FRRE - Producción de Investigación

Permanent URI for this communityhttp://48.217.138.120/handle/20.500.12272/498

Browse

Start date: 2020Subject: search.filters.subject.SDN

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Desarrollo de una arquitectura de ciberseguridad en redes IoT, aplicada a un ecosistema Zigbee basado en SDN
    (30° Congreso Argentino de Ciencias de la Computación - CACIC 2024, 2025-10-07) Scappini, Reinaldo; Bolatti, Diego; Gramajo, Sergio; Roa, Jorge; Montiel, Raúl
    Este trabajo propone el desarrollo de arquitectura de ciberseguridad para sistemas basados en IoT, mostrando un ejemplo aplicado a un entorno Zigbee. Para ello se presenta una arquitectura de ciberseguridad innovadora basada en SDN para proteger de manera efectiva las redes IoT. La propuesta centraliza la gestión de políticas de seguridad en un controlador SDN, permitiendo un control granular del tráfico a través de conmutadores OpenFlow. Al aprovechar parámetros de los dispositivos IoT, como identificadores únicos y niveles de batería, se establecen políticas de acceso y priorización personalizadas. La arquitectura se valida en un entorno real utilizando una red Zigbee, demostrando su eficacia en la detección y mitigación de amenazas. Los resultados obtenidos respaldan la viabilidad de esta solución para asegurar la creciente diversidad de dispositivos IoT y garantizar la privacidad de los datos.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Congestion control proposal in SDN with random early detection
    (2021-08-13) Lezcano Airaldi, Luis; Gramajo, Sergio; Scappini, Reinaldo; Bolatti, Diego
    The emerging technology of SDN (Software Defined Networks) separates the data control plane from the forwarding plane while maintaining a centralized control of the network management. The SDN features require new traffic engineering techniques that exploit the global (centralized) network view, and the status and features of traffic flows. Our purpose is to perform traffic engineering in an SDN architecture, using the OpenFlow protocol capabilities and the potential of the SDN controller to collect operational data of the entire network, such as topology, latency, buffer utilization and frame sizes of the controlled devices in order to implement QoS. Considering that the performance of the network is an essential component of Quality of Service (QoS), and congestion is the main factor that affects it, this paper explores a method to search for alternative paths based on data from switches using the Random Early Detection (RED) congestion control mechanism.
  • Thumbnail Image
    Item
    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