Self-organizing software scan scheduling based on neural network cognitive classification

Abstract

An approach for updating of a repetition frequency of a system scan operation. The approach calculates values of asset management factors based on results of the asset management factors from a current and a previously performed system scan operation. Groups of the computer systems are treated as node equivalents, and the asset management factors are treated as synapse equivalents of the node equivalents. The approach also feeds values of the factors and weighing values as input for determining an update value for the repetition frequency as output. The weighing value is adaptable via the learning capability of the neural network equivalent. Finally, the repetition frequency is updated using the update value by an activation function.