Publications

@article{javierblanco004,

title = {Machine Learning Predictors for Min-Entropy Estimation},

author = {Javier Blanco-Romero and Vicente Lorenzo and Florina Almenares-Mendoza and Daniel Díaz-Sánchez},

url = {https://www.mdpi.com/1099-4300/27/2/156},

doi = {https://doi.org/10.3390/e27020156},

year  = {2025},

date = {2025-02-02},

urldate = {2025-02-02},

journal = {Entropy 2025},

volume = {27},

number = {156},

issue = {2},

pages = {1-31},

abstract = {This study investigates the application of machine learning predictors for the estimation of min-entropy in random number generators (RNGs), a key component in cryptographic applications where accurate entropy assessment is essential for cybersecurity. Our research indicates that these predictors, and indeed any predictor that leverages sequence correlations, primarily estimate average min-entropy, a metric not extensively studied in this context. We explore the relationship between average min-entropy and the traditional min-entropy, focusing on their dependence on the number of target bits being predicted. Using data from generalized binary autoregressive models, a subset of Markov processes, we demonstrate that machine learning models (including a hybrid of convolutional and recurrent long short-term memory layers and the transformer-based GPT-2 model) outperform traditional NIST SP 800-90B predictors in certain scenarios. Our findings underscore the importance of considering the number of target bits in min-entropy assessment for RNGs and highlight the potential of machine learning approaches in enhancing entropy estimation techniques for improved cryptographic security.},

keywords = {autoregressive processes, compromise, I-Shaper, machine learning predictors, min-entropy estimation, Qursa, random number generators},

pubstate = {published},

tppubtype = {article}

}

@article{almenarez018,

title = {Authorisation models for IoT environments: A survey},

author = {Jaime Pérez-Díaz and Florina Almenares-Mendoza},

url = {https://www.sciencedirect.com/science/article/pii/S2542660524003718?via%3Dihub#d1e3887},

doi = {https://doi.org/10.1016/j.iot.2024.101430},

issn = {2542-6605},

year  = {2024},

date = {2024-11-23},

urldate = {2024-11-23},

journal = { www.elsevier.com/locate/iot},

abstract = {Authorization models are pivotal in the Internet of Things (IoT) ecosystem, ensuring secure management of data access and communication. These models function after authentication, determining the specific actions that a device is allowed to perform. This paper aims to provide a comprehensive and comparative analysis of authorization solutions within IoT contexts, based on the requirements identified from the existing literature. We critically assess the functionalities and capabilities of various authorization solutions, particularly those designed for IoT cloud platforms and distributed architectures. Our findings highlight the urgent need for further development of authorization models optimized for the unique demands of IoT environments. Consequently, we address both the persistent challenges and the gaps within this domain. As IoT continues to reshape the technological landscape, the refinement and adaptation of authorization models remain imperative ongoing pursuits.},

keywords = {ABAC, compromise, DAC, I-Shaper, IoT, MAC, Models, Platforms, Qursa, RBAC, Security},

pubstate = {published},

tppubtype = {article}

}

@article{javierblanco003,

title = {Evaluating integration methods of a quantum random number generator in OpenSSL for TLS},

author = {Javier Blanco-Romero and Vicente Lorenzo and Florina Almenares and Daniel Díaz-Sánchez and Carlos García-Rubio and Celeste Campo and Andrés Marín},

url = {https://www.sciencedirect.com/science/article/pii/S1389128624007096?via%3Dihub},

