Commit af0790c9dddbc170d5a251ccdd6283f1350313a2

Authored by Miguel Tuñón
1 parent 79d24d2f

Only conclusions left

Showing 89 changed files with 1002 additions and 653 deletions
ISO27001effectiveness/NAMESPACE
1 1 # Generated by roxygen2: do not edit by hand
2 2  
  3 +export(GetAttackTypeEvolution)
  4 +export(GetAttackTypePie)
  5 +export(GetAttackTypeSigleEvolution)
  6 +export(GetAttacksEvolution)
  7 +export(GetAttacksMonthEvolution)
  8 +export(GetCertsEvolution)
  9 +export(GetContinentAttackCol)
  10 +export(GetContinentAttacksEvolution)
  11 +export(GetContinentAttacksSigleEvolution)
  12 +export(GetContinentCertsEvolution)
  13 +export(GetContinentCertsSingleEvolution)
  14 +export(GetContinentPie)
  15 +export(GetCountriesAttacksSingleEvolution)
  16 +export(GetCountriesCertsSingleEvolution)
3 17 export(GetDefaultAttacksData)
4 18 export(GetISOSurveyCertsPerCountry)
5 19 export(GetISOSurveyCertsPerSector)
6 20 export(GetISOSurveySitesPerCountry)
7   -export(GetReportGraphs)
8 21 export(ParseHMExcel)
9 22 export(ParseHMFolder)
10 23 export(ProccesISOSurveyByCountryRaw)
... ...
ISO27001effectiveness/R/ReportGraphs.R
... ... @@ -301,6 +301,17 @@ GetAttackTypeSigleEvolution <- function(Attacks,
301 301 #-------------------------Geolocal evolution---------
302 302 #----------------------------------------------------------------
303 303  
  304 +#' Return pie graph to show % of attacks and certifications for each continent
  305 +#'
  306 +#' @param Attacks data.frame with procesed source data of attacks
  307 +#' @param Attacks.label.vjust Vector of vertical just to each portion label
  308 +#' @param Attacks.label.hjust Vector of horizontal just to each portion label
  309 +#' @param Cert_PerCountry data.frame with procesed source data of certifications
  310 +#' @param Certs.label.vjust Vector of vertical just to each portion label
  311 +#' @param Certs.label.hjust Vector of horizontal just to each portion label
  312 +#'
  313 +#' @return list(AttacksGraph, CertsGraph, ContinentListToStudy)
  314 +#' @export
304 315 GetContinentPie <- function (Attacks,
305 316 Attacks.label.vjust = 0,
306 317 Attacks.label.hjust = 0,
... ... @@ -362,19 +373,33 @@ GetContinentPie &lt;- function (Attacks,
362 373 axis.title.y=element_blank()) +
363 374 ggtitle("ISO 27001")
364 375  
365   - list(graph1, unique(attack.pie[attack.pie$perc > 5,]$Continent),
366   - graph2, unique(cert.pie[cert.pie$perc > 5,]$Continent))
  376 + #Return graphs and union of continent to study
  377 + list(graph1, graph2,
  378 + union(unique(attack.pie[attack.pie$perc > 5,]$Continent),
  379 + unique(cert.pie[cert.pie$perc > 5,]$Continent)))
367 380 }
368 381  
369   -GetContinentAttacksEvolution <- function(Attacks){
370 382  
371   - attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Continent, Year)
  383 +#' Return graph to show the evolution of a attack types list
  384 +#'
  385 +#' @param Attacks data.frame with procesed source data
  386 +#' @param ContinentList List with continents to show
  387 +#'
  388 +#' @return list(graph, data.frame with ContinentList attacks data by year)
  389 +#' @export
  390 +GetContinentAttacksEvolution <- function(Attacks,
  391 + ContinentList){
  392 +
  393 + #Extract year from date
  394 + attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>%
  395 + group_by(Continent, Year) #Grouping atacks by continent and year
  396 + #Counting attacks for each continent and year
372 397 attacks.evol <- as.data.frame(table(attacks.evol$Continent, attacks.evol$Year))
373 398 colnames(attacks.evol) <- c("Continent", "Year","Attacks")
