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Carlos García Garino
Researcher at National University of Cuyo
Publications - 88
Citations - 1032
Carlos García Garino is an academic researcher from National University of Cuyo. The author has contributed to research in topics: Cloud computing & Autoscaling. The author has an hindex of 12, co-authored 88 publications receiving 832 citations. Previous affiliations of Carlos García Garino include National Scientific and Technical Research Council & Universidad de Mendoza.
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Proceedings ArticleDOI
An analysis of Recurrent Neural Networks for Botnet detection behavior
TL;DR: This work provides an analysis of the viability of Recurrent Neural Networks to detect the behavior of network traffic by modeling it as a sequence of states that change over time, which makes it a potential candidate for implementation and deployment on real-world scenarios.
Journal ArticleDOI
Automatic network intrusion detection: Current techniques and open issues
TL;DR: The present article surveys the most relevant works in the field of automatic network intrusion detection and considers several features required for truly deploying each one of the reviewed approaches.
Journal ArticleDOI
An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection
TL;DR: Experiments show that the use of the proposed autonomous labeling approach for autonomous labeling of normal traffic not only outperforms existing SVM alternatives but also, under some attack distributions, obtains improvements over SNORT itself.
Journal ArticleDOI
Software Survey: Distributed job scheduling based on Swarm Intelligence: A survey
TL;DR: This paper surveys SI-based job scheduling algorithms for bag-of-tasks applications (such as PSEs) on distributed computing environments, and uniformly compares them based on a derived comparison framework.
Balancing Throughput and Response Time in Online Scientific Clouds via Ant Colony Optimization
TL;DR: This work describes and evaluates a Cloud scheduler based on Ant Colony Optimization (ACO) and shows that the scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.