By Alexander B. Sideridis, Zoe Kardasiadou, Constantine P. Yialouris, Vasilios Zorkadis (eds.)
This e-book constitutes the completely refereed post-conference lawsuits of the fifth overseas convention on safeguard, privateness and belief in a electronic global, e-Democracy 2013, held in Athens, Greece, December 2013. The 20 revised complete papers awarded have been rigorously chosen from a variety of submissions. The papers are equipped in topical sections on safeguard, privateness; e-governance; e-government functions, virtualizations; politics, laws: eu initiatives.
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Additional info 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
Springer, Heidelberg (2012) 32. : Comparison of risk analysis methods: Mehari, Magerit, NIST800-30 and Microsoft’s Security Management Guide. org Abstract. With inception of Service-Orientation in research and industry, the need to select a Reference Architecture (RA) that supports Service Orientation in some speciﬁc domain has developed into a challenge. Institutionalizing a criterion that helps software designers and developers to properly extend or design an RA for a domain-speciﬁc, goal-aware and context-aware implementation of a Service-Oriented Architecture (SOA) system has evolved into a necessity.
These steps are the processes of identifying, quantifying and associating risk to assets. On the other hand, a risk assessment tool is oriented towards the measurement of the potential for losses that a threat could have on an organization. Tools are based on methodologies and most often a speciﬁc tool is developed for a speciﬁc methodology [1, 26]. These methodologies must be compliant to standards that deal with critical infrastructures’ safety and security. Table 1 provides a list of standards as well as of some best practices that address security and safety issues in ports.
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.