  399 + #Filtering by the ContinentList especified
  400 + attacks.evol <- attacks.evol[attacks.evol$Continent %in% ContinentList,]
374 401  
375   - attacks.evol <- attacks.evol[attacks.evol$Continent != "Oceania",]
376   - attacks.evol <- attacks.evol[attacks.evol$Continent != "Africa",]
377   -
  402 + #Graph
378 403 graph1 <- ggplot2::qplot(main = "Cyberattacks evolution",
379 404 x = attacks.evol$Year,
380 405 y = attacks.evol$Attacks,
... ... @@ -384,26 +409,34 @@ GetContinentAttacksEvolution &lt;- function(Attacks){
384 409 data = attacks.evol,
385 410 geom = "point",
386 411 color = Continent) +
387   - geom_line() +
388   - theme(plot.title = element_text(hjust = 0.5))
  412 + geom_line() +
  413 + theme(plot.title = element_text(hjust = 0.5))
389 414  
390   - graph1
  415 + #Return graph and data to represent 1b1
  416 + list(graph1, attacks.evol)
391 417  
392 418 }
393 419  
394   -GetContinentCertsEvolution <- function(Cert_PerCountry){
395   -
396   - certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>% group_by(Continent, Year)
397   -
398   - certs.evol <- summarise(certs.evol,
399   - #Continent = Continent,
400   - #Year = Year,
401   - Certs = sum(Certs))
  420 +#' Return graph to show the evolution of certifications in a continent list
  421 +#'
  422 +#' @param Cert_PerCountry data.frame with procesed source data
  423 +#' @param ContinentList List with continents to show
  424 +#'
  425 +#' @return list(graph, data.frame with ContinentList certifications data by year)
  426 +#' @export
  427 +GetContinentCertsEvolution <- function(Cert_PerCountry,
  428 + ContinentList){
  429 + #Collapsing year columns to 1 column with year value
  430 + certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>%
  431 + group_by(Continent, Year) #Grouping by continent and year
  432 + #Sum of certifications for echa continent and year
  433 + certs.evol <- summarise(certs.evol, Certs = sum(Certs))
  434 + #Removing the X from year values
402 435 certs.evol$Year <- substr(certs.evol$Year,2,5)
  436 + #Filtering for the specified continents
  437 + certs.evol <- certs.evol[certs.evol$Continent %in% ContinentList,]
403 438  
404   - certs.evol <- certs.evol[certs.evol$Continent != "Oceania",]
405   - certs.evol <- certs.evol[certs.evol$Continent != "Africa",]
406   -
  439 + #graph
407 440 graph1 <- ggplot2::qplot(main = "ISO 27001 evolution",
408 441 x = certs.evol$Year,
409 442 y = certs.evol$Certs,
... ... @@ -413,268 +446,261 @@ GetContinentCertsEvolution &lt;- function(Cert_PerCountry){
413 446 data = certs.evol,
414 447 geom = "point",
415 448 color = Continent) +
416   - geom_line() +
417   - theme(plot.title = element_text(hjust = 0.5))
  449 + geom_line() +
  450 + theme(plot.title = element_text(hjust = 0.5))
418 451  
419   - graph1
  452 + #Return the graph and data to represent continents 1b1
  453 + list(graph1, certs.evol)
420 454  
421 455 }
  456 +#' Return graph to show the evolution of attacks in a single continent and his smooth
  457 +#'
  458 +#' @param Attacks data.frame with procesed source data
  459 +#' @param Continent Continent to represent
  460 +#' @param smooth.x Horizontal position to smooth slope label
  461 +#' @param smooth.y Vertical position to smooth slope label
  462 +#' @param smooth.label.hjust Horizontal just to smooth slope label
  463 +#'
  464 +#' @return list(graph, slope)
  465 +#' @export
  466 +GetContinentAttacksSigleEvolution <- function(Attacks,
  467 + Continent,
  468 + smooth.x = 0,
  469 + smooth.y = 0,
  470 + smooth.label.hjust = 0){
422 471  
423   -GetContinentAttacksTopEvolution <- function(Attacks){
