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DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling
TLDR
DroidCat, a novel dynamic app classification technique, to complement existing approaches using a diverse set of dynamic features based on method calls and inter-component communication (ICC) Intents without involving permission, app resources, or system calls while fully handling reflection, achieves superior robustness than static approaches as well as dynamic approaches relying on system calls.
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Assessing and Improving Malware Detection Sustainability through App Evolution Studies
  • Haipeng Cai
  • Computer Science
    ACM Trans. Softw. Eng. Methodol.
  • 4 March 2020
TLDR
The main takeaway, which also explains the superiority of DroidSpan is that the use of features consistently differentiating malware from benign apps over time is essential for sustainable learning-based malware detection, and that these features can be learned from studies on app evolution.
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On the Deterioration of Learning-Based Malware Detectors for Android
TLDR
The hypothesis that existing Android malware detectors do deteriorate largely and rapidly over time is confirmed and a new classification approach is proposed that is built on the results of a longitudinal characterization study of Android apps with a focus on their dynamic behaviors is proposed.
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Automated Detection and Repair of Incompatible Uses of Runtime Permissions in Android Apps
TLDR
A novel analysis is presented that detects the incompatible permission uses in a given app and repairs them when found, hence automatically adapting the app to the runtime permission model.
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Understanding Application Behaviours for Android Security: A Systematic Characterization
TLDR
This study presents the first dynamic characterization study of Android applications that targets such a broad understanding of Android application run-time characteristics and their security implications and discusses the security implications to secure development and effective security defense of modern Android applications.
Exploring how deprecated Python library APIs are (not) handled
In this paper, we present the first exploratory study of deprecated Python library APIs to understand the status quo of API deprecation in the realm of Python libraries. Specifically, we aim to…
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MR-Droid: A Scalable and Prioritized Analysis of Inter-App Communication Risks
TLDR
MR-Droid is presented, a MapReduce-based computing framework for accurate and scalable inter-app ICC analysis in Android and leverages the ICC graph to provide contexts for inter- app communications to produce precise alerts and prioritize risk assessments.
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Effects of Stereo and Screen Size on the Legibility of Three-Dimensional Streamtube Visualization
TLDR
It is found the task completion time was not improved by the use of the larger display and that performance accuracy was hurt rather than helped by the introduction of stereo in the study with dense DMRI data.
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Diver: precise dynamic impact analysis using dependence-based trace pruning
TLDR
This work presents a novel dynamic impact analysis called DIVER that exploits static dependencies to identify runtime impacts much more precisely without reducing safety and at acceptable costs.
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Exploiting Parts-of-Speech for effective automated requirements traceability
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