A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things
DOI:
https://doi.org/10.56294/sctconf2024648Keywords:
Federated Learning, Transfer Learning, Internet-of-Things, AutoEncoders, Clustering; Feature Selection and Intrusion Detection SystemsAbstract
Federated Learning (FL) has established as a potentially effective practice for cyberattack identification in the last decade, particularly for Internet-of-Things (IoT) structures. FL can increase learning effectiveness, lower transmission overheads, and enhance intrusion detection system (IDS) privacy by spreading the learning process amongst IoT gateways. The absence of labeled data and the distinction of data features for training pose significant obstacles to the deployment of FL in IoT networks. In this research, suggest an Autoencoder based Deep Federated Transfer Learning (ADFTL) to conquer these obstacles. Specifically, Create an ADFTL model utilizing two AutoEncoders (AEs) as the basis. Initially the supervised mode is employed to train the first AE (AE1) on the source datasets while the unsupervised mode is employed to train the second AE (AE2) on the target datasets without label information. The bottleneck layer, or latent representation, of AE2 is forced via the transfer learning method in an effort to resemble the latent representation of AE1. Subsequently, assaults in the input in the target domain are identified employing the latent representation of AE2. Particularly, Weighted k-Subspace Network (WkSNC) clustering is proposed for clustering the dataset and Boosted Sine Cos method (BSCM) is used for feature selection. The requirement that the network datasets utilized in current studies have identical properties is significant since it restricts the effectiveness, adaptability, and scalability of IDS. Nonetheless, the suggested structure can tackle these issues by sharing the "knowledge" of learning among distinct deep learning (DL) simulations, even in cases when their datasets possess dissimilar features. Comprehensive tests on current BoT-IoT datasets demonstrate that the suggested structure can outperform the most advanced DL-based methods by more than 6 %
References
1. Abd Elaziz, M., Al-qaness, M. A., Dahou, A., Ibrahim, R. A., & Abd El-Latif, A. A. (2023). Intrusion detection approach for cloud and IoT environments using deep learning and Capuchin Search Algorithm. Advances in Engineering Software, 103402.
2. Abdalrahman, G. A., & Varol, H. (2019, June). Defending against cyber-attacks on the internet of things. In 2019 7th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-6). IEEE.
3. Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L. F., & Abdulkadir, S. J. (2022). Detecting cybersecurity attacks in internet of things using artificial intelligence methods: A systematic literature review. Electronics, 11(2), 198.
4. Amado DPA, Diaz FAC, Pantoja R del PC, Sanchez LMB. Benefits of Artificial Intelligence and its Innovation in Organizations. AG Multidisciplinar 2023;1:15-15. https://doi.org/10.62486/agmu202315.
5. Avros, R., Frenkel, Z., Toledano-Kitai, D., & Volkovich, Z. (2015). An Iterative Projective Clustering Method. Procedia Computer Science, 60, 122-130.
6. Batista-Mariño Y, Gutiérrez-Cristo HG, Díaz-Vidal M, Peña-Marrero Y, Mulet-Labrada S, Díaz LE-R. Behavior of stomatological emergencies of dental origin. Mario Pozo Ochoa Stomatology Clinic. 2022-2023. AG Odontologia 2023;1:6-6. https://doi.org/10.62486/agodonto20236.
7. BoTNeTIoT-L01, link: https://www.kaggle.com/datasets/azalhowaide/iot-dataset-for-intrusion-detection-systems-ids.
8. Caero L, Libertelli J. Relationship between Vigorexia, steroid use, and recreational bodybuilding practice and the effects of the closure of training centers due to the Covid-19 pandemic in young people in Argentina. AG Salud 2023;1:18-18. https://doi.org/10.62486/agsalud202318.
9. Cavalcante L de FB. Feminicide from the perspective of the cultural mediation of information. Advanced Notes in Information Science 2023;5:24-48. https://doi.org/10.47909/978-9916-9906-9-8.72.
10. Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2019). Network intrusion detection for IoT security based on learning techniques. IEEE Communications Surveys & Tutorials, 21(3), 2671-2701.
11. Chalan SAL, Hinojosa BLA, Claudio BAM, Mendoza OAV. Quality of service and customer satisfaction in the beauty industry in the district of Los Olivos. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:5-5. https://doi.org/10.56294/piii20235.