424   - attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year, Continent)
425   - attacks.evol <- as.data.frame(table(attacks.evol$Year, attacks.evol$Continent))
426   - attacks.evol <- setNames(attacks.evol, c("Year", "Continent", "Count"))
427   - attacks.evol <- attacks.evol[attacks.evol$Continent != "",]
428   -
429   - attacks.evol <- spread(attacks.evol, "Continent", "Count")
  472 + #Filtering for the continent specified
  473 + attacks.evol <- Attacks[Attacks$Continent == Continent,]
430 474  
  475 + #Calc slope
  476 + slope1 <- lm(formula = Attacks ~ as.numeric(Year), data = attacks.evol)$coef[[2]]
  477 + #Graph
431 478 graph1 <- ggplot(data = attacks.evol,
432   - aes(x = Year, y = Americas, group = 1)) +
433   -
434   - geom_line() +
435   - geom_point() +
436   -
437   - theme(plot.title = element_text(hjust = 0.5)) +
438   - ggtitle("Americas") +
439   - xlab("Years") + ylab("Attacks")+
440   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
441   -
442   - graph2 <- ggplot(data = attacks.evol,
443   - aes(x = Year, y = Asia, group = 1)) +
444   -
445   - geom_line() +
446   - geom_point() +
447   -
448   - theme(plot.title = element_text(hjust = 0.5)) +
449   - ggtitle("Asia") +
450   - xlab("Years") + ylab("Attacks")+
451   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
452   -
453   - graph3 <- ggplot(data = attacks.evol,
454   - aes(x = Year, y = Europe, group = 1)) +
455   -
456   - geom_line() +
457   - geom_point() +
458   -
459   - theme(plot.title = element_text(hjust = 0.5)) +
460   - ggtitle("Europe") +
461   - xlab("Years") + ylab("Attacks")+
462   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
463   -
464   -
465   - list(graph1, graph2, graph3)
  479 + aes(x = Year, y = Attacks, group = 1)) +
  480 + geom_line() +
  481 + geom_point() +
  482 + theme(plot.title = element_text(hjust = 0.5)) +
  483 + ggtitle(Continent) +
  484 + xlab("Years") + ylab("Attacks")+
  485 + geom_smooth(method = "lm",
  486 + se = FALSE) +
  487 + geom_label(aes(x = smooth.x, y = smooth.y,
  488 + label = paste("Slope =", signif(slope1, 5))),
  489 + color = "blue",
  490 + hjust = smooth.label.hjust)
466 491  
  492 + #Returning list(graph, slope)
  493 + list(graph1, slope1)
467 494 }
  495 +#' Return graph to show the evolution of certifications in a single continent and his smooth
  496 +#'
  497 +#' @param Certs_byContinent data.frame with procesed source data
  498 +#' @param Continent Continent to represent
  499 +#' @param smooth.x Horizontal position to smooth slope label
  500 +#' @param smooth.y Vertical position to smooth slope label
  501 +#' @param smooth.label.hjust Horizontal just to smooth slope label
  502 +#'
  503 +#' @return list(graph, slope)
  504 +#' @export
  505 +GetContinentCertsSingleEvolution <- function(Certs_byContinent,
  506 + Continent,
  507 + smooth.x = 0,
  508 + smooth.y = 0,
  509 + smooth.label.hjust = 0){
468 510  
469   -GetContinentCertsTopEvolution <- function(Attacks){
470   -
471   - certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>% group_by(Continent, Year)
472   -
473   - certs.evol <- summarise(certs.evol,
474   - #Continent = Continent,
475   - #Year = Year,
476   - Certs = sum(Certs))
477   - certs.evol$Year <- substr(certs.evol$Year,2,5)
478   -
479   - certs.evol <- spread(certs.evol, "Continent", "Certs")
  511 + #Filtering for the continent specified
  512 + certs.evol <- Certs_byContinent[Certs_byContinent$Continent == Continent,]
480 513  
  514 + #Calc slope
  515 + slope1 <- lm(formula = Certs ~ as.numeric(Year), data = certs.evol)$coef[[2]]
  516 + #Graph
