates the OWL ontology of mobile applications and analyzes the relationships among object-oriented anti-patterns and offer methods to resolve the anti-patterns by detecting and treating 15 different design’s semantic and structural anti-patterns that occurred in analyzing of 29 mobile applications. We choose 29 mobile applications randomly. Selecting a browser is not a criterion in this method because the proposed method is applied on a design level. We demonstrate a semantic integration method to reduce the incidence of anti-patterns using the ontology merging on mobile applications.ResultsThe proposed method detected 15 semantic and structural design anti-patterns which have appeared 1,262 times in a random sample of 29 mobile applications. The proposed method introduced a new classification of the anti-patterns divided into four groups. “The anti-patterns in the class group” is the most group that has the maximum occurrences of anti-patterns and “The anti-patterns in the operation group” is the smallest one that has the minimum occurrences of the anti-patterns which are detected by the proposed method. The results also showed the correlation between the selected tools which we used as Modelio, the Protégé platform, and the OLED editor of the OntoUML. The results showed that there was a high positive relation between Modelio and Protégé which implies that the combination between both increases the accuracy level of the detection of anti-patterns. In the evaluation and analyzing the suitable integration method, we applied the different methods on homogeneous mobile applications and found that using ontology increased the detection percentage approximately by 11.3% in addition to guaranteed consistency.

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“…RE techniques have been used with mobile applications for many purposes not just for detecting antipatterns. Song, L. et al (2017) used RE for improving the security of Android applications. While Zhou, X. et al (2018) used the RE technique to detect logging classes and to remove logging calls and unnecessary instructions.…”

Section: Related Workmentioning

confidence: 99%

Peer Review #2 of "Reverse engineering approach for improving the quality of mobile applications (v0.1)"

Dahlke¹

2019

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Background: Portable-devices applications (Android applications) are becoming complex software systems that must be developed quickly and evolve continuously to fit new user requirements and execution contexts. Applications must be produced rapidly and advance persistently in order to fit new client requirements and execution settings. However, catering to these imperatives may bring about poor outline decisions on design choices, known as anti-patterns, which may possibly corrupt programming quality and execution. Thus, the automatic detection of anti-patterns is a vital process that facilitates both maintenance and evolution tasks. Additionally, it guides developers to refactor their applications and consequently enhance their quality. Methods: We proposed a general method to detect mobile applications' anti-patterns that can detect both semantic and structural design anti-patterns. The proposed method is via reverse-engineering and Ontology by using UML modeling environment, OWL ontology-based platform and Ontology-driven conceptual modeling. We present and test a new method that generates OWL Ontology of mobile applications and analyzes the relationships among object-oriented anti-patterns and offer methods to resolve the anti-patterns by detecting and treating 15 different design's semantic and structural antipatterns that occurred in analyzing of 29 mobile applications. We choose 29 mobile applications randomly. Selecting a browser is not a criterion in this method, because the proposed method is applied on a design level. We demonstrate a semantic integration method to reduce the incidence of antipatterns using the Ontology merging on mobile applications. Results: The proposed method detected 15 semantic and structural design anti-patterns which have appeared 1262 times in a random sample of 29 mobile applications. The proposed method introduced a new classification of the anti-patterns divided into four groups. ''The anti-patterns in the class group'' is the most group that has the maximum occurrences of anti-patterns and ''The anti-patterns in the operation group'' is the smallest one that has the minimum occurrences of the anti-patterns which are detected by the proposed method. The results also showed the correlation between the selected tools which we used as Modelio, Protégé platform and OLED editor of the OntoUML. The results showed that there was a high positive relation between Modelio and Protégé which implies that the combination between both increases the accuracy level of the detection of anti-patterns. In the evaluation and analyzing the suitable integration method, we applied the different methods on homogeneous mobile applications and found that using Ontology increased the detection percentage approximately by 11.3% in addition to guaranteed consistency.

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AppIS: Protect Android Apps Against Runtime Repackaging Attacks

Cited by 15 publications

References 18 publications

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Reverse engineering approach for improving the quality of mobile applications

Peer Review #2 of "Reverse engineering approach for improving the quality of mobile applications (v0.1)"

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