Tesis
Permanent URI for this communityhttps://hdl.handle.net/11285/345119
Colección de Tesis y Trabajos de grado (informe final del proyecto de investigación, tesina, u otro trabajo académico diferente a Tesis, sujeto a la revisión y aceptación de una comisión dictaminadora) presentados por alumnos para obtener un grado académico del Tecnológico de Monterrey.
Para enviar tu trabajo académico al RITEC, puedes consultar este Infográfico con los pasos generales para que tu tesis sea depositada en el RITEC.
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- Enhancing BGP security with MAD anomaly detection system and machine learning techniques(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Romo Chavero, María Andrea; Cantoral Ceballos, José Antonio; emipsanchez; Botero Vega, Juan Felipe; Navarro Barrón, Francisco Javier; School of Engineering and Sciences; Campus Monterrey; Pérez Díaz, Jesús ArturoAnomalies in the Border Gateway Protocol (BGP) represent a signicant vulnerability in the Internet’s infrastructure, as they can cause widespread disruptions, trafc misdirection, and even security breaches. Proactive detection of these anomalies is vital to preserving network stability and preventing potential cyberattacks. In response to this challenge, we present the Median Absolute Deviation (MAD) anomaly detection system, which combines traditional statistical methods with advanced machine learning (ML) techniques for more precise and dynamic detection. Our approach introduces a novel adaptive threshold mechanism, allowing the system to adjust based on the changing conditions of network trafc. This dynamic thresholding signif- icantly improves the accuracy, precision, and F1-score of anomaly detection compared to the previous xed-threshold version. Additionally, we integrate the MAD system with a diverse ML classiers, including Random Forest, XGBoost, LightGBM, CatBoost, and ExtraTrees to enhance the system’s ability to identify complex patterns that indicate unusual BGP behavior.We evaluate our detection system on well-documented BGP anomaly events, such as the Slammer worm, Nimda, Code Red 1 v2, the Moscow blackout, and the Telekom Malaysia misconguration. The results show that our system when combined with ML models achieves an overall accuracy and F1-score of 0.99, demonstrating its effectiveness across various anomaly types. By using both statistical and ML models, the system is able to capture irregularities that could signal security threats, offering a more comprehensive detection solution.This research highlights the importance of combining statistical anomaly detection with ML to obtain a balance between accuracy and computational efciency. The system’s low resource requirements and minimal pre-processing make it highly scalable, allowing it to be potentially deployed in real-time on large-scale networks.
- Democracia y blockchain. La tecnología al servicio del sistema electoral mexicano. Horizonte 2030(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-11) González Reyna, Juan José; Díaz Domínguez, Alejandro; emigmmayorquin; Benavides Rincón, Guillermina; Sobrino Macías, M. Fernanda; Escuela de Gobierno y Transformación Pública; Sede EGAP Santa FeEn esta investigación se estudiará la aplicación de la plataforma blockchain para ofrecer distintos beneficios al sistema electoral mexicano, no solo en cuanto a certeza y transparencia, sino también en cuanto a seguridad, así como a promover el ahorro, pues en México, las cifras de gasto con motivos de los procesos de elecciones tienden a ascender en cada ejercicio presupuestal.
- Evaluating teaching performance of teaching-only and teaching-and-research professors in higher education through data analysis(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06) Chávez López, Mario Daniel; Cantú Ortiz, Francisco Javier; emipsanchez/tolmquevedo; Torres Delgado, Gabriela; Hernández Gress, Neil; Barrón Cano, Olivia Maricela; Escuela de Ingeniería y Ciencias; Campus Monterrey; Ceballos Cancino, Héctor GibránWe present a study that compares the teaching performance of Teaching-only versus Teaching-and-research professors at higher education institutions. It is a common belief that, generally, Teaching-only professors outperform Teaching-and-Research professors in teaching and research universities according to student perception reflected in student surveys. We present a case study which demonstrates that, in the vast majority of the cases, it is not necessarily true. Our work analyzes these two type of professors at their ability to function as an intellectual challenger, learning guide and their tendency to be recommended to other students. The case study takes place at Tecnológico de Monterrey (Tec), a teaching and research private university in Mexico that has developed a research profile during the last two decades with a mix of teaching-only and teaching and research faculty members and shows a growing accomplishment on world university rankings. We use five datasets from a student survey called ECOA which accounts observations from 2016 to 2019. We present the results of statistical and machine learning methods applied when the taught courses of more than nine thousand professors are taken into account. Methods include Analysis of Variance, Logistic Regression, Recursive Feature Elimination, Coarsened Exact Matching and Panel Data. Contrary to common belief we show that, for the case presented, teaching and research professors perform better or at least the same as teaching-only professors. We also document the differences found on teaching with respect to attributes related to courses and professors.
- A hybrid metaheuristic optimization approach for the synthesis of operating procedures for optimal drum-boiler startups(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020) Garduño Hernández, Emilio; BATRES PRIETO, RAFAEL; 589386; NOGUEZ MONROY, JUANA JULIETA; 202512; PONCE CRUZ, PEDRO; 31857; Batres Prieto, Rafael; RR; Noguez Monroy, Juana Julieta; Ponce Cruz, Pedro; Escuela de Ingeniería y Ciencias; Campus Ciudad de MéxicoA steam generator serves as a power generation equipment that uses the expansive power of the steam to generate electricity. The startup process of a steam generator plays an important role in the ability of a power plant to adjust its electricity generation to changes in demand. As renewable generation plants increase, the levels of variability in electricity production increase. Fast startups become instrumental as they enable traditional power generation plants to provide the quantity of electricity missing when variable renewable energies cannot satisfy demand. A main equipment involved in the startup process of the steam generator is the drum boiler. However, if the startup process is carried out too fast, excessive thermal stresses can occur and provoke damage to the components of the drum boiler. This thesis proposes a dynamic optimization methodology to synthesize operating valve profiles that minimize the startup time of the drum boiler while avoiding the excessive formation of thermal stresses. Since valve operations influence the time-varying behavior of the steam, dynamic simulation is needed in order to evaluate the operating procedure. This thesis proposes a dynamic optimization approach with a hybrid-metaheuristic algorithm that generates the optimal startup procedure of a drum boiler. The proposed algorithm is based on two important elements of two metaheuristic algorithms. Namely, the search zone in the cooling element from the simulated annealing algorithm and the efficient computational performance provided from the tabu search algorithm memory structures. A case study evaluates the proposed approach by comparing it against results previously published in the literature.