AndroZooOpen

Liu, Pei; Li, Li; Zhao, Yanjie; Sun, Xiaoyu; Grundy, John

doi:10.1145/3379597.3387503

Cited by 29 publications

(7 citation statements)

References 19 publications

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“…AndroZoo dataset is a collection of Android Applications from Google Play store and other sources [16]. Each app in this dataset has been analyzed by different Antivirus software to know which malware are.…”

Section: Android Datasetmentioning

confidence: 99%

Analysis of Markov Blanket Based Feature Ranking for Android Malware Detection

Yusfrizal,

Andrian Syahputra,

Yahya Tanjung

et al. 2024

j. of artif. intell. and eng. appl.

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The ubiquity of Android applications in our daily lives has brought forth an indispensable need for robust app security mechanisms. Malware-infested applications not only jeopardize user privacy but also compromise data integrity and overall device security. Detecting and mitigating malicious behavior within Android applications is becoming increasingly challenging due to the high-dimensional nature of the data. Moving forward, employing Machine Learning (ML) techniques to detect malware in Android apps has become the norm. High dimensional feature space poses several formidable challenges, including data scarcity, over fitting, and heightened computational demands. While dimensionality reduction techniques are a potential remedy, they often result in loss of crucial information essential for comprehending and identifying malicious behaviors accurately. Feature ranking based feature subset selection emerges as a promising alternative, as it allows us to retain the original feature space, ensuring precise representation of app behaviors. This article explores various Markov Blanket (MB) based feature ranking techniques, proposes a variant of Grow Shrink (GS) MB technique, GS++ which demonstrates enhanced performance within the context of Android malware detection.

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Section: Android Datasetmentioning

confidence: 99%

Analysis of Markov Blanket Based Feature Ranking for Android Malware Detection

Yusfrizal,

Andrian Syahputra,

Yahya Tanjung

et al. 2024

j. of artif. intell. and eng. appl.

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“…Since the conventional usage categories are summarized with manual eforts on a given set of apps, they may not be representative and thereby may not cover all possible cases. Therefore, in this work, we go one step deeper to further investigate the completeness of all the seven categories of conventional usages by applying our approach to another set of randomly selected 10,000 malware and 10,000 benign apps from AndroZoo [45]. We remind the readers that AndroZoo includes over 10 million Android apps that were collected from both the oicial Google Play store and several third-party app markets.…”

Section: Conventional Usage Analysismentioning

confidence: 99%

Demystifying Hidden Sensitive Operations in Android Apps

Sun

Chen

et al. 2023

ACM Trans. Softw. Eng. Methodol.

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Security of Android devices is now paramount, given their wide adoption among consumers. As researchers develop tools for statically or dynamically detecting suspicious apps, malware writers regularly update their attack mechanisms to hide malicious behavior implementation. This poses two problems to current research techniques: static analysis approaches, given their over-approximations, can report an overwhelming number of false alarms, while dynamic approaches will miss those behaviors that are hidden through evasion techniques. We propose in this work a static approach specifically targeted at highlighting hidden sensitive operations, mainly sensitive data flows. The prototype version of HiSenDroid has been evaluated on a large-scale dataset of thousands of malware and goodware samples on which it successfully revealed anti-analysis code snippets aiming at evading detection by dynamic analysis. We further experimentally show that, with FlowDroid, some of the hidden sensitive behaviors would eventually lead to private data leaks. Those leaks would have been hard to spot either manually among the large number of false positives reported by the state of the art static analyzers, or by dynamic tools. Overall, by putting the light on hidden sensitive operations, HiSenDroid helps security analysts in validating potential sensitive data operations, which would be previously unnoticed.

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“…In this study, we utilize AndroZooOpen [38], a dataset of 46,522 open-source Android apps spanning multiple categories. From this source dataset, we attempt to clone each project repository 1 .…”

Section: A Source Datasetmentioning

confidence: 99%

An Exploratory Study on the Occurrence of Self-Admitted Technical Debt in Android Apps

Wilder¹,

Riley²,

Watson³

et al. 2023

Preprint

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Technical debt describes situations where developers write less-than-optimal code to meet project milestones. However, this debt accumulation often results in future developer effort to live with or fix these quality issues. To better manage this debt, developers may document their sub-optimal code as comments in the code (i.e., self-admitted technical debt or SATD). While prior research has investigated the occurrence and characteristics of SATD, this research has primarily focused on non-mobile systems. With millions of mobile applications (apps) in multiple genres available for end-users, there is a lack of research on sub-optimal code developers intentionally implement in mobile apps.In this study, we examine the occurrence and characteristics of SATD in 15,614 open-source Android apps. Our findings show that even though such apps contain occurrences of SATD, the volume per app (a median of 4) is lower than in non-mobile systems, with most debt categorized as Code Debt. Additionally, we identify typical elements in an app that are prone to intentional sub-optimal implementations. We envision our findings supporting researchers and tool vendors with building tools and techniques to support app developers with app maintenance.

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AndroZooOpen

Cited by 29 publications

References 19 publications

Analysis of Markov Blanket Based Feature Ranking for Android Malware Detection

Analysis of Markov Blanket Based Feature Ranking for Android Malware Detection

Demystifying Hidden Sensitive Operations in Android Apps

An Exploratory Study on the Occurrence of Self-Admitted Technical Debt in Android Apps

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