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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There are two datasets: Drebin4000 and AMD6000.


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.


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.


MIRAGE-2019 is a human-generated dataset for mobile traffic analysis with associated ground-truth, having the goal of advancing the state-of-the-art in mobile app traffic analysis.

MIRAGE-2019 takes into consideration the traffic generated by more than 280 experimenters using 40 mobile apps via 3 devices.

APP LIST reports the details on the apps contained in the two versions of the dataset.

If you are using MIRAGE-2019 human-generated dataset for scientific papers, academic lectures, project reports, or technical documents, please help us increasing its impact by citing the following reference:

Giuseppe Aceto, Domenico Ciuonzo, Antonio Montieri, Valerio Persico and Antonio Pescapè,"MIRAGE: Mobile-app Traffic Capture and Ground-truth Creation",4th IEEE International Conference on Computing, Communications and Security (ICCCS 2019), October 2019, Rome (Italy).



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 ( The dataset is created using an artificial traffic induction (between the phone and the access point) approach to enable useful and meaningful RSS value