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Author: search.filters.author.Todt, CarolinaSubject: search.filters.subject.IoT

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    Network traffic monitor for IDS in IoT
    (2022-08-05) Bolatti, Diego; Todt, Carolina; Scappini, Reinaldo; Gramajo, Sergio
    As network services and IoT technologies rapidly evolve, in literature there are many anomalies detection proposals based on datasets to deal with cybersecurity threats. Most of this proposal uses structured data classification and they can recognize with a certain degree of accuracy whether a type of traffic is “anomalous” or not. Even what kind of anomaly it has. Nevertheless, previous works do not clearly indicate the technical methodology to set up the data gathered scenarios. As a main contribution, we are going to show a detailed deployment IoT traffic monitor ready for intelligent network traffic classification. Monitoring and sniffers are an essential concept in network management as it helps network operators to determine the network behavior and status of its components. Anomaly detection also depends on monitoring for decision-making. Thus, this paper will describe the creation of a portable network traffic monitor for IoT using Docker container and bridge networking with SDN.
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    Baseline text for proposed new work item to develop a technical report of intelligent anomaly detection system for IoT
    (INTERNATIONAL TELECOMMUNICATION UNION TELECOMMUNICATION STANDARDIZATION SECTOR, 2020-07-07) Bolatti, Diego; Karanik, Marcelo; Scappini, Reinaldo; Todt, Carolina
    This document presents an update of the technical report on Intelligent Anomaly Detection System for IoT (Y.STR-IADIoT) and seeks agreement that it be the basis of future work.
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    Intelligent anomaly detection system for IoT
    (2021-05-22) Bolatti, Diego; Karanik, Marcelo; Todt, Carolina; Scappini, Reinaldo; Gramajo, Sergio
    The growing use of the Internet of Things (IoT) in different areas implies a proportional growth in threats and attacks on end devices. To solve this problem, the IoT systems must be equipped with an anomaly detection system (ADS). This work introduces the design of a hybrid ADS based on the Software-Defined Network (SDN) architecture, which combines the rule-based and Machine Learning-based detection technique. Whereas the rule-based approach is used to detect known attacks with the help of rules defined by security experts. And the Machine Learning approach is used to detect unknown attacks with the help of Artificial Intelligence techniques
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    Modelo de seguridad IoT
    (2019-10-14) Monzón, Germán; Todt, Carolina; Bolatti, Diego; Gramajo, Sergio; Scappini, Reinaldo
    El Internet de las cosas (IoT) no solo conectará computadoras y dispositivos móviles, sino que también interconectará edificios, hogares y ciudades inteligentes, así como redes eléctricas, redes de agua y gas, automóviles, aviones, etc. IoT liderará al desarrollo de una amplia gama de servicios de información avanzados que deben procesarse en tiempo real. Sin embargo, las infraestructuras y servicios de IoT presentan grandes desafíos de seguridad debido al aumento significativo de la superficie de ataque, la complejidad, la heterogeneidad y la cantidad de recursos. En este documento, presentamos un marco de seguridad de IoT para infraestructuras inteligentes como Smart Homes, Smart Grid, Smart Connected Health y otras aplicaciones basadas en IoT.