@conference {114, title = {Application of a Bayesian semi-supervised Learning Strategy to Network Intrution Detection}, booktitle = {Proceedings of ASAI 2010, Argentinean Symposioum of Artificial Intelligence}, year = {2010}, publisher = {SADIO}, organization = {SADIO}, address = {Buenos Aires}, author = {Catania, C. A. and Garcia Garino, C. and Bromberg, F.} } @conference {179, title = {An Autonomous labeling approach to SVM algorithms for network traffic anomaly detection}, booktitle = {Argentine Symposium of Artificial Intelligence (ASAI). Jornadas Argentinas de Inform{\'a}tica. Mar del Plata, Argentina.}, year = {2009}, month = {08/2009}, publisher = {Sociedad Argentina de Inform{\'a}tica}, organization = {Sociedad Argentina de Inform{\'a}tica}, address = {Mar del Plata, Argentina}, abstract = {

In the past years, several support vector machines anomaly detection approaches have been proposed in the network intrusion detetion field. The main advantage of these approaches is that they\  can\  characterize normal traffic\  when trained using a data set\  containing not only normal traffic\  but also possible attacks. Unfortunately, these algorithms seem to be accurate only when the normal traffic\  vastly outnumbers the numbers of attacks or anomalies present in the dataset. This work presents an approach for autonomous labeling of normal traffic\ \  as a way of dealing with situations where\  class distributions do not present the required unbalance. The autonomous labeling process is made by SNORT, a misuse-based intrusion detection system. Experiments\  conducted on the 1998 DARPA dataset show the proposed autonomous labeling approach not only outperforms existing SVM alternatives but also obtains signicant improvement over SNORT itself.

}, author = {Brombereg, Facundo and Catania, Carlos A. and Garcia Garino, Carlos} } @conference {223, title = {A demo of the PEACH IoT-based frost event prediction system for precision agriculture}, booktitle = {Sensing, Communication, and Networking (SECON), 2016 13th Annual IEEE International Conference on}, year = {2016}, publisher = {IEEE}, organization = {IEEE}, doi = {10.1109/SAHCN.2016.7732963}, url = {https://hal.inria.fr/hal-01311527/document}, author = {Brun-Laguna, Keoma and Diedrichs, Ana Laura and Chaar, Javier Emilio and Dujovne, Diego and Taffernaberry, Juan Carlos and Mercado, Gustavo and Watteyne, Thomas} } @proceedings {257, title = {Multi species weed detection with Retinanet one-step network in a maize field}, journal = {Precision agriculture{\textquoteright}21}, year = {2021}, month = {06/2021}, pages = {2223{\textendash}2228}, publisher = {Wageningen Academic Publishers}, address = {Budapest, Hungary}, abstract = {

Weed density and composition are not uniform throughout the field, nevertheless, the conventional approach is to carry out a uniform application. Object Detection Networks have already arrived in agricultural applications that can be used for weed management. The current study developed a detection and classification of weeds system in a one-step procedure using RetinaNet Object Detection Network. The procedure was based on identifying Solanum nigrum L., Cyperus rotundus L. and Echinochloa crus-galli L. and two growth stages both for a broadleaf species (S. nigrum) as well as narrow-leaved species (C. rotundus) in a maize field. The predictions were evaluated by mAP metric. The result obtained was 0.88 with values between 0.98 and 0.75 depending on the class.

}, keywords = {deep learning, object detection networks, RetinaNet, site-specific weed management}, isbn = {978-90-8686-916-9}, issn = {978-90-8686-363-1}, doi = {https://doi.org/10.3920/978-90-8686-916-9}, url = {https://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-916-9}, author = {Correa, JM L{\'o}pez and Todeschini, M and P{\'e}rez, DS and Karouta, J and Bromberg, F and Ribeiro, A and And{\'u}jar, D} } @article {113, title = {An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection}, journal = {Expert Systems with Applications}, volume = {39}, year = {2012}, pages = {1822{\textendash}1829}, author = {Catania, Carlos A and Bromberg, Facundo and Garino, Carlos Garc{\'\i}a} } @article {224, title = {Peach: Predicting frost events in peach orchards using iot technology}, journal = {EAI Endorsed Transactions on the Internet of Things}, year = {2016}, abstract = {

In 2013, 85\% of the peach production in the Mendoza region (Argentina) was lost because of frost. In a couple of hours, farmers can lose everything. Handling a frost event is possible, but it is hard to predict when it is going to happen. The goal of the PEACH project is to predict frost events by analyzing measurements from sensors deployed around an orchard. This article provides an in-depth description of a complete solution we designed and deployed: the low-power wireless network and the back-end system. The low-power wireless network is composed entirely of commercial o -the-shelf devices. We develop a methodology for deploying the network and present the open-source tools to assist with the deployment and to monitor the network. The deployed low-power wireless mesh network is 100\% reliable, with end-to-end latency below 2 s, and over 3 years of battery lifetime. This article discusses how the technology used is the right one for precision agriculture applications.

}, doi = {http://dx.doi.org/10.4108/eai.1-12-2016.151711}, author = {Watteyne, Thomas and Diedrichs, Ana Laura and Brun-Laguna, Keoma and Chaar, Javier Emilio and Dujovne, Diego and Taffernaberry, Juan Carlos and Mercado, Gustavo} }