12. Chávez JJB, Trujillo REO, Hinojosa BLA, Claudio BAM, Mendoza OAV. Influencer marketing and the buying decision of generation «Z» consumers in beauty and personal care companies. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:7-7. https://doi.org/10.56294/piii20237.
13. Choudhary, S., Dey, A., & Kesswani, N. (2021). CRIDS: Correlation and Regression-Based Network Intrusion Detection System for IoT. SN Computer Science, 2, 1-7.
14. Das, S., & Mao, E. (2020). The global energy footprint of information and communication technology electronics in connected Internet-of-Things devices. Sustainable Energy, Grids and Networks, 24, 100408.
15. Diaz DPM. Staff turnover in companies. AG Managment 2023;1:16-16. https://doi.org/10.62486/agma202316.
16. Espinosa JCG, Sánchez LML, Pereira MAF. Benefits of Artificial Intelligence in human talent management. AG Multidisciplinar 2023;1:14-14. https://doi.org/10.62486/agmu202314.
17. Figueredo-Rigores A, Blanco-Romero L, Llevat-Romero D. Systemic view of periodontal diseases. AG Odontologia 2023;1:14-14. https://doi.org/10.62486/agodonto202314.
18. Gonzalez-Argote J, Castillo-González W. Productivity and Impact of the Scientific Production on Human-Computer Interaction in Scopus from 2018 to 2022. AG Multidisciplinar 2023;1:10-10. https://doi.org/10.62486/agmu202310.
19. Gyamfi, E., & Jurcut, A. (2022). Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, ML, and Datasets. Sensors, 22(10), 3744.
20. Gyamfi, E., & Jurcut, A. (2022). Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, ML, and Datasets. Sensors, 22(10), 3744.
21. Habib, M., Aljarah, I., & Faris, H. (2020). A modified multi-objective particle swarm optimizer-based Lévy flight: An approach toward intrusion detection in Internet of Things. Arabian Journal for Science and Engineering, 45(8), 6081-6108.
22. Hazman, C., Guezzaz, A., Benkirane, S., & Azrour, M. (2022). lIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning. Cluster Computing, 1-15.
23. Hernández-Flórez N. Breaking stereotypes: “a philosophical reflection on women criminals from a gender perspective". AG Salud 2023;1:17-17. https://doi.org/10.62486/agsalud202317.
24. Hinojosa BLA, Mendoza OAV. Perceptions on the use of Digital Marketing of the micro-entrepreneurs of the textile sector of the Blue Gallery in the emporium of Gamarra. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:9-9. https://doi.org/10.56294/piii20239.
25. Keserwani, P. K., Govil, M. C., Pilli, E. S., & Govil, P. (2021). A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO–PSO–RF model. Journal of Reliable Intelligent Environments, 7(1), 3-21.
26. Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 1-22.
27. Kumar, S., Tiwari, P., & Zymbler, M. (2019). Internet of Things is a revolutionary approach for future technology enhancement: a review. Journal of Big data, 6(1), 1-21.
28. Lamorú-Pardo AM, Álvarez-Romero Y, Rubio-Díaz D, González-Alvarez A, Pérez-Roque L, Vargas-Labrada LS. Dental caries, nutritional status and oral hygiene in schoolchildren, La Demajagua, 2022. AG Odontologia 2023;1:8-8. https://doi.org/10.62486/agodonto20238.
29. Ledesma-Céspedes N, Leyva-Samue L, Barrios-Ledesma L. Use of radiographs in endodontic treatments in pregnant women. AG Odontologia 2023;1:3-3. https://doi.org/10.62486/agodonto20233.
30. Liu, J., Yang, D., Lian, M., & Li, M. (2021). Research on intrusion detection based on particle swarm optimization in IoT. IEEE Access, 9, 38254-38268.
31. Liu, L., Wang, P., Lin, J., & Liu, L. (2020). Intrusion detection of imbalanced network traffic based on ML and DL. IEEE access, 9, 7550-7563.
32. Lopez ACA. Contributions of John Calvin to education. A systematic review. AG Multidisciplinar 2023;1:11-11. https://doi.org/10.62486/agmu202311.
33. Marcillí MI, Fernández AP, Marsillí YI, Drullet DI, Isalgué RF. Older adult victims of violence. Satisfaction with health services in primary care. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:12-12. https://doi.org/10.56294/piii202312.
34. Marcillí MI, Fernández AP, Marsillí YI, Drullet DI, Isalgué VMF. Characterization of legal drug use in older adult caregivers who are victims of violence. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:13-13. https://doi.org/10.56294/piii202313.
35. Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, 120-133.
36. Moraes IB. Critical Analysis of Health Indicators in Primary Health Care: A Brazilian Perspective. AG Salud 2023;1:28-28. https://doi.org/10.62486/agsalud202328.
37. Ogolodom MP, Ochong AD, Egop EB, Jeremiah CU, Madume AK, Nyenke CU, et al. Knowledge and perception of healthcare workers towards the adoption of artificial intelligence in healthcare service delivery in Nigeria. AG Salud 2023;1:16-16. https://doi.org/10.62486/agsalud202316.
38. Oseni, A., Moustafa, N., Creech, G., Sohrabi, N., Strelzoff, A., Tari, Z., & Linkov, I. (2022). An Explainable Deep Learning Framework for Resilient Intrusion Detection in IoT-Enabled Transportation Networks. IEEE Transactions on Intelligent Transportation Systems.
39. Peñaloza JEG, Bermúdez L marcela A, Calderón YMA. Perception of representativeness of the Assembly of Huila 2020-2023. AG Multidisciplinar 2023;1:13-13. https://doi.org/10.62486/agmu202313.
40. Pérez DQ, Palomo IQ, Santana YL, Rodríguez AC, Piñera YP. Predictive value of the neutrophil-lymphocyte index as a predictor of severity and death in patients treated for COVID-19. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:14-14. https://doi.org/10.56294/piii202314.
41. Prado JMK do, Sena PMB. Information science based on FEBAB’s census of Brazilian library science: postgraduate data. Advanced Notes in Information Science 2023;5:1-23. https://doi.org/10.47909/978-9916-9906-9-8.73.
42. Pupo-Martínez Y, Dalmau-Ramírez E, Meriño-Collazo L, Céspedes-Proenza I, Cruz-Sánchez A, Blanco-Romero L. Occlusal changes in primary dentition after treatment of dental interferences. AG Odontologia 2023;1:10-10. https://doi.org/10.62486/agodonto202310.
43. Quiroz FJR, Oncoy AWE. Resilience and life satisfaction in migrant university students residing in Lima. AG Salud 2023;1:9-9. https://doi.org/10.62486/agsalud20239.
44. Roa BAV, Ortiz MAC, Cano CAG. Analysis of the simple tax regime in Colombia, case of night traders in the city of Florencia, Caquetá. AG Managment 2023;1:14-14. https://doi.org/10.62486/agma202314.
45. Rodríguez AL. Analysis of associative entrepreneurship as a territorial strategy in the municipality of Mesetas, Meta. AG Managment 2023;1:15-15. https://doi.org/10.62486/agma202315.
46. Rodríguez LPM, Sánchez PAS. Social appropriation of knowledge applying the knowledge management methodology. Case study: San Miguel de Sema, Boyacá. AG Managment 2023;1:13-13. https://doi.org/10.62486/agma202313.
47. Serra S, Revez J. As bibliotecas públicas na inclusão social de migrantes forçados na Área Metropolitana de Lisboa. Advanced Notes in Information Science 2023;5:49-99. https://doi.org/10.47909/978-9916-9906-9-8.50.
48. Shareena, J., Ramdas, A., & AP, H. (2021). Intrusion detection system for iot botnet attacks using deep learning. SN Computer Science, 2(3), 205.
49. Sharma, B., Sharma, L., Lal, C., & Roy, S. (2023). Anomaly based network intrusion detection for IoT attacks using deep learning technique. Computers and Electrical Engineering, 107, 108626.
50. Solano AVC, Arboleda LDC, García CCC, Dominguez CDC. Benefits of artificial intelligence in companies. AG Managment 2023;1:17-17. https://doi.org/10.62486/agma202317.
51. UNSW-NB 15 dataset: link: https://www.kaggle.com/datasets/mrwellsdavid/unsw-nb15?select=UNSW-NB15_1.csv
52. Zhang, Y., Li, P., & Wang, X. (2019). Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access, 7, 31711-31722.
53. Zhao, R., Gui, G., Xue, Z., Yin, J., Ohtsuki, T., Adebisi, B., & Gacanin, H. (2021). A novel intrusion detection method based on lightweight neural network for internet of things. IEEE Internet of Things Journal, 9(12), 9960-9972
Published
Issue
Section
License
Copyright (c) 2024 V. S. Lavanya, R. Anushiya (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
