Desarrollo de un modelo para la detección de ataques SSDF en redes de radio cognitiva móvil integrando técnicas de inteligencia artificial
| dc.contributor.author | Yara Cifuentes, Lina María | |
| dc.contributor.author | Cadena Muñoz, Ernesto | |
| dc.contributor.author | Cubillos Sánchez, Rafael Orlando | |
| dc.contributor.author | Mateus Rojas, Armando | |
| dc.contributor.corporatename | Universidad Santo Tomás | |
| dc.contributor.cvlac | https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_r h=0001465749 | |
| dc.contributor.cvlac | https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do ?cod_rh=0001040294 | |
| dc.contributor.cvlac | https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do ?cod_rh=0000680630 | |
| dc.contributor.googlescholar | https://scholar.google.com/citations?user=qTycqC4AAAAJ&hl=es | |
| dc.contributor.googlescholar | https://scholar.google.com/citations?hl=en&user=a527iHIAAAAJ | |
| dc.contributor.googlescholar | https://scholar.google.com/citations?user=1az5o_IAAAAJ&hl=es | |
| dc.contributor.gruplac | https://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000 000001433 | |
| dc.contributor.gruplac | https://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000002964 | |
| dc.contributor.orcid | https://orcid.org/0000-0002-1086-3665 | |
| dc.contributor.orcid | https://orcid.org/0000-0002-2399-4859 | |
| dc.date.accessioned | 2026-08-15T15:14:55Z | |
| dc.date.available | 2026-08-15T15:14:55Z | |
| dc.date.issued | 2026-02-09 | |
| dc.description | Las redes de radio cognitiva móvil (MCRNs) permiten el acceso dinámico al espectro mediante la técnica de detección cooperativa del espectro (CSS), pero presentan vulnerabilidades frente a ataques de falsificación de datos de detección del espectro (SSDF). Diversos estudios han abordado este problema mediante técnicas de aprendizaje automático, modelos de confianza y métodos híbridos. Este articulo presenta los principales resultados de un modelo hibrido que integra máquina de vectores de soporte (SVM), el algoritmo K-Nearest Neighbors (KNN) y un sistema de reputación basado en la distribución Beta para identificar usuarios maliciosos. La validación experimental mediante radio definida por software (SDR) demuestra probabilidades de detección superiores al 90 %, una reducción significativa de falsas alarmas y una mayor robustez en condiciones de movilidad y con bajos valores de relación señal-ruido (SNR). | |
| dc.description.abstract | Mobile Cognitive Radio Networks (MCRNs) enable dynamic spectrum access through the Cooperative Spectrum Sensing (CSS) technique; however, they present vulnerabilities to Spectrum Sensing Data Falsification (SSDF) attacks. Various studies have addressed this problem using machine learning techniques, trust models, and hybrid methods. This article presents the main results of a hybrid model that integrates Support Vector Machines (SVM), the K-Nearest Neighbors (KNN) algorithm, and a reputation system based on the Beta distribution to identify malicious users. Experimental validation using Software Defined Radio (SDR) demonstrates detection probabilities above 90%, a significant reduction in false alarms, and greater robustness under mobility conditions and low Signal-to-Noise Ratio (SNR) values. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Yara Cifuentes, L. M., Cadena Muñoz, E., Cubillos Sánchez, R., y Mateus Rojas, A. (2024). Desarrollo de un modelo para la detección de ataques SSDF en redes de radio cognitiva móvil integrando técnicas de inteligencia artificial. Documento de investigación presentado para optar al título de Magíster en Ingeniería Electrónica, Universidad Santo Tomás. | |
| dc.identifier.uri | http://hdl.handle.net/11634/73905 | |
| dc.publisher.branch | CRAI-USTA Bogotá | |
| dc.relation.references | Solanki, S., Dehalwar, V., & Choudhary, J. (2021). Deep learning for spectrum sensing in cognitive radio. Symmetry, 13(1), 147. https://doi.org/10.3390/sym13010147 | |
