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El impacto de la inteligencia artificial en la transformación de la seguridad informática

dc.contributor.advisorLeguizamón Páez, Miguel Angel
dc.contributor.authorPianda Rodriguéz, Julian Andres
dc.contributor.authorSalazar Bogotá, Jersson Leonardo
dc.contributor.authorLeguizamón Páez, Miguel Ángel
dc.contributor.orcidLeguizamón Páez, Miguel Angel [0000-0003-0457-0126]
dc.creator.emailjuanpiro.m41n@outlook.com
dc.creator.emailjesalazar0190@gmail.com
dc.creator.emailmianlepa@gmail.com
dc.creator.id20142578103
dc.creator.id20151678001
dc.date.accessioned2026-05-09T22:19:29Z
dc.date.available2026-05-09T22:19:29Z
dc.date.created2027-01-15
dc.descriptionEste artículo examina el papel de la inteligencia artificial en la seguridad informática, considerando su impacto actual y los desafíos futuros derivados de su creciente integración en entornos digitales. Mediante una revisión de literatura científica y estudios relevantes, se analizan las principales aplicaciones de la inteligencia artificial en ciberseguridad, destacando su eficacia en la identificación y mitigación de riesgos. También se examinan algoritmos clave, entre ellos el aprendizaje automático aplicado a la detección de patrones para la prevención de ataques. Además, se describe la evolución de la ciberseguridad, desde medidas tradicionales como los firewalls hasta estrategias avanzadas de defensa en tiempo real basadas en inteligencia artificial. Los resultados evidencian que la inteligencia artificial fortalece significativamente las capacidades de detección y respuesta ante amenazas cibernéticas. No obstante, su implementación también plantea desafíos, como el posible uso malicioso de estas tecnologías por parte de atacantes. En este sentido, la inteligencia artificial se consolida como un elemento clave en la evolución de la ciberseguridad y en el desarrollo de estrategias de protección frente a amenazas cada vez más sofisticadas.
dc.description.abstractThis article examines the role of artificial intelligence in information security, considering its current impact and the future challenges arising from its growing integration into digital environments. Through a review of scientific literature and relevant studies, the main applications of artificial intelligence in cybersecurity are analyzed, highlighting its effectiveness in the identification and mitigation of risks. Key algorithms are also examined, including machine learning applied to pattern detection for attack prevention. In addition, the evolution of cybersecurity is described, from traditional measures such as firewalls to advanced real-time defense strategies based on artificial intelligence. The results show that artificial intelligence significantly strengthens the capabilities for detecting and responding to cyber threats. However, its implementation also poses challenges, such as the potential malicious use of these technologies by attackers. In this regard, artificial intelligence has established itself as a key element in the evolution of cybersecurity and in the development of protection strategies against increasingly sophisticated threats.
dc.format.mimetypepdf
dc.identifier.urihttp://hdl.handle.net/11349/101317
dc.language.isospa
dc.publisherUniversidad Distrital Francisco José de Caldas
dc.relation.referencesI. A. Maslova, “DIGITAL SECURITY IN TODAY'S WORLD”, Res. Result Econ. Res., vol. 10, n.º 3, septiembre de 2024. DOI: 10.18413/2409-1634-2024-10-3-0-8.
dc.relation.referencesE. Benavides, W. Fuertes y S. Sanchez, “Caracterización de los ataques de phishing y técnicas para mitigarlos. Ataques: una revisión sistemática de la literatura”, Cienc. Tecnol., vol. 13, n.º 1, pp. 97–104, junio de 2020. DOI: 10.18779/cyt.v13i1.357
dc.relation.referencesO. Polat, Ö. Durmuş, F. Doğan, M. Türkoğlu, H. Şeker, F. Atasoy, and E. Algül, “Supervised and deep learning techniques for DDoS detection in software-defined network architectures: a systematic review,” Eng. Sci. Technol., Int. J., vol. 75, Art. no. 102290, 2026, DOI: 10.1016/j.jestch.2026.102290.
