Browsing by Author "Gutiérrez Gaitán, Miguel José"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Adaptive Intrusion Mitigation in Software-Defined Vehicles Using Deep Reinforcement Learning
    (2025) Kurunathan, Harrison; Ismail Ali, Hazem; Javanmardi, Gowhar; Eldefrawy, Mohamed; Gutiérrez Gaitán, Miguel José; Robles, Ramiro; Yomsi, Patrick; Tovar, Eduardo
    Software-defined vehicles (SDVs) leverage vehicle-to-everything (V2X) communication to enable advanced connectivity and autonomous driving capabilities. However, this increased interconnectivity also exposes them to cyber threats such as spoofing, denial-of-service attacks, and data manipulation, making intrusion detection systems (IDS) essential for ensuring SDV security and reliability. In this work, we propose a novel intrusion mitigation approach that integrates Advantage Actor-Critic (A2C) reinforcement learning with a Long Short-Term Memory (LSTM) network to detect anomalies and intrusions in V2X communications. The LSTM component captures temporal dependencies in V2X data, enhancing the model's ability to identify emerging attack patterns, while the A2C framework dynamically adjusts defensive actions, including flagging, blocking or monitoring traffic, based on evolving threat levels. Experimental results demonstrate the model's effectiveness, achieving high detection accuracy and sensitivity. Additionally, we analyze how the system adapts over time, becoming more confident in its decision-making and optimizing security enforcement. This work enhances SDV cybersecurity by introducing a learning-based adaptive intrusion response system aiming at mitigating threats in highly dynamic vehicular networks.
  • Thumbnail Image
    Item
    Channel Access Probability in Unslotted IEEE 802.15.4 csma Under Correlated Clear Channel Assessments
    (2025) Espinoza Henríquez, Miguel Antonio; Oberli Graf, Christian Robert; Gutiérrez Gaitán, Miguel José; Maass Martínez, Alejandro Ignacio
    Correlation between successive clear channel assessments in unslotted carrier sense multiple access with collision avoidance (CSMA/CA) is commonly assumed in the literature to be negligible for modeling the medium access control algorithm. However, in previous research, the authors found this assumption to be questionable under certain realistic conditions. In this paper, the correlation between successive clear channel assessments is studied and its effect on the probability and delay of accessing the medium by a node following the IEEE 802.15.4 Standard is modeled for the unslotted CSMA/CA mode. The results obtained are used for evaluating the delay probability distributions of the three outcome events of the CSMA/CA algorithm: frame transfer success, channel access failure and frame transfer failure. For scenarios in which the duration of contentions is similar to, or shorter than, the duration of the frames propagating in the air, the correlation effect is strong, making classical models to yield results that deviate from a realistic scenario. The theoretical results are validated by simulations. It is shown that neglecting correlation between successive clear channel assessments leads to overestimating the expected delay of frame transfer success and channel access failure by up to 15%. The results also reveal that the probability of frame transfer success can be overestimated by up to 30%, the probability of frame transfer failure by up to 80% and the probability of channel access failure is underestimated by up to a factor 6 when clear channel assessment correlation is ignored.
  • Thumbnail Image
    Item
    Q-light: Q-learning enabled VLC network routing
    (ACM, 2024) Kurunathan, Harrison; Robles, Ramiro; Gutiérrez Gaitán, Miguel José; Ravichandran, Indhumathi; Tovar, Eduardo
    Visible Light Communication (VLC) is an emerging technology that uses light sources such as light-emitting diodes (LEDs) and lasers for data transmission. This is enabled by the IEEE 802.15.7 protocol, which supports deterministic communication through its guaranteed timeslot mechanism. VLC networks provide several advantages in terms of high bandwidth, security, and immunity to electromagnetic interference. Most of the works in VLC are centred around point-to-point communication and do not necessarily provide emphasis on the challenges due to blockage, deafness, or hidden node problems. In this work, we present a machine learning (ML)-optimized routing protocol (Q Light) that improves network throughput and reliability through the selection of efficient routes for the IEEE 802.15.7 protocol.