Tendencias de las técnicas de la inteligencia artificial, en la detección de delitos informáticos: Revisión Sistemática de la Literatura (SLR)

Contenido principal del artículo

Joseph Alberto Delgado-Indacochea
Roberth Abel Alcívar-Cevallos

Resumen

En este artículo, se presenta una Revisión Sistemática de la Literatura (RSLR) que se centra en las aplicaciones de las técnicas de Inteligencia Artificial (IA) con el propósito de detectar delitos informáticos. La primera parte de la revisión se dedica a seleccionar las fuentes de información a emplear, mientras que en la siguiente sección se proporciona una descripción detallada de las investigaciones que han empleado estas técnicas de (IA). Durante esteel proceso de investigación, se evidenció que la mayoría de los estudios han empleado una diversidad de algoritmos de (IA), e. Entre los más frecuentes figuran SVM, Decision Tree, Logistic Regression, Naive Bayes, KNN y Random Forest, los cuales han demostrado su eficacia en múltiples áreas de ciberseguridad, abarcando la detección de intrusiones, ataques de denegación de servicio (DoS), phishing y malware. En este contexto, se ha observado que XGBoost, Random Forest y Logistic Regression destacan por su asombroso equilibrio entre las métricas de precisión y exactitud, como lo respaldan varias investigaciones, . lLos hallazgos enfatizan la necesidad de adaptar la elección del algoritmo según el conjunto de datos y el contexto específico, subrayando la importancia de llevar a cabo pruebas y definiciones meticulosas. Por último, los resultados obtenidos de esta revisión proporcionan una guía esclarecedora que puede orientar decisiones, ofreciendo a los lectores una visión de las técnicas más prometedoras de las áreas que ameritan mayor atención, además de la exploración para futuras investigaciones. 

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Detalles del artículo

Cómo citar
Delgado-Indacochea, J. ., & Alcívar-Cevallos, R. . (2024). Tendencias de las técnicas de la inteligencia artificial, en la detección de delitos informáticos: Revisión Sistemática de la Literatura (SLR). 593 Digital Publisher CEIT, 9(1), 810-830. https://doi.org/10.33386/593dp.2024.1.2184
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Investigaciones /estudios empíricos
Biografía del autor/a

Joseph Alberto Delgado-Indacochea, Universidad Técnica de Manabí - Ecuador

https://orcid.org/0009-0009-2343-2643

Estudiante de ingeniería de Sistema de Información, Facultad de Ciencias Informáticas, Universidad Técnica de Manabí. 

https://orcid.org/0009-0009-2343-2643

Estudiante de ingeniería de Sistema de Información, Facultad de Ciencias Informáticas, Universidad Técnica de Manabí.

Roberth Abel Alcívar-Cevallos, Universidad Técnica de Manabí - Ecuador

https://orcid.org/0000-0001-6282-8493

Doctor en Ciencias de la ingeniería con mención en informática, Docente en el Departamento Tecnología de la Información, Facultad de Ciencias Informáticas, Universidad Técnica de Manabí.

Citas

​​Advanced Persistent Threat Attack Detection using Clustering Algorithms—ProQuest. (s. f.). Recuperado 6 de septiembre de 2023, de https://www.proquest.com/openview/e4cf78c9c76360df56db08e93dac95b2/1?pq-origsite=gscholar&cbl=5444811

​Alabdulkreem, E., Alotaibi, S., Alamgeer, M., Marzouk, R., Hilal, A., Motwakel, A., Zamani, A., Rizwanullah, M., & Mustafa, A. (2022). Intelligent Cybersecurity Classification Using Chaos Game Optimization with Deep Learning Model. Computer Systems Science and Engineering, 45, 971-983. https://doi.org/10.32604/csse.2023.030362

​An Empirical Study on Fake News Detection System using Deep and Machine Learning Ensemble Techniques—ProQuest. (s. f.). Recuperado 6 de septiembre de 2023, de https://www.proquest.com/openview/afe7ca89f1656bc6daff1d157e23ea25/1?pq-origsite=gscholar&cbl=5444811

