Android

This dataset aims to provide researchers with the essential information to aid in the development and improvement surrounding system call pattern detection for crypto ransomware on Android.

Our dataset provides two sets of extracted and formatted system call logs. The first set consists of system call logs collected from 213 crypto ransomware and the second set consist of 502 benign Android applications.

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291 Views

The update of Android OS constantly brings users various new features and enhances system security. On the other hand, the system and API modifications with the update may introduce the app compatibility issue. The app's SDK version may not align with the Android OS version, making apps not work adequately. This condition will inevitably damage the Android ecosystem. Thus, while developing Android OS, Google considered and deployed compatibility support. The software engineering research community also noticed the Android compatibility issue and conducted some investigations.

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84 Views

The Android Malware Detection Dataset consists of different flavors and diversity of malware APK files that can be used for malware detection using machine learning. It is my research work and if you use this dataset please cite my work in your research papers.

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758 Views

There are two datasets: Drebin4000 and AMD6000.

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3870 Views

We compared the performances of an LwM2M device management protocol implementation and FIWARE’s Ultralight 2.0. In addition to demonstrating the viability of the proposed approach, the obtained results point to mixed advantages/disadvantages of one protocol over the other.

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405 Views

Network traffic analysis, i.e. the umbrella of procedures for distilling information from network traffic, represents the enabler for highly-valuable profiling information, other than being the workhorse for several key network management tasks. While it is currently being revolutionized in its nature by the rising share of traffic generated by mobile and hand-held devices, existing design solutions are mainly evaluated on private traffic traces, and only a few public datasets are available, thus clearly limiting repeatability and further advances on the topic.

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1788 Views

The dataset is an extensive collection of labeled high-frequency Wi-Fi Radio Signal Strength (RSS) measurements corresponding to multiple hand gestures made near a smartphone under different spatial and data traffic scenarios. We open source the software code and an Android app (Winiff) to create this dataset, which is available at Github (https://github.com/mohaseeb/wisture). The dataset is created using an artificial traffic induction (between the phone and the access point) approach to enable useful and meaningful RSS value

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1126 Views