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999			_c5328 _d5328
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003			FR-PaOEC
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008			190601s2018 ||| o i|0| 0 eng d
035			_a(FR-PaOEC)
040			_aFR-PaOEC _cES-MaONT
100	1		_aBothos, John M.A. _93365
245	1	0	_aCorrelation and dependence analysis on cyberthreat alerts / _cJohn M.A. Bothos ... [el al.]
260			_aGeneva : _bInternational Telecommunication Union, _c2018.
300			_a7 p.
336			_atexto (visual) _2isbdcontent
337			_aelectrónico _2isbdmedia
338			_arecurso en línea _2rdacarrier
520	3		_aIn this paper, a methodology for the enhancement of computer networks' cyber-defense is presented. Using a time-series dataset, drawn for a 60-day period and for 12 hours per day and depicting the occurrences of cyberthreat alerts at hourly intervals, the correlation and dependency coefficients that occur in an organization's network between different types of cyberthreat alerts are determined. Certain mathematical methods like the Spearman correlation coefficient and the Poisson regression stochastic model are used. For certain types of cyberthreat alerts, results show a significant positive correlation and dependence between them. The analysis methodology presented could help the administrative and IT managers of an organization to implement organizational policies for cybersecurity.
650		0	_aTecnologías habilitadoras digitales _918
653		4	_aScience and Technology
700	1		_aThanos, Konstantinos-Georgios _93366
700	1		_aKyriazanos, Dimitris M. _93367
700	1		_aVardoulias, George _93368
700	1		_aZalonis, Andreas _93369
700	1		_aPapadopoulou, Eirini _93370
700	1		_aCorovesis, Yannis _93371
700	1		_aThomopoulos, Stelios C.A. _93372
773	0		_04843 _tITU Journal: ICT Discoveries _gVol. 2018, no. 1, p. 129-135 _q2018:1<129 _x2616-8375
856	4	0	_aitu-ilibrary.org _uhttps://www.itu.int/dms_pub/itu-s/opb/journal/S-JOURNAL-ICTS.V1I1-2018-15-PDF-E.pdf _yAcceso al documento _x0 _qpdf
942			_cART _2udc