Ciencias Exactas y Ciencias de la Salud

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Modeling power control in WCDMA for multimedia support
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2010-12-01) Pineda Rico, Ulises; PINEDA RICO, ULISES; 179307; ITESM
Modern Third Generation Wireless Networks demand more and more resources in order to satisfy customers� needs. And these resources can only be provided by a good Power Control. However, power control needs an algorithm in order to work at the margin of the Quality of Service (QoS) requirements. This work is related to this; we propose a power control algorithm modeled under probabilistic criteria. This means, applying a Markovian model to a MAC Protocol (power control algorithm), in order to optimize the power assigment to each user in the system. This protocol is highly interrelated to the power control functionality in order to extract the maximum capacity and flexibility out of the WCDMA scheme. Once it is done, the algorithm is submitted to different scenarios to demonstrate its capabilities and limitations, also thus analyze its behavior under different conditions and viewpoints. At the end, conclusions are listed, together with future work and proposals to strengthen this work.
Low Overhead Host-Based IDS
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 01/07/2004) Aguilar Rodríguez, Ignacio J.; Max Perera, Jorge Carlos; Rodriguez Morales, José Ramón; Aguilar Coutiño, Artemio; ITESM
The area of Intrusion Detection is very important these days. Companies have acquired more interest in having this type of systems beacuse of the importance that information has for them. Machine learning algorithms are being used along with IDSs as an efficient approach. For these reasons we work with this approach in this thesis, presenting from general to specific, the information of the models and types of IDSs, and some machine learning algorithms and some fusion rules for them, that can help achieving a good IDS. In this work, we focus on Host-based intrusion detection, and three machine learning algorithms, which are C4.5, RIPPER and PART. It is showed a method to reduce false alarm rates and with this, increasing the possibility of detecting true alarms when our system trigger them.

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