| dc.relation.references | Manoranjan, G., Chandan, M., Karthik, G., Satpathy, R., & Sugumaran, S. (2021). Security against SSDF attacks using novel attack mitigation mechanism for cognitive radio networks. En 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1–6). IEEE. https://doi.org/10.1109/ICECCT52121.2021 | |
| dc.relation.references | Paul, A., Mishra, A. K., Shreevastava, S., & Tiwari, A. K. (2022). Deep reinforcement learning based reliable spectrum sensing under SSDF attacks in cognitive radio networks. Journal of Network and Computer Applications, 205, 103454. https://doi.org/10.1016/j.jnca.2022.103454 | |
| dc.relation.references | Sarmah, R., Taggu, A., & Marchang, N. (2020). Detecting Byzantine attack in cognitive radio networks using machine learning. Wireless Networks, 26(8), 5939–5950. https://doi.org/10.1007/s11276-020-02367-1 | |
| dc.relation.references | Banerjee, S., Singh, T., & Singh, K. R. (2022). Mitigation of spectrum sensing data falsification attack (SSDF) in cognitive radio network. Journal of The Institution of Engineers (India): Series B, 103(4), 1249–1257. https://doi.org/10.1007/s40031-022-00736-5 | |
| dc.relation.references | Wan, R., Ding, L., Xiong, N., & Zhou, X. (2019). Mitigation strategy against spectrum-sensing data falsification attack in cognitive radio sensor networks. International Journal of Distributed Sensor Networks, 15(9). https://doi.org/10.1177/1550147719870645 | |
| dc.relation.references | Ahmadfard, A., Jamshidi, A., & Keshavarz-Haddad, A. (2017). Probabilistic spectrum sensing data falsification attack in cognitive radio networks. Signal Processing, 137, 1– 9. https://doi.org/10.1016/j.sigpro.2017.02.015 | |
| dc.relation.references | Chatterjee, S., & Chatterjee, P. S. (2015). A comparison based clustering algorithm to counter SSDF attack in CWSN. En 2015 International Conference on Computational Intelligence and Networks (pp. 194–195). IEEE. https://doi.org/10.1109/CINE.2015.31 | |
| dc.relation.references | Zhi, X., Cao, Y., & Liu, X. (2024). Anti-dynamic SSDF attack scheme based on credit mechanism. En 2024 4th International Conference on Electronic Information Engineering and Computer (EIECT) (pp. 875–879). IEEE. | |
| dc.relation.references | Kumar, R., Parmar, A., Captain, K., & Patel, J. (2023). Reinforcement learning for performance improvement in cooperative spectrum sensing. Physical Communication, 59, 102112. https://doi.org/10.1016/j.phycom.2023.102112 | |
| dc.relation.references | Antoinette, A., & Buvaneswari, S. (2023). Security and energy management in cooperative spectrum sensing. Procedia Comput er Science, 230, 716–724 https://doi.org/10.1016/j.procs.2023.11.083 | |
| dc.relation.references | Rachakonda, L. P., Siddula, M., & Sathya, V. (2024). A comprehensive study on IoT privacy and security challenges with focus on spectrum sharing in next-generation networks (5G/6G/beyond). High-Confidence Computing, 100220. https://doi.org/10.1016/j.hcc.2024.100220 | |
| dc.relation.references | Liu, M., Liu, L., Xu, D., & Zhang, L. (2025). Cognitive IoT collusion SSDF attack detection based on FP-growth algorithm. Journal of Network and Systems Management, 33(2), 25. | |
| dc.relation.references | Feng, J., Zhang, M., Xiao, Y., & Yue, H. (2018). Securing cooperative spectrum sensing against collusive SSDF attack using XOR distance analysis in cognitive radio networks. Sensors, 18(2), 370. https://doi.org/10.3390/s18020370 | |
| dc.relation.references | Zardosht, F., Derakhtian, M., Jamshidi, A., & Eshaghi, H. (2022). Recognition and elimination of SSDF attackers in cognitive radio networks. Telecommunication Systems, 81(1), 53–66. https://doi.org/10.1007/s11235-022-00942-z | |
| dc.relation.references | Liu, Y., Shaikh, A. H., & Khoso, W. M. (2023). Impact of spectrum sensing data falsification attack and imperfect reporting channel on cooperative spectrum sensing. En 2023 9th International Conference on Computer and Communications (ICCC) (pp. 470–474). IEEE. | |
| dc.relation.references | Feng, J., Zhang, M., Xiao, Y., & Yue, H. (2018). Securing cooperative spectrum sensing against collusive SSDF attack using XOR distance analysis in cognitive radio networks. Sensors, 18(2), 370. https://doi.org/10.3390/s18020370 | |