doi = {https://doi.org/10.1016/j.comnet.2024.110877},

isbn = {1389-1286},

year  = {2024},

date = {2024-10-25},

urldate = {2024-10-25},

volume = {255},

publisher = {Computer Networks},

abstract = {The rapid advancement of quantum computing poses a significant threat to conventional cryptography. Whilst post-quantum cryptography (PQC) stands as the prevailing trend for fortifying the security of cryptographic systems, the coexistence of quantum and classical computing paradigms presents an opportunity to leverage the strengths of both technologies, for instance, nowadays the use of Quantum Random Number Generators (QRNGs) – considered as True Random Number Generators (TRNGs) – opens up the possibility of discussing hybrid systems. In this paper, we evaluate both aspects, on the one hand, we use hybrid TLS (Transport Layer Security) protocol that leverages the widely used secure protocol on the Internet and integrates PQC algorithms, and, on the other hand, we evaluate two approaches to integrate a QRNG, i.e., Quantis PCIe-240M, in OpenSSL 3.0 to be used by TLS. Both approaches are compared through a Nginx Web server, that uses OpenSSL’s implementation of TLS 1.3 for secure web communication. Our findings highlight the importance of optimizing such integration method, because while direct integration can lead to performance penalties specific to the method and hardware used, alternative methods demonstrate the potential for efficient QRNG deployment in cryptographic systems.},

keywords = {compromise, I-Shaper, Linux, OpenSSL, QRNGs, Quantum random number generators, Qursa, TLS},

pubstate = {published},

tppubtype = {article}

}

@article{marta001,

title = {Reducing DNS Traffic to Enhance Home IoT Device Privacy},

author = {Marta Moure-Garrido and Carlos García-Rubio and Celeste Campo},

url = {https://www.mdpi.com/1424-8220/24/9/2690/pdf?version=1713941333},

doi = {https://doi.org/10.3390/s24092690},

year  = {2024},

date = {2024-04-24},

urldate = {2024-04-24},

journal = {Sensors },

volume = {24},

issue = {9},

publisher = {Sensors 2024},

abstract = {The deployment of Internet of Things (IoT) devices is widespread in different environments, including homes. Although security is incorporated, homes can become targets for cyberattacks because of their vulnerabilities. IoT devices generate Domain Name Server (DNS) traffic primarily for communication with Internet servers. In this paper, we present a detailed analysis of DNS traffic from IoT devices. The queried domains are highly distinctive, enabling attackers to easily identify the IoT device. In addition, we observed an unexpectedly high volume of queries. The analysis reveals that the same domains are repeatedly queried, DNS queries are transmitted in plain text over User Datagram Protocol (UDP) port 53 (Do53), and the excessive generation of traffic poses a security risk by amplifying an attacker’s ability to identify IoT devices and execute more precise, targeted attacks, consequently escalating the potential compromise of the entire IoT ecosystem. We propose a simple measure that can be taken to reduce DNS traffic generated by IoT devices, thus preventing it from being used as a vector to identify the types of devices present in the network. This measure is based on the implementation of the DNS cache in the devices; caching few resources increases privacy considerably.},

keywords = {compromise, DNS, I-Shaper, IoT privacy, network traffic, Qursa},

pubstate = {published},

tppubtype = {article}

}

@article{campo002,

title = {Real time detection of malicious DoH traffic using statistical analysis },

author = {Marta Moure-Garrido and Celeste Campo-Vázquez and Carlos García-Rubio},

url = {http://hdl.handle.net/10016/38151},

doi = {https://doi.org/10.1016/j.comnet.2023.109910},

issn = {1389-1286},

year  = {2023},

date = {2023-10-09},

urldate = {2023-10-09},

journal = {COMPUTER NETWORKS},

volume = {234},

issue = {109910},

pages = {1-10},

abstract = {The DNS protocol plays a fundamental role in the operation of ubiquitous networks. All devices connected to these networks need DNS to work, both for traditional domain name to IP address translation, and for more advanced services such as resource discovery. DNS over HTTPS (DoH) solves certain security problems present in the DNS protocol. However, malicious DNS tunnels, a covert way of encapsulating malicious traffic in a DNS connection, are difficult to detect because the encrypted data prevents performing an analysis of the content of the DNS traffic.



In this study, we introduce a real-time system for detecting malicious DoH tunnels, which is based on analyzing DoH traffic using statistical methods. Our research demonstrates that it is feasible to identify in real-time malicious traffic by analyzing specific parameters extracted from DoH traffic. In addition, we conducted statistical analysis to identify the most significant features that distinguish malicious traffic from benign traffic. Using the selected features, we achieved satisfactory results in classifying DoH traffic as either benign or malicious.},

keywords = {classification, compromise, computer science, cynamon, dns tunnels, doh traffic, intrusion detection system, malicious doh, Qursa, statistical analysis},

pubstate = {published},

tppubtype = {article}

}