CANDYMAN: Classifying Android malware families by modelling dynamic traces with Markov chains

Author:

Martín Alejandro,Rodríguez-Fernández Víctor,Camacho David

Funder

Comunidad Autónoma de Madrid

Spanish Ministry of Science and Education and Competitivity

European Regional Development Fund

Justice Programme of the European Union

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference50 articles.

1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al., 2016. Tensorflow: Large-scale machine learning on heterogeneous distributed systems, arXiv preprint arXiv:1603.04467.

2. Continuous-time Markov Chains: An Applications-Oriented Approach;Anderson,2012

3. Drebin: effective and explainable detection of android malware in your pocket;Arp,2014

4. High-order extensions of the double chain Markov model;Berchtold;Stoch. Models,2002

5. Crowdroid: behavior-based malware detection system for android;Burguera,2011

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