Percorrer por autor "Mosse, Daniel"
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- LEAF: Improving handoff flexibility of IEEE 802.11 networks with an SDN-based virtual access point frameworkPublication . Vieira, Juan Lucas; Mosse, Daniel; Passos, DiegoMobile devices’ popularization has brought several new applications to communication networks. As we move into an increasingly denser scenario, problems such as collisions between transmissions and unbalanced load become more pronounced. Moreover, while station-based handoff is inefficient to reduce these issues, network-wide handover decisions might provide better network resource management. This paper proposes LEAF, an access point virtualization solution based on Software Defined Networking to enable station (STA) handover conducted by the network, based on a global scope. Unlike other solutions in the literature, our proposal fully supports multichannel migrations through the IEEE 802.11h Channel Switch Announcement without restricting the channel utilization by the access points. To demonstrate the feasibility of such an approach, we present experimental data regarding the behavior of several different devices in face of this mechanism. We also evaluate our complete virtualization solution, which reveals that the handoff of STAs did not lead to significant packet losses or delays in STAs’ connections, while providing a foundation to improve network’s self-management and flexibility, allowing association control and load balancing tasks to be executed on top of our solution.
- Towards feature engineering for intrusion detection in IEC-61850 communication networksPublication . Quincozes, Vagner; Ereno Quincozes, Silvio; Passos, Diego; Albuquerque, Célio; Mosse, DanielDigital electrical substations are fundamental in providing a reliable basis for smart grids. However, the deployment of the IEC-61850 standards for communication between intelligent electronic devices (IEDs) brings new security challenges. Intrusion detection systems (IDSs) play a vital role in ensuring the proper function of digital substations services. However, the current literature lacks efficient IDS solutions for certain classes of attacks, such as the masquerade attack. In this work, we propose the extraction and correlation of relevant multi-layer information through a feature engineering process to enable the deployment of machine learning-based IDSs in digital substations. Our results demonstrate that the proposed solution can detect attacks that are considered challenging in the literature, attaining an F1-score of up to 95.6% in the evaluated scenarios.