By Alexander B. Sideridis, Zoe Kardasiadou, Constantine P. Yialouris, Vasilios Zorkadis (eds.)

This e-book constitutes the completely refereed post-conference complaints of the fifth overseas convention on safety, privateness and belief in a electronic international, e-Democracy 2013, held in Athens, Greece, December 2013. The 20 revised complete papers awarded have been conscientiously chosen from various submissions. The papers are equipped in topical sections on protection, privateness; e-governance; e-government purposes, virtualizations; politics, laws: ecu initiatives.

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Additional resources for E-Democracy, Security, Privacy and Trust in a Digital World: 5th International Conference, E-Democracy 2013, Athens, Greece, December 5-6, 2013, Revised Selected Papers

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IEEE Press, Spain (2010a) 26. : Collaborative security management services for Port Information Systems. In: Proceedings of International Conference on e-Business, pp. 305–308. SciTePress, Italy (2012a) 27. : STORM-RM: a collaborative and multicriteria risk management methodology. Int. J. Multicriteria Decis. Making 2(2), 159–177 (2012) 28. : STORM-RA: an implemented, collaborative, multicriteria decision making risk assessment methodology. In: 7th Meeting Multicriteria Decision Analysis, Greece (2010b) 29.

SciTePress, Italy (2012a) 27. : STORM-RM: a collaborative and multicriteria risk management methodology. Int. J. Multicriteria Decis. Making 2(2), 159–177 (2012) 28. : STORM-RA: an implemented, collaborative, multicriteria decision making risk assessment methodology. In: 7th Meeting Multicriteria Decision Analysis, Greece (2010b) 29. 0. Carnegie Mellon University, June 2001 (2010). org/octave/ 30. : Security management of the ports’ information systems. ENISA project (2013). eu. Accessed 4 Mar 2014 31.

Demertzis and L. Iliadis encoding based on Gaussian receptive fields. As a consequence of this transformation input neurons emit spikes at pre-defined firing times, invoking the one pass learning algorithm. The learning iteratively creates repositories of neurons, one repository for each class. Finally, the output of the second neural layer determines the class label of the presented input stimulus. The eSNN model was investigated in a number of scenarios and reported promising results. Moreover the MLFF ANN system is a pattern recognition system which detects the attacks and classifies them with high accuracy and adds a greater degree of integrity to the rest of security infrastructure of HESADM.

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