dc.relation.referencesR. Raimundo y A. Rosário, “The Impact of Artificial Intelligence on Data System Security: A Literature Review”, Sensors, vol. 21, n.º 21, p. 7029, octubre de 2021. DOI: 10.3390/s21217029
dc.relation.referencesA. Aloseel et al., “Attack-Detection Architectural Framework Based on Anomalous Patterns of System Performance and Resource Utilization—Part II”, IEEE Access, vol. 9, pp. 87611–87629, 2021. DOI: 10.1109/access.2021.3088411
dc.relation.referencesS. Zeadally, E. Adi, Z. Baig y I. A. Khan, “Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity”, IEEE Access, vol. 8, pp. 23817–23837, 2020. DOI: 10.1109/access.2020.2968045
dc.relation.referencesN. G. Camacho, “The Role of AI in Cybersecurity: Addressing Threats in the Digital Age”, JAIGS, vol. 3, n.º 1, pp. 143–154, marzo de 2024. DOI: 10.60087/jaigs.v3i1.75
dc.relation.referencesH. Alturkistani y M. A. El-Affendi, “Optimizing cybersecurity incident response decisions using deep reinforcement learning”, IJECE, vol. 12, n.º 6, p. 6768, diciembre de 2022. DOI: 10.11591/ijece.v12i6.pp6768-6776
dc.relation.referencesY. Huang, L. Huang y Q. Zhu, “Reinforcement Learning for feedback-enabled cyber resilience”, Annu. Rev. Control, 2022. DOI: 10.1016/j.arcontrol.2022.01.001
dc.relation.referencesR. Rivera Delgado de Vela, «Inteligencia artificial: evolución, aplicaciones y futuro», REVISTA MEDICA HHUT, vol. 17, n.º 1, pp. 51–60, jul. 2024. DOI: 10.70409/rmhhut.2024.171.24
dc.relation.referencesB. Li et al., “Research on AI security enhanced encryption algorithm of autonomous IoT systems”, Inf. Sci., vol. 575, pp. 379–398, 2021. DOI: 10.1016/j.ins.2021.06.016
dc.relation.referencesP. Timmers, “Ethics of AI and Cybersecurity When Sovereignty is at Stake”, Minds Mach., vol. 29, n.º 4, pp. 635–645, 2019. DOI: 10.1007/s11023-019-09508-4
dc.relation.referencesA. A. Becerril Gil, “Retos para la regulación jurídica de la Inteligencia Artificial en el ámbito de la Ciberseguridad”, REV. IUS, vol. 15, n.º 48, 2021. DOI: 10.35487/rius.v15i48.2021.705
dc.relation.referencesJ. Lee et al., “Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles”, IEEE Access, vol. 7, pp. 165607–165626, 2019. DOI: 10.1109/access.2019.2953095
dc.relation.referencesX. Meng, “Advanced AI and ML techniques in cybersecurity”, Appl. Comput. Eng., vol. 82, n.º 1, pp. 1–5, 2024. DOI: 10.54254/2755-2721/82/2024glg0054
dc.relation.referencesD. I. Quirumbay Yagual et al., “Una revisión del Aprendizaje profundo aplicado a la ciberseguridad”, Rev. Cient. Tecnol. UPSE, vol. 9, n.º 1, pp. 57–65, 2022. DOI: 10.26423/rctu.v9i1.671
dc.relation.referencesM. Pešec, “Impact of Artificial Intelligence Development on National Security”, Contemporary Military Challenges, 2020. DOI: 10.33179/bsv.99.svi.11.cmc.22.1.7
dc.relation.referencesH. Wu et al., “Research on Artificial Intelligence Enhancing Internet of Things Security: A Survey”, IEEE Access, vol. 8, pp. 153826–153848, 2020. DOI: 10.1109/access.2020.3018170
dc.relation.referencesM. Powell y B. M. Drozdenko, “SSOLV: Real-Time AI/ML-based Cybersecurity via Statistical Analysis”, IEEE Access, 2024. DOI: 10.1109/access.2024.3444703
dc.relation.referencesM. M. et al., “Deep Learning Approach For Intelligent Intrusion Detection System”, IRJASH, 2021. DOI: 10.47392/irjash.2021.061
dc.relation.referencesR. Tahri, Y. Balouki, A. Jarrar y A. Lasbahani, “Intrusion Detection System Using Machine Learning Algorithms”, ITM Web Conf., vol. 46, p. 02003, 2022. DOI: 10.1051/itmconf/20224602003
dc.relation.referencesS. A. Ludwig, “Applying a Neural Network Ensemble to Intrusion Detection”, J. Artif. Intell. Soft Comput. Res., vol. 9, n.º 3, pp. 177–188, julio de 2019. DOI: 10.2478/jaiscr-2019-0002
dc.relation.referencesI. A. Kandhro et al., “Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures”, IEEE Access, 2023. DOI: 10.1109/access.2023.3238664
dc.relation.referencesM. Sharp, M. Dadfarnia, T. Sprock y D. Thomas, “Procedural Guide for System-Level Impact Evaluation of Industrial Artificial Intelligence-Driven Technologies”, J. Manuf. Sci. Eng., vol. 144, n.º 7, 2021. DOI: 10.1115/1.4053155
dc.relation.referencesY. Wei et al., “AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification”, IEEE Access, 2021. DOI: 10.1109/access.2021.3123791
dc.relation.referencesW. Ma, “Analysis of anomaly detection method for Internet of things based on deep learning,” Trans. Emerging Telecommun. Technol., vol. 31, no. 12, Apr. 2020, DOI: 10.1002/ett.3893.
