As the effectiveness of modern Internet-based anonymization infrastructures grows, law enforcement agencies are experiencing a progressive erosion of their surveillance capabilities. This can severely undermine their efforts to prevent and investigate various types of unlawful activities, potentially increasing the impunity of organized criminal networks. Balancing the legitimate privacy needs of individuals with the imperative to maintain public safety and combat criminal behavior in the digital world remains a complex tradeoff for both policymakers and technologists who need to find a systematic and reliable way to link the traffic traces associated with criminal activities to their anonymized origins. Accordingly, this paper presents a simple but very effective de-anonymization approach capable of associating traffic traces captured at the edge of the overlay infrastructures, in correspondence with the true origins, to those captured in correspondence with the destinations. The approach is based on determining the minimum-distance pairs within a complete bipartite graph in which the traffic traces are the nodes. Experiments with different distance functions, applied in varied ways, show that the resulting framework appears to be a promising solution that is scalable and easily deployable on real-life network equipment. • A framework to de-anonymize traffic based on distances between descriptions of traffic. • An estimation of the confidence in results is provided. • The proposed solution is interpretable and it scales well.

This paper proposes a prediction model based on fuzzy logic applied to anticipate electricity production and consumption in a building equipped with photovoltaics and connected to the grid. The goal is a smart energy management system able to make decisions and to adapt the consumption to the actual context and to the future electricity levels. The interest is to use as much electricity as possible from own production. The surplus is captured by an energy storage system or is sent to the grid. When no electricity is available from self-production, the grid is used to cover the necessities. The evaluations are performed on a data set collected in a real household. The proposed method is compared in terms of mean absolute error with other existing methods. The method developed based on fuzzy logic has an error of about 67 W, which places it among the most efficient models.

Modern interconnected power grids are a critical target of many kinds of cyber-attacks, potentially affecting public safety and introducing significant economic damages. In such a scenario, more effective detection and early alerting tools are needed. This study introduces a novel anomaly detection architecture, empowered by modern machine learning techniques and specifically targeted for power control systems. It is based on stacked deep neural networks, which have proven to be capable to timely identify and classify attacks, by autonomously eliciting knowledge about them. The proposed architecture leverages automatically extracted spatial and temporal dependency relations to mine meaningful insights from data coming from the target power systems, that can be used as new features for classifying attacks. It has proven to achieve very high performance when applied to real scenarios by outperforming state-of-the-art available approaches.

SARS-CoV-2 is a novel severe acute respiratory syndrome-like coronavirus (SARS-CoV), which is responsible of the ongoing world pandemic of COVID-19 disease. Although many approaches are being investigated to address this issue, nowaday there are no vaccines available and there is little evidence supporting the efficiency of potential therapeutic agents. Moreover, the high mutation rate of this virus heavily affects the understanding of its evolution and diffusion mechanisms, and, in turn, the development of effective solutions. In this study, two novel algorithms are provided for finding out recurrent patterns of nucleotide subsequences of different SARS-CoV-2 genomes as a unique signature capable of identifying the most peculiar features of the pathogen. In particular, we provide several subsequence patterns related to the Spike glycoprotein, which is believed to be the main target for developing effective drugs and vaccines against the COVID-19 disease because of its role in the entrance of coronaviruses into host cells. The experimental results, obtained by analyzing 5000 genomes of SARS-CoV-2, have shown that the extracted patterns are able to recognize the Spyke protein in the 99.35% of the considered genomes. In addition, such patterns have proven to be highly discriminating with respect to other pathogenic genomes, such as SARS, Middle East respiratory syndrome, Nipah, and the streptococcus bacteria. We hope that the findings presented in this study can help specialists in speeding up the design of more accurate drugs or vaccines against SARS-CoV-2.