Random Decentering Algorithm (RDA) on a undirected unweighted graph is defined and tested over several concrete scale-free networks. RDA introduces ancillary nodes to the given network following basic principles of minimal cost, density preservation, centrality reduction and randomness. First simulations over scale-free networks show that RDA gives a significant decreasing of both betweenness centrality and closeness centrality and hence topological protection of network is improved. On the other hand, the procedure is performed without significant change of the density of connections of the given network. Thus ancillae are not distinguible from real nodes (in a straightforward way) and hence network is obfuscated to potential adversaries by our manipulation.
We focus the problem of classification of Raman spectra by using different techniques: Statistic models, neural networks and expert systems. We point out some results for the concrete problem of identification of explosive substances.
The study of cybersecurity incidents is an active research field. The purpose of this work is to determine accurate measures of cybersecurity incidents. An effective method to aggregate cybersecurity incident reports is defined to set these measures. As a result we are able to make predictions and, therefore, to deploy security policies. Forecasting time-series of those cybersecurity aggregates is performed based on Koopman's method and Dynamic Mode Decomposition algorithm. Both techniques have shown to be accurate for a wide variety of dynamical systems ranging from fluid dynamics to social sciences. We have performed some experiments on public databases. We show that the measure of the risk trend can be effectively forecasted.
INDEX TERMSCybersecurity, extended dynamic mode decomposition, Koopman operator, time series forecasting, threat prediction. MSC[2010]: 00A72 (General methods of simulation), 93A30 (Mathematical modeling), 65F20 (Overdetermined systems and pseudo-inverses), 68T10 (Pattern recognition), 37N40 (Dynamical systems in optimization and economics), 62-07 (Data analysis).
Cybersecurity aggregates are numerical data obtained by aggregation on features along a database of cybersecurity reports. These aggregates are obtained by integration of time-stamped tables using some recent results of non-standard calculus. Time-series of aggregates are shown to contain relevant information about the concrete system dealt with. Trend time series is also forecasted using known data-driven methods. Although absolute forecasting of trend time series is not obtained, a directional forecasting of trend time series is achieved thence validated by means of a rolling cross validation scheme on a public database of Scareware reports.
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