Browsing by Subject "Fingerprinting"

Now showing 1 - 2 of 2
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    Publication
    Open Access
    Behavioral fingerprinting to detect ransomware in resource-constrained devices
    (Elsevier, 2023-12) Sánchez Sánchez, Pedro Miguel; Von der Assen, Jan; Shushack, Dennis; Perales Gómez, Ángel Luis; Bovet, Gérôme; Martínez Pérez, Gregorio; Stiller, Burkhard; Huertas Celdrán, Alberto; Ingeniería y Tecnología de Computadores
    The Internet of Things (IoT), a network of interconnected devices, has grown and gained traction over the last few years. This paradigm can impact our lives while also providing significant economic benefits. However, although resource-constrained IoT devices offer numerous advantages, they are also vulnerable to cyberattacks. As a result, ransomware severely threatens IoT devices managing sensitive and relevant information. Solutions based on Machine and Deep Learning (ML/DL) that consider behavioral data have been identified as promising. However, most detection solutions have been developed for Windows-based systems, which generally have more resources than IoT devices. As a result, these solutions are not suitable for resource-constrained components. In addition, no solution compares the pros and cons of different behavioral dimensions of resource-constrained devices. Thus, this work presents a framework that combines three different behavioral sources with supervised and unsupervised ML/DL algorithms to detect and classify heterogeneous ransomware impacting resource-constrained spectrum sensors. A pool of experiments has demonstrated the suitability of the proposed solution and compared its performance with a rule-based system. In conclusion, the usage of resources combined with local outlier factor and decision tree are the most promising combinations to detect anomalies and classify ransomware while consuming CPU, RAM, and time of devices in a reduced manner.
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    Publication
    Open Access
    Evolution of web tracking protection in Chrome
    (2023-11-08) Pan, Ronghao; Ruiz-Martínez, Antonio; Ingeniería de la Información y las Comunicaciones
    In our society, protecting users’ privacy is of utmost importance, especially when users access websites. Increased awareness of privacy concerns has led web browsers to implement new mechanisms to improve privacy while browsing the Internet. In each new version of web browsers, it is claimed that they provide better improvements to protect our privacy. However, there is no analysis of these improvements. To cope with this issue, in this paper, we present an analysis of the privacy of different versions of the Chrome web browser. This analysis is based on the PrivacyScanner tool, which we have improved with the detection of additional tracking techniques. Our findings reveal that tracking protection has seen modest enhancements (namely, between Chrome version 83 and 90, we observed a 7.55% reduction in trackers and 4.76% decrease in Google Analytics elements). Therefore, despite these improvements, there is still ample room for further enhancement.