​Alarfaj, F. K., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed, M. (2022). Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms. IEEE Access, 10, 39700-39715. https://doi.org/10.1109/ACCESS.2022.3166891

​Al-Khater, W. A., Al-Maadeed, S., Ahmed, A. A., Sadiq, A. S., & Khan, M. K. (2020). Comprehensive Review of Cybercrime Detection Techniques. IEEE Access, 8, 137293-137311. https://doi.org/10.1109/ACCESS.2020.3011259

​Anomaly-based Network Intrusion Detection using Ensemble Machine Learning Approach—ProQuest. (s. f.). Recuperado 6 de septiembre de 2023, de https://www.proquest.com/openview/d9d72dd1b72f456e91148e9657176137/1?pq-origsite=gscholar&cbl=5444811

​BCT-CS: Blockchain Technology Applications for Cyber Defense and Cybersecurity: A Survey and Solutions - ProQuest. (s. f.). Recuperado 6 de septiembre de 2023, de https://www.proquest.com/openview/421f66c9054aaffb1ba624e83c2e3757/1?pq-origsite=gscholar&cbl=5444811

​Capuano, N., Fenza, G., Loia, V., & Stanzione, C. (2022). Explainable Artificial Intelligence in CyberSecurity: A Survey. IEEE Access, 10, 93575-93600. https://doi.org/10.1109/ACCESS.2022.3204171

​Ch, R., Gadekallu, T. R., Abidi, M. H., & Al-Ahmari, A. (2020). Computational System to Classify Cyber Crime Offenses Using Machine Learning. Sustainability, 12(4087), 4087. https://doi.org/10.3390/su12104087

​COVID-19 malicious domain names classification—ScienceDirect. (s. f.). Recuperado 6 de septiembre de 2023, de https://www.sciencedirect.com/science/article/pii/S0957417422008715

​Department of Computer Science Engineering, Bhagwan Parshuram Institute of Technology, New Delhi-110089, India, Pandey, H., Goyal, R., Virmani, D., & Gupta, C. (2021). Ensem_SLDR: Classification of Cybercrime using Ensemble Learning Technique. International Journal of Computer Network and Information Security, 14(1), 81-90. https://doi.org/10.5815/ijcnis.2022.01.07

​Electronics | Free Full-Text | A Robust Forgery Detection Method for Copy–Move and Splicing Attacks in Images. (s. f.). Recuperado 7 de septiembre de 2023, de https://www.mdpi.com/2079-9292/9/9/1500

​Gawande, R., & Badotra, S. (2022). Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept. International Journal of Advanced Computer Science and Applications, 13(6). https://doi.org/10.14569/IJACSA.2022.0130693

​Gil, B., & Anyel, A. (2021). Challenges for the legal regulation of Artificial Intelligence in the field of Cybersecurity. Revista IUS, 15(48), 9-34. https://doi.org/10.35487/rius.v15i48.2021.705

​G. Zhao, P. Jia, C. Huang, A. Zhou, y Y. Fang, «A Machine Learning Based Framework for Identifying Influential Nodes in Complex Networks», IEEE Access, vol. 8, pp. 65462-65471, 2020, doi: 10.1109/ACCESS.2020.2984286.

​Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). Machine Learning and Deep Learning Approaches for CyberSecurity: A Review. IEEE Access, 10, 19572-19585. https://doi.org/10.1109/ACCESS.2022.3151248

​Hina, M., Ali, M., Javed, A. R., Ghabban, F., Khan, L. A., & Jalil, Z. (2021). SeFACED: Semantic-Based Forensic Analysis and Classification of E-Mail Data Using Deep Learning. IEEE Access, 9, 98398-98411. https://doi.org/10.1109/ACCESS.2021.3095730

​Kabla, A. H. H., Anbar, M., Manickam, S., & Karupayah, S. (2022). Eth-PSD: A Machine Learning-Based Phishing Scam Detection Approach in Ethereum. IEEE Access, 10, 118043-118057. https://doi.org/10.1109/ACCESS.2022.3220780

​Karim, A., Shahroz, M., Mustofa, K., Belhaouari, S. B., & Joga, S. R. K. (2023). Phishing Detection System Through Hybrid Machine Learning Based on URL. IEEE Access, 11, 36805-36822. https://doi.org/10.1109/ACCESS.2023.3252366

​Khan, F., Ncube, C., Ramasamy, L. K., Kadry, S., & Nam, Y. (2020). A Digital DNA Sequencing Engine for Ransomware Detection Using Machine Learning. IEEE Access, 8, 119710-119719. https://doi.org/10.1109/ACCESS.2020.3003785

​Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. 2.

