Current techniques employed in security alert correlation area for multi-step attack recognition purpose are intricate to be performed due to the complexity of the methods and huge computing workload generated during alert analysis and processing. In this paper, we proposed a new method of alert correlation aiming at providing concentrated security event information and thus finding multi-step ...

We study statistical estimators computed using iterative optimization methods that are not run until completion. Classical results on maximum likelihood (MLEs) assert a one-step estimator (OSE), in which single Newton-Raphson iteration is performed from starting point with certain properties, asymptotically equivalent to the MLE. further develop these early-stopping by deriving properties of de...

A new method of numerical integration is presented here, the variable-step Störmer-Cowell method. The method uses error control to regulate the step size, so larger step sizes can be taken when possible, and is double-integration, so only one evaluation per step is necessary when integrating second-order differential equations. The method is not variable-order, because variable-order algorithms...

The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to single large model learned end-to-end without sub-tasks, presented. Pre-optimized ML models are better performance obtained by re-optimizing the one. selection an from several small candidates for each sub-task has been performed...