| dc.relation.references | Zardosht, F., Derakhtian, M., Jamshidi, A., & Eshaghi, H. (2022). Recognition and elimination of SSDF attackers in cognitive radio networks. Telecommunication Systems, 81(1), 53–66. https://doi.org/10.1007/s11235-022-00942-z | |
| dc.relation.references | Liu, Y., Shaikh, A. H., & Khoso, W. M. (2023). Impact of spectrum sensing data falsification attack and imperfect reporting channel on cooperative spectrum sensing. En 2023 9th International Conference on Computer and Communications (ICCC) (pp. 470–474). IEEE. | |
| dc.relation.references | Peng, T., Chen, Y., Xiao, J., Zheng, Y., & Yang, J. (2016). Improved soft fusion-based cooperative spectrum sensing defense against SSDF attacks. En 2016 International Conference on Computer, Information and Telecommunication Systems (CITS) (pp. 1–5). IEEE. | |
| dc.relation.references | Li, L., Li, F., & Zhu, J. (2013). A method to defense against cooperative SSDF attacks in cognitive radio networks. En 2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013) (pp. 1–6). IEEE. | |
| dc.relation.references | Chatterjee, P. S. (2021). Systematic survey on SSDF attack and detection mechanism in cognitive wireless sensor network. En 2021 International Conference on Intelligent Technologies (CONIT) (pp. 1–5). IEEE. | |
| dc.relation.references | Fu, Y., & He, Z. (2022). An online learning approach for cooperator selection in CSS under SSDF attack. IEEE Communications Letters, 26(7), 1479–1483. https://doi.org/10.1109/LCOMM.2022.3171583 | |
| dc.relation.references | Reddy, A. A., Gandlapalli, V. S. A., Sai, K. V., & Battula, R. B. (2024). Secure spectrum sensing in CRNs: A study on SSDF attacks and counterme asure using USRP. En 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1–5). IEEE. | |
| dc.relation.references | Yao, D., Yuan, S., Lv, Z., Wan, D., & Mao, W. (2020). An enhanced cooperative spectrum sensing scheme against SSDF attack based on Dempster-Shafer evidence theory for cognitive wireless sensor networks. IEEE Access, 8, 175881–175890. https://doi.org/10.1109/ACCESS.2020.3026463 | |
| dc.relation.references | Liu, S., Liu, Q., Gao, J., & Guan, J. (2011). Attacker-exclusion scheme for cooperative spectrum sensing against SSDF attacks based on accumulated suspicious level. En 2011 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (pp. 239–243). IEEE. | |
| dc.relation.references | Sharifi, A. A., & Niya, M. J. M. (2015). Defense against SSDF attack in cognitive radio networks: Attack-aware collaborative spectrum sensing approach. IEEE Communications Letters, 20(1), 93-96. | |
| dc.relation.references | Cheng, Z., Zhang, J., Song, T., Hu, J., & Bao, X. (2020). Detection strategy against restricted SSDF attack with potential interaction assistance. IEEE Transactions on Cognitive Communications and Networking, 7(2), 553–566. https://doi.org/10.1109/TCCN.2020.3046092 | |
| dc.rights | Attribution-NonCommercial-NoDerivs 2.5 Colombia | en |
| dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/co/ | |
| dc.subject.keyword | Mobile Cognitive Radio Networks | |
| dc.subject.keyword | SSDF Attack | |
| dc.subject.keyword | Cooperative Spectrum Sensing | |
| dc.subject.keyword | Machine Learning | |
| dc.subject.keyword | Reputation Systems | |
| dc.subject.keyword | SDR | |
| dc.subject.lemb | Redes de radio cognitiva | |
| dc.subject.lemb | Redes móviles | |
| dc.subject.lemb | Seguridad de redes | |
| dc.subject.proposal | MCRN | |
| dc.subject.proposal | SSDF | |
| dc.subject.proposal | Aprendizaje automático | |
| dc.subject.proposal | SVM | |
| dc.subject.proposal | KNN | |
| dc.subject.proposal | Reputación | |
| dc.subject.proposal | SDR | |
| dc.title | Desarrollo de un modelo para la detección de ataques SSDF en redes de radio cognitiva móvil integrando técnicas de inteligencia artificial | |
| dc.type | internal report | |
| dc.type.category | Apropiación Social y Circulación del Conocimiento: Documento de trabajo (working papers) |