Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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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.
- Anomaly detection in a cost-effective conveyor belt testing system(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-09) Solórzano Souza, Ana Paula; Navarro Durán, David; mtyahinojosa, emipsanchez; Galluzzi Aguilera, Renato; Sotelo Molina, Carlos Gustavo; Navarro Durán, David; School of Engineering and Sciences; Campus Ciudad de MéxicoThe industrial manufacturing sector faces significant challenges regarding energy efficiency and operational sustainability, particularly in the management of motor driven systems such as conveyor belts. While traditional diagnostic methods effectively identify faults, a critical gap remains in closing the control loop to enable autonomous corrective actions, specifically for mechanical tension regulation y conveyor belts. This research addresses this limitation by designing, implementing, and validating a low-cost, small-scale prototype capable of soft real-time energy monitoring, anomaly detection, and automatic physical correction. The system utilizes an ESP32 microcontroller and an INA219 sensor to analyze voltage and current signals, employing unsupervised machine learning algorithms such as Isolation Forest (IF) and One-Class Support Vector Machine (OC-SVM). This serves to diagnose tension states defined by specific deflection thresholds. To execute the physical corrections, the platform integrates a custom built rack and pinion mechanism driven by a servomotor, which automatically regulates the belt tension. Methodologically, the study characterized optimal operational conditions at a 30% PWM duty cycle and applied a median filter that reduced signal variability. Four models were trained using exclusively optimal-state data under univariate and multivariate configurations. Experimental results demonstrate that electrical parameters serve as reliable indicators of mechanical tension in a small scale conveyor belt. The Univariate One-Class SVM model applied to voltage yielded the highest performance, achieving an F1-Score of 0.8624. Meanwhile, the Multivariate OC-SVM model demonstrated high reliability with a Precision of 90.51% and a Recall of 72.95%. Upon detection of anomalies (slippage or excessive tightness), the system successfully triggered autonomous adjustments via a rack and pinion mechanism. These findings validate the feasibility of using accessible monitoring to implement intelligent, self-correcting maintenance systems, offering a scalable solution to minimize downtime and energy waste in industrial environments.
- Road surface monitoring system through machine learning classification ensemble models(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12) Arce Sáenz, Luis Alejandro; Bustamante Bello, Martin Rogelio; puelquio, emipsanchez; Villagra Serrano, Jorge; Galluzzi Aguilera, Renato; Ramírez Mendoza, Ricardo Ambrocio; School of Engineering and Sciences; Campus Ciudad de México; Izquierdo Reyes, JavierThe development of megacities is currently the scene of many problems; an important one to consider is the quality and efficiency of their mobility. An essential factor impacting this is the quality of their road networks, which can affect the durability and safety of ground transportation systems. Mexico City is a great example of such deficiencies. Therefore smart mobility strategies and planning in terms of logistics have been proposed, but few technological integrations have been implemented. In this work, a platform capable of monitoring surface defects in road pavement using Inertial Measurement Units and Machine Learning classification models was designed and developed. This was achieved by recording accelerometer and gyroscope measurements on a test vehicle's damped and undamped mass while driving on Mexico City streets. The measurements were labeled to identify and classify general and specific elements of road irregularities: smooth and uneven road segments, potholes, manholes, speed bumps, and patches. It is described as a methodology for preprocessing the data through time series analysis and feature extraction in the time and frequency domain. Four ensemble models were trained using the best classification models out of eight candidates; an exhaustive grid search methodology was used to select the best classification models per category and optimize the system's performance. Finally, the algorithms and models were loaded into a cloud instance to process incoming raw data; the resultant predictions were stored in a cloud database to be visualized on a web platform.