481 517 graph1 <- ggplot(data = certs.evol,
482   - aes(x = Year, y = Americas, group = 1)) +
483   -
484   - geom_line() +
485   - geom_point() +
486   -
487   - theme(plot.title = element_text(hjust = 0.5)) +
488   - ggtitle("Americas") +
489   - xlab("Years") + ylab("Certifications")+
490   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
491   -
492   - graph2 <- ggplot(data = certs.evol,
493   - aes(x = Year, y = Asia, group = 1)) +
494   -
495   - geom_line() +
496   - geom_point() +
497   -
498   - theme(plot.title = element_text(hjust = 0.5)) +
499   - ggtitle("Asia") +
500   - xlab("Years") + ylab("Certifications")+
501   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
502   -
503   - graph3 <- ggplot(data = certs.evol,
504   - aes(x = Year, y = Europe, group = 1)) +
505   -
506   - geom_line() +
507   - geom_point() +
508   -
509   - theme(plot.title = element_text(hjust = 0.5)) +
510   - ggtitle("Europe") +
511   - xlab("Years") + ylab("Certifications")+
512   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
513   -
514   -
515   - list(graph1, graph2, graph3)
  518 + aes(x = Year, y = Certs, group = 1)) +
  519 + geom_line() +
  520 + geom_point() +
  521 + theme(plot.title = element_text(hjust = 0.5)) +
  522 + ggtitle(Continent) +
  523 + xlab("Years") + ylab("Certifications")+
  524 + geom_smooth(method = "lm",
  525 + se = FALSE) +
  526 + geom_label(aes(x = smooth.x, y = smooth.y,
  527 + label = paste("Slope =", signif(slope1, 5))),
  528 + color = "blue",
  529 + hjust = smooth.label.hjust)
516 530  
  531 + #Returning list(graph, slope)
  532 + list(graph1, slope1)
517 533 }
518 534  
519   -GetCountriesCol <- function(Attacks, Cert_PerCountry){
520   -
521   - certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>% group_by(Continent, country_short)
522   -
523   - certs.evol <- summarise(certs.evol,
524   - Certs = sum(Certs))
525   -
526   - graph1 <- ggplot2::ggplot(aes(x = reorder(country_short, Certs),
527   - y = Certs),
528   - data = certs.evol[certs.evol$Certs > (sum(certs.evol$Certs) * 0.02),]) +
529   - geom_col(aes(fill = Continent)) +
530   - theme(plot.title = element_text(hjust = 0.5)) +
531   - ggtitle("ISO 27001") +
532   - xlab("Country") + ylab("Certifications")
  535 +#' Return graph to show the attacks by country
  536 +#'
  537 +#' @param Attacks data.frame with procesed source data of attacks
  538 +#' @param Attacks.label.vjust Vector of vertical just to each portion label
  539 +#' @param Attacks.label.hjust Vector of horizontal just to each portion label
  540 +#' @param Cert_PerCountry data.frame with procesed source certifications
  541 +#' @param Certs.label.vjust Vector of vertical just to each portion label
  542 +#' @param Certs.label.hjust Vector of horizontal just to each portion label
  543 +#'
  544 +#' @return list(AttacksGraph, CertsGraph, CountryListToStudy)
  545 +GetCountriesCol <- function(Attacks,
  546 + Attacks.label.vjust = 0,
  547 + Attacks.label.hjust = 0,
  548 + Cert_PerCountry,
  549 + Certs.label.vjust = 0,
  550 + Certs.label.hjust = 0){
  551 +
  552 + #Group attacks by continent and country
  553 + attacks.col <- group_by(Attacks, Continent, Country)
  554 + #Count attacks for each country
  555 + attacks.col <- as.data.frame(table(attacks.col$Continent, attacks.col$Country))
  556 + attacks.col <- setNames(attacks.col, c("Continent", "Country", "Count"))
  557 + #Remove rows without Continent/Country specified
  558 + attacks.col <- attacks.col[attacks.col$Country != "",]
  559 + attacks.col <- attacks.col[attacks.col$Country != "AU",] #Abnormal behaivour, corrupt??
