Acteve++: An Improved Android Application Automatic Tester Based on Acteve

Qin, Jiawei; Zhang, Hua; Wang, Senmiao; Geng, Zhikui; Chen, Tengfei

doi:10.1109/access.2019.2902608

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…Because Android Monkey generates a random event stream, it is possible to test even in unpredictable situations, and it is possible to test without wasting human resources by controlling the frequency of occurrence, cycle of occurrence, and number of occurrences by the user. According to Choudhary et al [26], Android Monkey achieves higher code coverage than other tools such as SwiftHand [27] and ACTEve [28] and confirmed the highest efficiency. However, we could not overcome the limitation completely.…”

Section: Can Arbdroid Identify Hidden Sensitive Api Calls In Real-wor...mentioning

confidence: 90%

Deobfuscating Mobile Malware for Identifying Concealed Behaviors

Lee¹,

Jeon²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

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The smart phone market is continuously increasing and there are more than 6 billion of smart phone users worldwide with the aid of the 5G technology. Among them Android occupies 87% of the market share. Naturally, the widespread Android smartphones has drawn the attention of the attackers who implement and spread malware. Consequently, currently the number of malware targeting Android mobile phones is ever increasing. Therefore, it is a critical task to find and detect malicious behaviors of malware in a timely manner. However, unfortunately, attackers use a variety of obfuscation techniques for malware to evade or delay detection. When an obfuscation technique such as the class encryption is applied to a malicious application, we cannot obtain any information through a static analysis regarding its malicious behaviors. Hence, we need to rely on the manual, dynamic analysis to find concealed malicious behaviors from obfuscated malware. To avoid malware spreading out in larger scale, we need an automated deobfuscation approach that accurately deobfuscates obfuscated malware so that we can reveal hidden malicious behaviors. In this study, we introduce widely-used obfuscation techniques and propose an effective deobfuscation method, named ARBDroid, for automatically deobfuscating the string encryption, class encryption, and API hiding techniques. Our evaluation results clearly demonstrate that our approach can deobfuscate obfuscated applications based on dynamic analysis results.

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Section: Can Arbdroid Identify Hidden Sensitive Api Calls In Real-wor...mentioning

confidence: 90%

Deobfuscating Mobile Malware for Identifying Concealed Behaviors

Lee¹,

Jeon²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Acteve++ [34] is an automated testing tool for Android Apps. It is based on Acteve [35] but is improved to support input events and broadcast events in order to achieve higher coverage.…”

Section: Related Workmentioning

confidence: 99%

Smali+: An Operational Semantics for Low-Level Code Generated from Reverse Engineering Android Applications

et al. 2020

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Today, Android accounts for more than 80% of the global market share. Such a high rate makes Android applications an important topic that raises serious questions about its security, privacy, misbehavior and correctness. Application code analysis is obviously the most appropriate and natural means to address these issues. However, no analysis could be led with confidence in the absence of a solid formal foundation. In this paper, we propose a full-fledged formal approach to build the operational semantics of a given Android application by reverse-engineering its assembler-type code, called Smali. We call the new formal language Smali + . Its semantics consist of two parts. The first one models a single-threaded program, in which a set of main instructions is presented. The second one presents the semantics of a multi-threaded program which is an important feature in Android that has been glossed over in the-state-of-the-art works. All multi-threading essentials such as scheduling, threads communication and synchronization are considered in these semantics. The resulting semantics, forming Smali + , are intended to provide a formal basis for developing security enforcement, analysis and misbehaving detection techniques for Android applications.

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“…The Rico dataset is the de facto standard for UI design and layout generation [26], [27], [28], [29] in Android apps. We notice that if the Rico data is clustered properly, then we can use this clustered data for malware detection [30], [31], [32], UI generation [4], [26], and automated testing [21], [33], [34], [35]. However, there are no prior studies on activity clustering for the Rico dataset.…”

mentioning

confidence: 98%

An Empirical Study on Multimodal Activity Clustering of Android Applications

Choi

Seo²,

Han

2023

IEEE Access

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Recently, researchers have started looking at Android activities to improve user interface (UI) design. Since similar activities in Android have similar functional behaviors, activity clustering is a fundamental step toward efficient Android app development. Well-grouped activities are useful not only for UI design, but also for app design, development, and testing. However, there are no studies on activity clustering yet, and no activity dataset with labels and categories. The purpose of this study is to use the Rico dataset to know (i) whether the Rico dataset can be used for activity clustering, (ii) how useful activity attributes expressed in XML are for activity clustering, and (iii) how useful fusion with activity image and attributes is for activity clustering. We generate various activity latent vectors using a CNN autoencoder for the Rico dataset. Then, we produce a sequence-to-sequence latent vector from the semantic properties of the Rico dataset. Finally, by fusing the two models, we propose an activity clustering approach using multimodal learning. Since there are no labels in the dataset, we make 2000 labeled data for evaluation. The experimental results show that the activity clustering works well by fusing the semantic activity latent vector and the seq2seq latent vector. Especially, activity attributes such as component and position information are effective for activity clustering and help to boost the performance better than real activity images or Rico. Research findings on clustering and newly created labeled data can be a starting point for various studies on Android activity.

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Acteve++: An Improved Android Application Automatic Tester Based on Acteve

Cited by 7 publications

References 13 publications

Deobfuscating Mobile Malware for Identifying Concealed Behaviors

Deobfuscating Mobile Malware for Identifying Concealed Behaviors

Smali+: An Operational Semantics for Low-Level Code Generated from Reverse Engineering Android Applications

An Empirical Study on Multimodal Activity Clustering of Android Applications

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