Computer workstation vetting by supply current monitoring

It is our goal within this project to develop a powerful electronic system capable to claim, with high certainty, that a malicious software is running (or not) along with the workstations’ normal activity. The new product will be based on measurement of the supply current taken by a workstation from the grid. Unique technique is proposed within these proceedings that analyses the supply current to produce information about the state of the workstation and to generate information of the presence of malicious software running along with the rightful applications. The testing is based on comparison of the behavior of a fault-free workstation (established i advance) and the behavior of the potentially faulty device

Annealing Based Dynamic Learning in Second--Order Neural Networks

An algorithm that simultaneously determines an appropriate number of neurons and their interaction parameters in a single hidden layer feed-forward neural network (NN) classification model is proposed. First, a large pool of candidate hidden units with second--order inputs interaction is constructed. Next, the hidden layer is designed by selecting appropriate units from the pool. This is achieved through global hidden layer optimization by a simulated annealing technique that adds and deletes hidden units as needed. Experimental results using the proposed model show improved generalization and reduced complexity as compared to previous constructive learning algorithms based on greedy design techniques. 1. Introduction Determining an appropriate NN topology is a challenging problem that usually requires an expensive trial--and--error process. Rather than learning on a pre--specified network structure, the algorithm proposed in this paper learns network topology as well. The advantage ..