  560 + attacks.col <- attacks.col[attacks.col$Continent != "",]
  561 + #Only the countries with more than 2% of total attacks
  562 + attacks.col <- attacks.col[attacks.col$Count > (sum(attacks.col$Count) * 0.015), ]
  563 + #Sort by attacks
  564 + attacks.col <- arrange(attacks.col, desc(Count))
533 565  
534   - attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year, Continent, Country)
535   - attacks.evol <- as.data.frame(table(attacks.evol$Year, attacks.evol$Continent, attacks.evol$Country))
536   - attacks.evol <- setNames(attacks.evol, c("Year", "Continent", "Country", "Count"))
537   - attacks.evol <- attacks.evol[attacks.evol$Continent != "",]
538   - attacks.evol <- attacks.evol[attacks.evol$Country != "",]
539   - attacks.evol <- attacks.evol[attacks.evol$Country != "AU",]
  566 + #Attacks graph
  567 + graph1 <- ggplot2::ggplot(aes(x = reorder(Country, Count),
  568 + y = Count),
  569 + data = attacks.col) +
  570 + geom_col(aes(fill = Continent)) +
  571 + geom_text(aes(label = attacks.col$Count),
  572 + vjust = Attacks.label.vjust,
  573 + hjust = Attacks.label.hjust,
  574 + size = 3) +
  575 + theme(plot.title = element_text(hjust = 0.5)) +
  576 + ggtitle("Attacks") +
  577 + xlab("Country") + ylab("Attacks")
540 578  
541   - attacks.evol <- attacks.evol[attacks.evol$Count > (sum((attacks.evol[attacks.evol$Country != "US",])$Count) * 0.01),]
542   - attacks.evol <- arrange(attacks.evol, Count)
  579 + #Grouping certifications by continent
  580 + certs.col <- group_by(Cert_PerCountry, Continent, country_short)
  581 + #Counting certificates for each continent
  582 + certs.col <- dplyr::summarise(certs.col, Count = sum(X2011 + X2012 + X2013 + X2014 + X2015))
  583 + #Remove rows without Continent specified
  584 + certs.col <- certs.col[certs.col$Continent != "",]
  585 + certs.col <- certs.col[certs.col$country_short != "",]
  586 + #Only the countries with more than 2% of total certs
  587 + certs.col <- certs.col[certs.col$Count > (sum(certs.col$Count) * 0.02), ]
  588 + #Sort by certifications
  589 + certs.col <- arrange(certs.col, desc(Count))
543 590  
544 591  
545   - graph2 <- ggplot2::ggplot(aes(x = reorder(Country, Count),
  592 + #Certifications graph
  593 + graph2 <- ggplot2::ggplot(aes(x = reorder(country_short, Count),
546 594 y = Count),
547   - data = attacks.evol) +
548   - geom_col(aes(fill = Continent)) +
549   - theme(plot.title = element_text(hjust = 0.5)) +
550   - ggtitle("Attacks") +
551   - xlab("Country") + ylab("Attacks")
  595 + data = certs.col) +
  596 + geom_col(aes(fill = Continent)) +
  597 + geom_text(aes(label = certs.col$Count),
  598 + vjust = Certs.label.vjust,
  599 + hjust = Certs.label.hjust,
  600 + size = 3) +
  601 + theme(plot.title = element_text(hjust = 0.5)) +
  602 + ggtitle("ISO 27001") +
  603 + xlab("Country") + ylab("Certifications")
552 604  
553   - list(graph1, graph2)
  605 + #Return graphs and union of countries to study
  606 + list(graph1, graph2,
  607 + union(unique(head(attacks.col$Country, 3)),
  608 + unique(head(certs.col$country_short, 3))))
554 609  
555 610 }
556 611  
557   -GetCountriesAttacksTopEvolution <- function(Attacks){
