Influence of autoencoder latent space on classifying IoT CoAP attacks

  1. García-Ordás, María Teresa 3
  2. Aveleira-Mata, Jose 1
  3. García-Rodrígez, Isaías 2
  4. Luis Casteleiro-Roca, José 5
  5. Bayón-Gutiérrez, Martín 6
  6. Alaiz-Moretón, Héctor 4
  1. 1 Research Institute of Applied Sciences in Cybersecurity (RIASC) MIC , University of León, 24071 León, Spain, jose.aveleira@unileon.es
  2. 2 Department of Electrical and Systems Engineering , University of León, Escuela de Ingenierías, Campus de Vegazana, 24071 León, Spain, igarr@unileon.es
  3. 3 Department of Electrical and Systems Engineering , University of León, Escuela de Ingenierías, Campus de Vegazana, 24071 León, Spain, mgaro@unileon.es
  4. 4 Research Institute of Applied Sciences in Cybersecurity (RIASC) MIC , University of León, 24071 León, Spain, hector.moreton@unileon.es
  5. 5 Department of Industrial Engineering , University of A Coruña, CTC, CITIC Avda. 19 de febrero s/n, 15405, Ferrol, A Coruña, Spain, jose.luis.casteleiro@udc.es
  6. 6 Department of Electrical and Systems Engineering , University of León, Escuela de Ingenierías, Campus de Vegazana, 24071 León, Spain, martin.bayon@unileon.es
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Journal:
Logic Journal of the IGPL

ISSN: 1367-0751,  1368-9894

Year of publication: 2024

Type: Article

DOI: 10.1093/JIGPAL/JZAE104 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Logic Journal of the IGPL

Abstract

The Internet of Things (IoT) presents a unique cybersecurity challenge due to its vast network of interconnected, resource-constrained devices. These vulnerabilities not only threaten data integrity but also the overall functionality of IoT systems. This study addresses these challenges by exploring efficient data reduction techniques within a model-based intrusion detection system (IDS) for IoT environments. Specifically, the study explores the efficacy of an autoencoder’s latent space combined with three different classification techniques. Utilizing a validated IoT dataset, particularly focusing on the Constrained Application Protocol (CoAP), the study seeks to develop a robust model capable of identifying security breaches targeting this protocol. The research culminates in a comprehensive evaluation, presenting encouraging results that demonstrate the effectiveness of the proposed methodologies in strengthening IoT cybersecurity with more than a 99% of precision using only 2 learned feature

Bibliographic References

  • Abusitta, (2023), Internet of Things, 21, pp. 100656, 10.1016/j.iot.2022.100656
  • Arreaga, (2023), Procedia Computer Science, 224, pp. 223, 10.1016/j.procs.2023.09.031
  • Breiman, (2001), Machine Learning, 45, pp. 5, 10.1023/A:1010933404324
  • Chen, (2016), 10.1145/2939672.2939785
  • Correia, (2016), IEEE Internet of Things Journal, 3, pp. 923, 10.1109/JIOT.2016.2517120
  • De, (2023), 10.1007/978-981-19-2004-2_35
  • Dutta, (2022), Logic Journal of the IGPL, 30, pp. 912, 10.1093/jigpal/jzac002
  • Elmezughi, (2023), Heliyon, 9, pp. e19685, 10.1016/j.heliyon.2023.e19685
  • Fereidouni, (2023)
  • Granjal, (2018), Sensors, 18, pp. 2445, 10.3390/s18082445
  • Hassan, (2023), Wireless Personal Communications, 128, pp. 2753, 10.1007/s11277-022-10069-6
  • Jiang, (2023), Pattern Recognition Letters, 174, pp. 145, 10.1016/j.patrec.2023.09.001
  • Josso, (2023), Ore Geology Reviews, 162, pp. 105671, 10.1016/j.oregeorev.2023.105671
  • Kingsford, (2008), Nature Biotechnology, 26, pp. 1011, 10.1038/nbt0908-1011
  • Kovatsch, (2022)
  • Lin, (2017), IEEE Internet of Things Journal, 4, pp. 1125, 10.1109/JIOT.2017.2683200
  • Patel, (2021)
  • Mathews, (2022), Proceedings—2022 6th International Conference on Cryptography, Security and Privacy, CSP 2022, pp. 91
  • Pumsirirat, (2018), Technical Report 1, 10.14569/IJACSA.2018.090103
  • Rizzardi, (2022), Wireless Networks, 28, pp. 2857, 10.1007/s11276-022-02999-7
  • Shelby, (2014), 10.17487/rfc7252
  • Statista, (2019)
  • Sun, (2024), Expert Systems With Applications, 237, pp. 121549, 10.1016/j.eswa.2023.121549
  • Sutikno, (2022), TELKOMNIKA (Telecommunication Computing Electronics and Control), 20, pp. 1399, 10.12928/telkomnika.v20i6.22028
  • Mahesh, (2023), Healthcare Analytics, 4, pp. 100247, 10.1016/j.health.2023.100247
  • Thomas, (2017), eCrime Researchers Summit, eCrime, pp. 79
  • Vigoya, (2023), Applied Sciences, 13, pp. 4482, 10.3390/app13074482
  • Westphall, (2020), 2020 IEEE Sensors Applications Symposium (SAS), pp. 1, 10.1109/SAS48726.2020.9220033
  • Whitmore, (2015), Information Systems Frontiers, 10.1007/s10796-014-9489-2
  • Yao, (2023), Sensors, 23, 10.3390/s23084141