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Sub-optimal topological protection strategy from advanced malware

TitoloSub-optimal topological protection strategy from advanced malware
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2013
AutoriArbore, A., and Fioriti Vincenzo
RivistaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6983 LNCS
Paginazione81-92
ISSN03029743
Parole chiaveComputer crime, Critical infrastructures, Defensive strategies, Eigenvalues and eigenfunctions, Electronics devices, Epidemic spreading, High-level analysis, Malwares, Optimization, Protection strategy, Structures and organization in complex systems, Threshold
Abstract

The spreading of dangerous malware in inter-dependent networks of electronics devices has raised deep concern, because from the ICT networks infections may propagate to other Critical Infrastructures producing the well-known domino effect. Researchers are attempting to develop a high level analysis of malware propagation, discarding software details, in order to generalize to the maximum extent the defensive strategies. It has been suggested that the maximum eigenvalue could act as a threshold for the malware's spreading. In this paper we study the Italian Internet Autonomous System simulating the diffusion of a worm, verifying the theoretical threshold and showing how to choose in a sub-optimal way the set of most influential nodes to protect with respect to the spectral paradigm. Our algorithm is fast and outperforms measures as degree, closeness, betweenness, and dynamical importance. © 2013 Springer-Verlag.

Note

cited By 0; Conference of 6th International Workshop on Critical Information Infrastructures Security, CRITIS 2011 ; Conference Date: 8 September 2011 Through 9 September 2011; Conference Code:100143

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84885821869&doi=10.1007%2f978-3-642-41476-3_7&partnerID=40&md5=2a02032ad4255aab43c9c866fca5ccd3
DOI10.1007/978-3-642-41476-3_7
Citation KeyArbore201381