Listar por autor UPNA "Orduna Urrutia, Raúl"
Mostrando ítems 1-4 de 4
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Attacking bitcoin anonymity: generative adversarial networks for improving bitcoin entity classification
Classification of Bitcoin entities is an important task to help Law Enforcement Agencies reduce anonymity in the Bitcoin blockchain network and to detect classes more tied to illegal activities. However, this task is ... -
Bitcoin and cybersecurity: temporal dissection of blockchain data to unveil changes in entity behavioral patterns
The Bitcoin network not only is vulnerable to cyber-attacks but currently represents the most frequently used cryptocurrency for concealing illicit activities. Typically, Bitcoin activity is monitored by decreasing anonymity ... -
Generative adversarial networks for bitcoin data augmentation
In Bitcoin entity classification, results are strongly conditioned by the ground-truth dataset, especially when applying supervised machine learning approaches. However, these ground-truth datasets are frequently affected ... -
Network traffic analysis through node behaviour classification: a graph-based approach with temporal dissection and data-level preprocessing
Network traffic analysis is an important cybersecurity task, which helps to classify anomalous, potentially dangerous connections. In many cases, it is critical not only to detect individual malicious connections, but to ...