Alejandra Guadalupe Silva Trujillo

Dr. Alejandra Guadalupe Silva Trujillo

orcid

Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2024 in Journal of Sensors
Verified
Journal Article
Open access
GRGabriela Rangel
Gabriela Rangel
JCJuan Carlos Cuevas-Tello
Juan Carlos Cuevas-Tello
JNJose I. Nunez-Varela
Jose I. Nunez-Varela
CPCesar Puente
Cesar Puente
ATAlejandra Guadalupe Silva Trujillo
Alejandra Guadalupe Silva Trujillo
Convolutional neural networks (CNNs) have shown outstanding image classification performance, having been successfully applied in several real‐world applications. However, there are performance limitations encountered in CNNs and few works have pointed out these limitations across the literature. Therefore, this survey aims to provide a comprehensive explanation of both the importance and performance limitations of CNNs on image classification tasks. In this paper, we start with a brief CNN background and follow the main CNN architectures along with their contributions and benchmark datasets. We propose a classification of the CNNs performance limitations, which are grouped into four categories: labeled datasets, translation invariance, adversarial attacks, and spatial relationship. In addition, we describe some approaches that are currently being developed to overcome these performance limitations. Finally, we introduce specific ongoing research to address these performance limitations through capsule networks (CapsNets) and CNNs combined with CapsNets.

8 June 2023 in Sensors
Verified
Journal Article
Open access
PDF file
ATAlejandra Guadalupe Silva Trujillo
Alejandra Guadalupe Silva Trujillo
MGMauricio Jacobo González González
Mauricio Jacobo González González
LPLuis Pablo Rocha Pérez
Luis Pablo Rocha Pérez
Luis Javier García VillalbaLuis Javier García Villalba
Luis Javier García Villalba
Wearable devices are starting to gain popularity, which means that a large portion of the population is starting to acquire these products. This kind of technology comes with a lot of advantages, as it simplifies different tasks people do daily. However, as they recollect sensitive data, they are starting to be targets for cybercriminals. The number of attacks on wearable devices forces manufacturers to improve the security of these devices to protect them. Many vulnerabilities have appeared in communication protocols, specifically Bluetooth. We focus on understanding the Bluetooth protocol and what countermeasures have been applied during their updated versions to solve the most common security problems. We have performed a passive attack on six different smartwatches to discover their vulnerabilities during the pairing process. Furthermore, we have developed a proposal of requirements needed for maximum security of wearable devices, as well as the minimum requirements needed to have a secure pairing process between two devices via Bluetooth.

23 February 2024 in Computers
Verified
Journal Article
Open access
PDF file
RARyan Baker del Aguila
Ryan Baker del Aguila
CPCarlos Daniel Contreras Pérez
Carlos Daniel Contreras Pérez
ATAlejandra Guadalupe Silva Trujillo
Alejandra Guadalupe Silva Trujillo
JCJuan Carlos Cuevas-Tello
Juan Carlos Cuevas-Tello
JNJose I. Nunez-Varela
Jose I. Nunez-Varela
Recent advancements in cybersecurity threats and malware have brought into question the safety of modern software and computer systems. As a direct result of this, artificial intelligence-based solutions have been on the rise. The goal of this paper is to demonstrate the efficacy of memory-optimized machine learning solutions for the task of static analysis of software metadata. The study comprises an evaluation and comparison of the performance metrics of three popular machine learning solutions: artificial neural networks (ANN), support vector machines (SVMs), and gradient boosting machines (GBMs). The study provides insights into the effectiveness of memory-optimized machine learning solutions when detecting previously unseen malware. We found that ANNs shows the best performance with 93.44% accuracy classifying programs as either malware or legitimate even with extreme memory constraints.

7 December 2015 in The Journal of Supercomputing
Verified
Journal Article
Closed access
PDF file
JPJavier Portela
Javier Portela
Luis Javier García VillalbaLuis Javier García Villalba
Luis Javier García Villalba
ATAlejandra Guadalupe Silva Trujillo
Alejandra Guadalupe Silva Trujillo
AOAna Lucila Sandoval Orozco
Ana Lucila Sandoval Orozco
Tai-hoon KimTai-hoon Kim
Tai-hoon Kim

14 June 2018 in Multimedia Tools and Applications
Verified
Journal Article
Open access
PDF file
ATAlejandra Guadalupe Silva Trujillo
Alejandra Guadalupe Silva Trujillo
AOAna Lucila Sandoval Orozco
Ana Lucila Sandoval Orozco
Luis Javier García VillalbaLuis Javier García Villalba
Luis Javier García Villalba
Tai-hoon KimTai-hoon Kim
Tai-hoon Kim
The development of digital media, the increasing use of social networks, the easier access to modern technological devices, is perturbing thousands of people in their public and private lives. People love posting their personal news without consider the risks involved. Privacy has never been more important. Privacy enhancing technologies research have attracted considerable international attention after the recent news against users personal data protection in social media websites like Facebook. It has been demonstrated that even when using an anonymous communication system, it is possible to reveal user’s identities through intersection attacks or traffic analysis attacks. Combining a traffic analysis attack with Analysis Social Networks (SNA) techniques, an adversary can be able to obtain important data from the whole network, topological network structure, subset of social data, revealing communities and its interactions. The aim of this work is to demonstrate how intersection attacks can disclose structural properties and significant details from an anonymous social network composed of a university community.

1 November 2016 in Sensors
Verified
Journal Article
Open access
PDF file
JPJavier Portela
Javier Portela
Luis Javier García VillalbaLuis Javier García Villalba
Luis Javier García Villalba
ATAlejandra Guadalupe Silva Trujillo
Alejandra Guadalupe Silva Trujillo
AOAna Lucila Sandoval Orozco
Ana Lucila Sandoval Orozco
Tai-hoon KimTai-hoon Kim
Tai-hoon Kim
Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks.