We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices.
- Authors
Michelena, Álvaro; Ordás, María Teresa García; Aveleira-Mata, José; Blanco, David Yeregui Marcos del; Díaz, Míriam Timiraos; Zayas-Gato, Francisco; Jove, Esteban; Casteleiro-Roca, José-Luis; Quintián, Héctor; Alaiz-Moretón, Héctor; Calvo-Rolle, José Luis
- Abstract
This paper aims to enhance security in IoT device networks through a visual tool that utilizes three projection techniques, including Beta Hebbian Learning (BHL), t-distributed Stochastic Neighbor Embedding (t-SNE) and ISOMAP, in order to facilitate the identification of network attacks by human experts. This work research begins with the creation of a testing environment with IoT devices and web clients, simulating attacks over Message Queuing Telemetry Transport (MQTT) for recording all relevant traffic information. The unsupervised algorithms chosen provide a set of projections that enable human experts to visually identify most attacks in real-time, making it a powerful tool that can be implemented in IoT environments easily.
- Publication
Logic Journal of the IGPL, 2024, Vol 32, Issue 2, p352
- ISSN
1367-0751
- Publication type
Article
- DOI
10.1093/jigpal/jzae013