dc.relation.referencesM. Ruan, H. Gao y Y. Wang, “Secure and Privacy-Preserving Consensus”, IEEE Trans. Autom. Control, vol. 64, n.º 10, pp. 4035–4049, 2019. DOI: 10.1109/tac.2019.2890887
dc.relation.referencesM. Ibrahim, I. Taha y H. Al-Aloun, “A Network Based Intrusion Detection Model Using Neural Network”, Int. Conf. Aerosp. Sci. Aviation Technol., 2003. DOI: 10.21608/asat.2013.24707
dc.relation.referencesA. B. Torres, F. G. Rendón y J. F. Gutiérrez, “Revisión de las técnicas de inteligencia artificial aplicadas en seguridad informática”, Rev. Ontare, 2020. DOI: 10.21158/23823399.v7.n0.2019.2612
dc.relation.referencesJ. E. D. Chavez Flores, J. C. J. Pacheco Guzmán y A. C. Mendoza de los Santos, “El papel de la inteligencia artificial en la seguridad de la información”, Rev. Investigación Sist. Informática, vol. 16, n.º 1, pp. 71–80, 2023. DOI: 10.15381/risi.v16i1.25390
dc.relation.referencesJ. Wu, K. Ota, M. Dong y C. Li, “A Hierarchical Security Framework for Defending Against Sophisticated Attacks on Wireless Sensor Networks in Smart Cities”, IEEE Access, vol. 4, pp. 416–424, 2016. DOI: 10.1109/access.2016.2517321
dc.relation.referencesS. Chauhan y S. Lee, “Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency”, IEEE Access, vol. 10, pp. 132062–132070, 2022. DOI: 10.1109/access.2022.3230352
dc.relation.referencesM. Munir et al., “DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series”, IEEE Access, vol. 7, pp. 1991–2005, 2019. DOI: 10.1109/access.2018.2886457
dc.relation.referencesM. Lopez-Martin, B. Carro y A. Sanchez-Esguevillas, “Application of Deep Reinforcement Learning to Intrusion Detection”, Expert Systems with Applications, vol. 141, 2020. DOI: 10.1016/j.eswa.2019.112963
dc.relation.referencesK. Filus, J. Domańska, and E. Gelenbe, “Random Neural Network for Lightweight Attack Detection in the IoT,” in Modelling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2020, M. C. Calzarossa, E. Gelenbe, K. Grochla, R. Lent, and T. Czachórski, Eds., Lecture Notes in Computer Science, vol. 12527. Cham, Switzerland: Springer, 2021, pp. 79–91, DOI: 10.1007/978-3-030-68110-4_5.
dc.relation.referencesA. Y. Khan et al., “Malicious Insider Attack Detection in IoTs Using Data Analytics”, IEEE Access, vol. 8, pp. 11743–11753, 2020. DOI: 10.1109/access.2019.2959047
dc.relation.referencesK. Al Jallad, M. Aljnidi y M. S. Desouki, “Anomaly Detection Optimization Using Big Data and Deep Learning”, J. Big Data, 2020. DOI: 10.1186/s40537-020-00346-1
dc.relation.referencesA. Sivanathan et al., “Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics”, IEEE Trans. Mobile Comput., vol. 18, n.º 8, pp. 1745–1759, 2019. DOI: 10.1109/tmc.2018.2866249
dc.relation.referencesB. Patel, P. K. Bhagavanbhai y N. Dhameliya, “Revolutionizing Cybersecurity with AI: Predictive Threat Intelligence and Automated Response Systems”, Darpan Int. Res. Anal., 2024. DOI: 10.36676/dira.v12.i4.126
dc.relation.referencesL. Fernández Maimó et al., “Intelligent and Dynamic Ransomware Spread Detection and Mitigation”, Sensors, vol. 19, n.º 5, 2019. DOI: 10.3390/s19051114
dc.relation.referencesJ. P. A. Maranhão et al., “Tensor Based Framework for Distributed Denial of Service Attack Detection”, J. Netw. Comput. Appl., 2021. DOI: 10.1016/j.jnca.2020.102894
dc.relation.referencesT. T. Nguyen y V. J. Reddi, “Deep Reinforcement Learning for Cyber Security”, IEEE Trans. Neural Netw. Learn. Syst., 2021. DOI: 10.1109/tnnls.2021.3121870
dc.relation.referencesJ.-F. Zhang et al., “SNAP: An Efficient Sparse Neural Acceleration Processor for Deep Neural Network Inference”, IEEE J. Solid-State Circuits, 2020. DOI: 10.1109/jssc.2020.3043870
