Autoencoder Latent Space Influence on IoT MQTT Attack Classification

Garcí­a-Ordás, María Teresa; Aveleira‐Mata, José; Casteleiro‐Roca, José‐Luis; Calvo-Rolle, José Luís; Benavides, Carmen; Aláiz‐Moretón, Héctor

doi:10.1007/978-3-030-62365-4_27

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Therefore the dataset contains the overall traffic of a network Alaiz‐Moreton et al (2019). This dataset was used to achieve good results implementing auto‐encoder techniques García‐Ordás et al (2020). In other previous works, a multi‐classification approach for Intrusion, DoS and MitM attacks was performed, taking only the most relevant features using previously a entropy function.…”

Section: Introductionmentioning

confidence: 99%

A novel intelligent approach for man‐in‐the‐middle attacks detection over internet of things environments based on message queuing telemetry transport

et al. 2023

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One of the most common attacks is man‐in‐the‐middle (MitM) which, due to its complex behaviour, is difficult to detect by traditional cyber‐attack detection systems. MitM attacks on internet of things systems take advantage of special features of the protocols and cause system disruptions, making them invisible to legitimate elements. In this work, an intrusion detection system (IDS), where intelligent models can be deployed, is the approach to detect this type of attack considering network alterations. Therefore, this paper presents a novel method to develop the intelligent model used by the IDS, being this method based on a hybrid process. The first stage of the process implements a feature extraction method, while the second one applies different supervised classification techniques, both over a message queuing telemetry transport (MQTT) dataset compiled by authors in previous works. The contribution shows excellent performance for any compared classification methods. Likewise, the best results are obtained using the method with the highest computational cost. Thanks to this, a functional IDS will be able to prevent MQTT attacks.

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Section: Introductionmentioning

confidence: 99%

A novel intelligent approach for man‐in‐the‐middle attacks detection over internet of things environments based on message queuing telemetry transport

et al. 2023

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Beta Hebbian Learning for Intrusion Detection in Networks of IoT Devices

Michelena

García-Ordás

Blanco

et al. 2021

14th International Conference on Computational Intelligence in Security for Information Systems and 12th International Conferen

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Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices

Michelena,

García Ordás,

Aveleira-Mata

et al. 2024

Logic Journal of the IGPL

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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.

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Autoencoder Latent Space Influence on IoT MQTT Attack Classification

Cited by 3 publications

References 13 publications

A novel intelligent approach for man‐in‐the‐middle attacks detection over internet of things environments based on message queuing telemetry transport

A novel intelligent approach for man‐in‐the‐middle attacks detection over internet of things environments based on message queuing telemetry transport

Beta Hebbian Learning for Intrusion Detection in Networks of IoT Devices

Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices

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