​Larriva-Novo, X. A., Vega-Barbas, M., Villagrá, V. A., & Sanz Rodrigo, M. (2020). Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies. IEEE Access, 8, 9005-9014. https://doi.org/10.1109/ACCESS.2019.2963407

​Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. M. (2018). A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View. IEEE Access, 6, 12103-12117. https://doi.org/10.1109/ACCESS.2018.2805680

​Loor-Zambrano, B., Tello-Salvador, F., Alcivar-Cevallos, R., & Vaca-Cardenas, L. (2021). Approaches of predictive and clustering methods used in emergency events: A Systematic Literature Review. 2021 XLVII Latin American Computing Conference (CLEI), 1-8. https://doi.org/10.1109/CLEI53233.2021.9640022

​Luna-López, M., Hernández-Lozano, M., Aldana-Franco, R., Alvarez Sanchez, E., Leyva-Retureta, J., Ricaño-Herrera, F., & Aldana-Franco, F. (2021). Sistema inteligente de monitoreo para condiciones ambientales en Industria 4.0. Científica, 25, 1-10. https://doi.org/10.46842//ipn.cien.v25n2a07

​Mahfouz, A., Abuhussein, A., Alsubaei, F., & Shiva, S. (2022). Toward A Holistic, Efficient, Stacking Ensemble Intrusion Detection System using a Real Cloud-based Dataset. International Journal of Advanced Computer Science and Applications, 13, 2022. https://doi.org/10.14569/IJACSA.2022.01309110

​Masadeh, M., Davanager, H., & Muaad, A. Y. (2022). A Novel Machine Learning-Based Framework for Detecting Religious Arabic Hatred Speech in Social Networks. International Journal of Advanced Computer Science and Applications, 13, 2022. https://doi.org/10.14569/IJACSA.2022.0130991

​Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M.-S., & Zeineddine, H. (2019). An Experimental Study With Imbalanced Classification Approaches for Credit Card Fraud Detection. IEEE Access, 7, 93010-93022. https://doi.org/10.1109/ACCESS.2019.2927266

​Massaro, A., Gargaro, M., Dipierro, G., Galiano, A. M., & Buonopane, S. (2020). Prototype Cross Platform Oriented on Cybersecurity, Virtual Connectivity, Big Data and Artificial Intelligence Control. IEEE Access, 8, 197939-197954. https://doi.org/10.1109/ACCESS.2020.3034399

​Nahhas, L., Albahar, M., Alammari, A., & Jurcut, A. (2022). Android Malware Detection Using ResNet-50 Stacking. Computers, Materials & Continua, 74(2), 3997-4014. https://doi.org/10.32604/cmc.2023.028316

​Ordoñez-Tumbo, S., Márceles-Villalba, K., Amador-Donado, S., Ordoñez-Tumbo, S., Márceles-Villalba, K., & Amador-Donado, S. (2022). An adaptable Intelligence Algorithm to a Cybersecurity Framework for IIOT. Ingeniería y Competitividad, 24(2). https://doi.org/10.25100/iyc.v24i2.11762

​Otoom, M. M., Sattar, K. N. A., & Al Sadig, M. (2023). Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48. Advances in Science and Technology. Research Journal, Vol. 17(no 2). https://doi.org/10.12913/22998624/161820

​Prabha, P. S., & Kumar, S. M. (2022). A Novel Cyber-attack Leads Prediction System using Cascaded R2CNN Model. International Journal of Advanced Computer Science and Applications, 13(2). https://doi.org/10.14569/IJACSA.2022.0130260

