Herramienta de disección de tramas para protocolos IoT

  1. Narciandi-Rodríguez, Diego 1
  2. Aveleira-Mata, Jose 1
  3. Merayo Corcoba, Alicia 1
  4. Rubiños, Manuel 2
  5. Arcano-Bea, Paula 2
  6. Alaiz-Moretón, Héctor 1
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

  2. 2 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Revista:
Jornadas de Automática
  1. Cruz Martín, Ana María (coord.)
  2. Arévalo Espejo, V. (coord.)
  3. Fernández Lozano, Juan Jesús (coord.)

ISSN: 3045-4093

Ano de publicación: 2024

Número: 45

Tipo: Artigo

DOI: 10.17979/JA-CEA.2024.45.10804 DIALNET GOOGLE SCHOLAR lock_openAcceso aberto editor

Resumo

In recent years, the emergence and use of IoT (Internet of Things) devices, which stand out for their use of light weight protocols due to their low computational load, has led to the emergence of new attack vectors in systems with IoT devices.This is why it is necessary to train and develop machine learning models from real data, which are implemented in intrusion detection systems (IDS). This is where datasets come in, which make this activity possible thanks to the effective developmentof these models. This paper presents the development of a frame dissector that facilitates the generation of specific datasets for the different existing IoT protocols that are useful to create machine learning models from them.

Referencias bibliográficas

  • Alaiz-Moreton, H., Aveleira-Mata, J., Ondicol-Garcia, J., Mu ̃noz-Casta ̃neda, A. L., Garc ́ıa, I., Benavides, C., 2019. Multiclass classification procedure for detecting attacks on mqtt-iot protocol. Complexity 2019. DOI: 10.1155/2019/6516253 DOI: https://doi.org/10.1155/2019/6516253
  • Chatzoglou, E., Kambourakis, G., Kolias, C., 2021. Empirical evaluation of attacks against ieee 802.11 enterprise networks: The awid3 dataset. IEEE Access 9, 34188–34205. DOI: 10.1109/ACCESS.2021.3061609 DOI: https://doi.org/10.1109/ACCESS.2021.3061609
  • Hanan, H., Ethan, B., Miroslav, B., Robert, A., Christos, T., Xavier, B., 2020. Mqtt-iot-ids2020 dataset — papers with code. URL: https://paperswithcode.com/dataset/mqtt-iot-ids2020
  • Ibrahim, Z. A., Razali, R. A., Ismail, S. A., Azhar, I. H. K., Rahim, F. A., Azilan, A. M. A., 2022. A review of machine learning botnet detection techniques based on network traffic log. 2022 IEEE International Conference on Computing, ICOCO 2022, 204–209. DOI: 10.1109/ICOCO56118.2022.10031803 DOI: https://doi.org/10.1109/ICOCO56118.2022.10031803
  • Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., 2019. Survey of intrusion detection systems: techniques, datasets and challenges. The 14th International Conference on Ambient Systems, Networks and Technologies (ANT), March 15-17, 2023, Leuven, Belgium. DOI: 10.1186/s42400-019-0038- DOI: https://doi.org/10.1186/s42400-019-0038-7
  • Liao, H. J., Lin, C. H. R., Lin, Y. C., Tung, K. Y., 1 2013. Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications 36, 16–24. DOI: 10.1016/J.JNCA.2012.09.004 DOI: https://doi.org/10.1016/j.jnca.2012.09.004
  • Michelena, A., Díaz-Longueira, A., Timiraos, M., Jove, E., Aveleira-Mata, J., García-Rodiguez, I., García-Ordás, M. T., Calvo-Rolle, J. L., Alaiz-Moretón, H., 2023. One-class reconstruction methods for categorizing dos attacks on coap. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 14001 LNAI, 3–14. DOI: 10.1007/978-3-031-40725-3_1 DOI: https://doi.org/10.1007/978-3-031-40725-3_1
  • Mika, 2024. Creating a wireshark dissector in lua - part 1 (the basics) — mika’s tech blog. URL: https://mika-s.github.io/wireshark/lua/dissector/2017/11/04/creating-a-wireshark-dissector-in-lua-1.html
  • Moustafa, N., 2017. Designing an online and reliable statistical anomaly detection framework for dealing with large high-speed network traffic. DOI: 10.26190/UNSWORKS/3298
  • MQTT UAD, 2019. Mqtt uad: Mqtt under attack dataset. a public dataset for the detection of attacks in iot networks using mqtt. URL: https://figshare.com/s/2036c5c56ce6a3fc1191
  • Naidu, G. A., Kumar, J., 2019. Wireless protocols: Wi-fi son, bluetooth, zigbee, z-wave, and wi-fi. Lecture Notes in Networks and Systems 65, 229–239. URL: https://www.researchgate.net/publication/330927333_ DOI: https://doi.org/10.1007/978-981-13-3765-9_24
  • Otoum, Y., Nayak, A., 123. As-ids: Anomaly and signature based ids for the internet of things keywords internet of things (iot) security · anomaly-based ids · signature-based ids · deep q-learning · lightweight neural network (lightnet). Journal of Network and Systems Management 29, 23. DOI: 10.1007/s10922-021-09589-6 DOI: https://doi.org/10.1007/s10922-021-09589-6
  • Rizos, A., Bastos, D., Saracino, A., Martinelli, F., 2020. Distributed ucon in coap and mqtt protocols. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11980 LNCS, 35–52. DOI: 10.1007/978-3-030-42048-2_3 DOI: https://doi.org/10.1007/978-3-030-42048-2_3
  • Security group CNR-IEIIT, N., 2021. Mqttset. URL: https://www.kaggle.com/datasets/cnrieiit/mqttset
  • Shelby, Z., Hartke, K., Bormann, C., 6 2014. The constrained application pro-tocol (coap). URL:https://www.rfc-editor.org/info/rfc7252 DOI:10.17487/RFC7252 DOI: https://doi.org/10.17487/rfc7252
  • Singh, G., Khare, N., 2022. A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques. International Journal of Computers and Applications 44, 659–669. DOI: 10.1080/1206212X.2021.1885150 DOI: https://doi.org/10.1080/1206212X.2021.1885150
  • Timiraos, M., Michelena, Á., Díaz-Longueira, A., Jove, E., Aveleira-Mata,J., García-Rodiguez, I., Bayón-Gutiérrez, M., Alaiz-Moretón, H., Calvo-Rolle, J. L., 2023. Categorization of coap dos attack based on one-classboundary methods. In: García Bringas, P., Pérez García, H., Martínez de Pisón, F. J., Martínez ́Alvarez, F., Troncoso Lora, A., Herrero, ́A., Calvo Rolle, J. L., Quintián, H., Corchado, E. (Eds.), 18th International Con-ference on Soft Computing Models in Industrial and Environmental Appli-cations (SOCO 2023). Springer Nature Switzerland, Cham, pp. 112–121 DOI: https://doi.org/10.1007/978-3-031-42529-5_11
  • U. of California, 1999. Kdd cup 1999 data. URL: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
  • Wireless_Protocols_Wi-Fi_SON_Bluetooth_ZigBee_Z-Wave_and_Wi-Fi DOI: 10.1007/978-981-13-3765-9 24 DOI: https://doi.org/10.1007/978-981-13-3765-9
  • Wireshark, 2024. Wireshark · display filter reference: Index. URL: https://www.wireshark.org/docs/dfref/
Fonte de datos: Dialnet