558   - attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year, Country)
  612 +#' Return graph to show the evolution of attacks in a single continent and his smooth
  613 +#'
  614 +#' @param Attacks data.frame with procesed source data
  615 +#' @param Country Country to represent
  616 +#' @param smooth.x Horizontal position to smooth slope label
  617 +#' @param smooth.y Vertical position to smooth slope label
  618 +#' @param smooth.label.hjust Horizontal just to smooth slope label
  619 +#'
  620 +#' @return list(graph, slope)
  621 +#' @export
  622 +GetCountriesAttacksSingleEvolution <- function(Attacks,
  623 + Country,
  624 + smooth.x = 0,
  625 + smooth.y = 0,
  626 + smooth.label.hjust = 0){
  627 + #Extract year from date
  628 + attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>%
  629 + group_by(Year, Country) #Group by country
  630 + #Count attacks for each year and country
559 631 attacks.evol <- as.data.frame(table(attacks.evol$Year, attacks.evol$Country))
560 632 attacks.evol <- setNames(attacks.evol, c("Year", "Country", "Count"))
561   - attacks.evol <- attacks.evol[attacks.evol$Country != "",]
562   -
563   - attacks.evol <- spread(attacks.evol, "Country", "Count")
  633 + #Filter by the specified country
  634 + attacks.evol <- attacks.evol[attacks.evol$Country == Country,]
564 635  
565   - slope1 <- lm(formula = US ~ as.numeric(Year), data = attacks.evol)
  636 + #Calc slope
  637 + slope1 <- lm(formula = Count ~ as.numeric(Year), data = attacks.evol)$coef[[2]]
  638 + #Graph
566 639 graph1 <- ggplot(data = attacks.evol,
567   - aes(x = Year, y = US, group = 1)) +
568   -
569   - geom_line() +
570   - geom_point() +
571   -
572   - theme(plot.title = element_text(hjust = 0.5)) +
573   - ggtitle("US") +
574   - xlab("Years") + ylab("Attacks")+
575   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..)) +
576   - geom_label(aes(x = "2014", y = 700, label = signif(slope1$coef[[2]])), color = "blue")
577   -
578   - graph2 <- ggplot(data = attacks.evol,
579   - aes(x = Year, y = GB, group = 1)) +
580   -
581   - geom_line() +
582   - geom_point() +
583   -
584   - theme(plot.title = element_text(hjust = 0.5)) +
585   - ggtitle("GB") +
586   - xlab("Years") + ylab("Attacks")+
587   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
588   -
589   - graph3 <- ggplot(data = attacks.evol,
590   - aes(x = Year, y = IN, group = 1)) +
591   -
592   - geom_line() +
593   - geom_point() +
594   -
595   - theme(plot.title = element_text(hjust = 0.5)) +
596   - ggtitle("IN") +
597   - xlab("Years") + ylab("Attacks")+
598   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
599   -
600   - graph4 <- ggplot(data = attacks.evol,
601   - aes(x = Year, y = JP, group = 1)) +
602   -
603   - geom_line() +
604   - geom_point() +
605   -
606   - theme(plot.title = element_text(hjust = 0.5)) +
607   - ggtitle("JP") +
608   - xlab("Years") + ylab("Attacks")+
609   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
610   -
  640 + aes(x = Year, y = Count, group = 1)) +
  641 + geom_line() +
  642 + geom_point() +
  643 + theme(plot.title = element_text(hjust = 0.5)) +
  644 + ggtitle(Country) +
  645 + xlab("Years") + ylab("Attacks")+
  646 + geom_smooth(method = "lm",