​Predicting Malicious Software in IoT Environment Based on Machine Learning and Data Mining Techniques—ProQuest. (s. f.). Recuperado 6 de septiembre de 2023, de https://www.proquest.com/openview/ad34f3c57aa402f75d6047227dbce013/1?pq-origsite=gscholar&cbl=5444811

​Rizvi, S., Scanlon, M., Mcgibney, J., & Sheppard, J. (2022). Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions. IEEE Access, 10, 110362-110384. https://doi.org/10.1109/ACCESS.2022.3214506

​Sensors | Free Full-Text | An Insight into the Machine-Learning-Based Fileless Malware Detection. (s. f.). Recuperado 6 de septiembre de 2023, de https://www.mdpi.com/1424-8220/23/2/612

​Sun, B., Ban, T., Han, C., Takahashi, T., Yoshioka, K., Takeuchi, J., Sarrafzadeh, A., Qiu, M., & Inoue, D. (2021). Leveraging Machine Learning Techniques to Identify Deceptive Decoy Documents Associated With Targeted Email Attacks. IEEE Access, 9, 87962-87971. https://doi.org/10.1109/ACCESS.2021.3082000

​T. Mosa, D., Y. Shams, M., A. Abohany, A., M. El-kenawy, E.-S., & Thabet, M. (2023). Machine Learning Techniques for Detecting Phishing URL Attacks. Computers, Materials & Continua, 75(1), 1271-1290. https://doi.org/10.32604/cmc.2023.036422

​Veena, K., Meena, K., Kuppusamy, R., Teekaraman, Y., Angadi, R. V., & Thelkar, A. R. (2022). Cybercrime: Identification and Prediction Using Machine Learning Techniques. Computational Intelligence and Neuroscience, 2022, e8237421. https://doi.org/10.1155/2022/8237421

​Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., & Venkatraman, S. (2019). Robust Intelligent Malware Detection Using Deep Learning. IEEE Access, 7, 46717-46738. https://doi.org/10.1109/ACCESS.2019.2906934

​Wan Ali, W. N. H., Mohd, M., Fauzi, F., Shirai, K., & Noor, M. (2021). IMPLEMENTATION OF HYPERPARAMETER OPTIMISATION AND OVER-SAMPLING IN DETECTING CYBERBULLYING USING MACHINE LEARNING APPROACH. Malaysian Journal of Computer Science, 78-100. https://doi.org/10.22452/mjcs.sp2021no2.6

​Wei, Y., & Sekiya, Y. (2022). Sufficiency of Ensemble Machine Learning Methods for Phishing Websites Detection. IEEE Access, 10, 124103-124113. https://doi.org/10.1109/ACCESS.2022.3224781

​Wiafe, I., Koranteng, F. N., Obeng, E. N., Assyne, N., Wiafe, A., & Gulliver, S. R. (2020). Artificial Intelligence for Cybersecurity: A Systematic Mapping of Literature. IEEE Access, 8, 146598-146612. https://doi.org/10.1109/ACCESS.2020.3013145

​Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018). Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access, 6, 35365-35381. https://doi.org/10.1109/ACCESS.2018.2836950

​Yuan, J., Chen, G., Tian, S., & Pei, X. (2021). Malicious URL Detection Based on a Parallel Neural Joint Model. IEEE Access, 9, 9464-9472. https://doi.org/10.1109/ACCESS.2021.3049625

​Zeadally, S., Adi, E., Baig, Z., & Khan, I. A. (2020). Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity. IEEE Access, 8, 23817-23837. https://doi.org/10.1109/ACCESS.2020.2968045

​Zhang, S., Xie, X., & Xu, Y. (2020). A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity. IEEE Access, 8, 128250-128263. https://doi.org/10.1109/ACCESS.2020.3008433

​Zieni, R., Massari, L., & Calzarossa, M. C. (2023). Phishing or Not Phishing? A Survey on the Detection of Phishing Websites. IEEE Access, 11, 18499-18519. https://doi.org/10.1109/ACCESS.2023.3247135

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