dc.relation.referencesD.-O. Won, Y.-N. Jang y S.-W. Lee, “PlausMal-GAN: Plausible Malware Training Based on Generative Adversarial Networks”, IEEE Trans. Emerg. Topics Comput., 2022. DOI: 10.1109/tetc.2022.3170544
dc.relation.referencesJ.-Y. Kim, S.-J. Bu y S.-B. Cho, “Zero-Day Malware Detection Using Generative Adversarial Networks”, Inf. Sci., vol. 460–461, pp. 83–102, 2018. DOI: 10.1016/j.ins.2018.04.092
dc.relation.referencesI. Siniosoglou et al., “Unified Deep Learning Anomaly Detection for Smart Grid Environments”, IEEE Trans. Netw. Service Manage., 2021. DOI: 10.1109/tnsm.2021.3078381
dc.relation.referencesH. Fahmy et al., “Simulator-Based Explanation and Debugging of Hazard-Triggering Events in DNN Systems”, ACM Trans. Softw. Eng. Methodol., 2022. DOI: 10.1145/3569935
dc.relation.referencesM. S. Alzboon et al., “The Two Sides of AI in Cybersecurity: Opportunities and Challenges”, ICNGN, IEEE, 2023. DOI: 10.1109/icngn59831.2023.10396670
dc.relation.referencesD. Li y D. Li, “Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection”, IEEE Trans. Inf. Forensics Secur., 2020. DOI: 10.1109/tifs.2020.3003571
dc.relation.referencesR. Vinayakumar et al., “Robust Intelligent Malware Detection Using Deep Learning”, IEEE Access, 2019. DOI: 10.1109/access.2019.2906934
dc.relation.referencesZ. Wang, C. Liu, and X. Cui, “EvilModel: Hiding Malware Inside of Neural Network Models,” in Proc. IEEE Symp. Comput. Commun. (ISCC), Athens, Greece, 2021, pp. 1–7, DOI: 10.1109/ISCC53001.2021.9631425.
dc.relation.referencesS. Mirishli, “Ethical Implications of AI in Data Collection”, Ancient Land, 2024. DOI: 10.36719/2706-6185/38/40-55
dc.relation.referencesA. Hindle, “Impact of GDPR on Identity and Access Management”, IDPro Body of Knowledge, 2020. DOI: 10.55621/idpro.24
dc.relation.referencesO. Johnphill et al., “Self-Healing in Cyber–Physical Systems Using Machine Learning”, Future Internet, 2023. DOI: 10.3390/fi15070244
dc.relation.referencesA. Alijoyo et al., “Enhancing Cyber-Physical Systems Resilience Using LSTM Networks”, IEEE, 2024. DOI: 10.1109/accai61061.2024.10602467
dc.relation.referencesM. Gao, “Obstacles and Impacts of Artificial Intelligence in Digital Security”, Advances Econ. Manage. Political Sci., 2024. DOI: 10.54254/2754-1169/93/20241092
dc.relation.referencesS. Samtani, M. Kantarcioglu y H. Chen, “Trailblazing the Artificial Intelligence for Cybersecurity Discipline”, ACM Trans. Manage. Inf. Syst., 2020. DOI: 10.1145/3430360
dc.relation.referencesY. Hu et al., “Artificial Intelligence Security: Threats and Countermeasures”, ACM Comput. Surv., 2023. DOI: 10.1145/3487890
dc.rights.accesoRestringido (Solo Referencia)
dc.rights.accessrightsRestrictedAccess
dc.subjectSeguridad Informática
dc.subjectInteligencia Artificial
dc.subjectCiberseguridad
dc.subjectAutomatización de Respuestas
dc.subjectProtección Avanzada
dc.subject.keywordComputer Security
dc.subject.keywordArtificial Intelligence
dc.subject.keywordCybersecurity
dc.subject.keywordResponse Automation
dc.subject.keywordAdvanced Protection
dc.subject.lembingeniería Telemática -- Tesis y disertaciones académicas
dc.subject.lembInteligencia artificial — Aplicaciones en seguridad informática
dc.subject.lembSeguridad informática
dc.subject.lembTecnologías de la información — Innovaciones tecnológicas
dc.titleEl impacto de la inteligencia artificial en la transformación de la seguridad informática
dc.title.titleenglishThe impact of artificial intelligence in the transformation of information security
dc.typebachelorThesis
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.type.degreeProducción Académica
dc.type.driverinfo:eu-repo/semantics/bachelorThesis

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