  647 + se = FALSE) +
  648 + geom_label(aes(x = smooth.x, y = smooth.y,
  649 + label = paste("Slope =", signif(slope1, 5))),
  650 + color = "blue",
  651 + hjust = smooth.label.hjust)
611 652  
612   - list(graph1, graph2, graph3, graph4)
  653 + #Return list(graph, slope)
  654 + list(graph1, slope1)
613 655  
614 656 }
615 657  
616   -GetCountriesCertsTopEvolution <- function(Attacks){
617   -
618   - certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>% group_by(country_short, Year)
619   -
620   - certs.evol <- rbind(certs.evol[certs.evol$country_short == "US",],
621   - certs.evol[certs.evol$country_short == "GB",],
622   - certs.evol[certs.evol$country_short == "IN",],
623   - certs.evol[certs.evol$country_short == "JP",])
624   -
  658 +#' Return graph to show the evolution of certifications in a single continent and his smooth
  659 +#'
  660 +#' @param Attacks data.frame with procesed source data
  661 +#' @param Country Country to represent
  662 +#' @param smooth.x Horizontal position to smooth slope label
  663 +#' @param smooth.y Vertical position to smooth slope label
  664 +#' @param smooth.label.hjust Horizontal just to smooth slope label
  665 +#'
  666 +#' @return list(graph, slope)
  667 +#' @export
  668 +GetCountriesCertsSingleEvolution <- function(Cert_PerCountry,
  669 + Country,
  670 + smooth.x = 0,
  671 + smooth.y = 0,
  672 + smooth.label.hjust = 0){
  673 +
  674 + #Collapsing year columns to only one with the year value
  675 + certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>%
  676 + group_by(country_short, Year) #Group by country and year
  677 + #sum certificates for each country and year
625 678 certs.evol <- summarise(certs.evol,
626 679 Certs = sum(Certs))
  680 + #Removing the X of the year values
627 681 certs.evol$Year <- substr(certs.evol$Year,2,5)
  682 + #Filter by the specified Country
  683 + certs.evol <- certs.evol[certs.evol$country_short == Country,]
628 684  
629   - certs.evol <- spread(certs.evol, "country_short", "Certs")
630   -
  685 + #Calc slope
  686 + slope1 <- lm(formula = Certs ~ as.numeric(Year), data = certs.evol)$coef[[2]]
  687 + #Graph
631 688 graph1 <- ggplot(data = certs.evol,
632   - aes(x = Year, y = US, group = 1)) +
633   -
  689 + aes(x = Year, y = Certs, group = 1)) +
634 690 geom_line() +
635 691 geom_point() +
636   -
637 692 theme(plot.title = element_text(hjust = 0.5)) +
638   - ggtitle("US") +
  693 + ggtitle(Country) +
639 694 xlab("Years") + ylab("Certifications")+
640   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
641   -
642   - graph2 <- ggplot(data = certs.evol,
643   - aes(x = Year, y = GB, group = 1)) +
644   -
645   - geom_line() +
646   - geom_point() +
647   -
648   - theme(plot.title = element_text(hjust = 0.5)) +
649   - ggtitle("GB") +
650   - xlab("Years") + ylab("Certifications")+
651   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
652   -
653   - graph3 <- ggplot(data = certs.evol,
654   - aes(x = Year, y = IN, group = 1)) +
655   -
656   - geom_line() +
657   - geom_point() +
658   -
659   - theme(plot.title = element_text(hjust = 0.5)) +
660   - ggtitle("IN") +
661   - xlab("Years") + ylab("Certifications")+
