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DNS Threats Dataset

Authors: Palau, Franco; , Catania; Guerra; , Garcia; , Rigaki;

DNS Threats Dataset

Abstract

The dataset contains Normal, DGA and Tunneling domain names: i. the total number of normal domains are conformed by the Alexa top one million domains, 3,161 normal domains provided by the Bambenek Consulting feed, and another 177,017 normal domains; ii. the DGA domains were obtained from the repositories of DGA domains of Andrey Abakumov and John Bambenek, corresponding to 51 different malware families; iii. the DNS Tunneling consist of 8000 tunnel domains generated using a set of well known DNS tunneling tools under laboratory conditions: iodine, dnscat2 and dnsExfiltrator. The dataset is described in the paper: Palau, F., Catania, C., Guerra, J., García, S. J., & Rigaki, M. (2019). Detecting DNS threats: A deep learning model to rule them all. In XX Simposio Argentino de Inteligencia Artificial (ASAI 2019)-JAIIO 48 (Salta).

The paper where the dataset is discussed can be found at http://sedici.unlp.edu.ar/handle/10915/87859

Keywords

deep neural networks, network security, botnet

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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