662   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
663   -
664   -
665   - graph4 <- ggplot(data = certs.evol,
666   - aes(x = Year, y = JP, group = 1)) +
667   -
668   - geom_line() +
669   - geom_point() +
670   -
671   - theme(plot.title = element_text(hjust = 0.5)) +
672   - ggtitle("JP") +
673   - xlab("Years") + ylab("Certifications")+
674   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
675   -
676   -
677   - list(graph1, graph2, graph3, graph4)
  695 + geom_smooth(method = "lm",
  696 + se = FALSE) +
  697 + geom_label(aes(x = smooth.x, y = smooth.y,
  698 + label = paste("Slope =", signif(slope1, 5))),
  699 + color = "blue",
  700 + hjust = smooth.label.hjust)
  701 +
  702 + #Return graph and slope
  703 + list(graph1, slope1)
678 704  
679 705 }
680 706  
... ... @@ -683,300 +709,134 @@ GetCountriesCertsTopEvolution &lt;- function(Attacks){
683 709 #-------------------------gelocal/type---------------------------
684 710 #----------------------------------------------------------------
685 711  
686   -GetContinentAttackPie <- function (Attacks){
  712 +#' Return graph representing % of attacks type by continent
  713 +#'
  714 +#' @param Attacks data.frame with procesed source data
  715 +#' @param Country Country to represent on the left side
  716 +#' @param Country2 Country to represent on the right side
  717 +#'
  718 +#' @return Graph
  719 +#' @export
  720 +GetContinentAttackCol <- function (Attacks,
  721 + Country,
  722 + Country2){
687 723  
  724 + #Grouping attacks by attack type and country
688 725 attack.pie <- group_by(Attacks, Attack.standar, Country)
  726 + #Counting attacks for each attack type and country
689 727 attack.pie <- as.data.frame(table(attack.pie$Country, attack.pie$Attack.standar))
690 728 attack.pie <- setNames(attack.pie, c("Country", "Attack", "Count"))
691   -
  729 + #Remove rows withouth a country specified
692 730 attack.pie <- attack.pie[attack.pie$Country != "",]
  731 + #Remove rows withouth attack type specified
693 732 attack.pie <- attack.pie[attack.pie$Attack != "",]
694   -
  733 + #grouping non 'important' attack types in 'Otros'
695 734 attack.pie$Attack <- as.character(attack.pie$Attack)
696 735 attack.pie[attack.pie$Attack != "DDoS" &
697 736 attack.pie$Attack != "Defacement" &
698 737 attack.pie$Attack != "Injection",]$Attack <- "Otros"
699 738 attack.pie$Attack <- as.factor(attack.pie$Attack)
700   -
  739 + #Grouping atatcks by attacky type and country
701 740 attack.pie <- group_by(attack.pie, Attack, Country)
  741 + #sum attacks for each attack type and country
702 742 attack.pie <- summarise(attack.pie, Count = sum(Count))
703 743  
704   - attack.pie.US <- attack.pie[attack.pie$Country == "US",]
705   - attack.pie.JP <- attack.pie[attack.pie$Country == "JP",]
  744 + #Filtering by the desired countries
  745 + attack.pie.C1 <- attack.pie[attack.pie$Country == Country,]
  746 + attack.pie.C2 <- attack.pie[attack.pie$Country == Country2,]
706 747  
  748 + #Calc % of each attack type
  749 + attack.pie.C1$perc <- round(100 * attack.pie.C1$Count / sum(attack.pie.C1$Count), 2)
  750 + attack.pie.C2$perc <- round(100 * attack.pie.C2$Count / sum(attack.pie.C2$Count), 2)
  751 +
  752 + #Calc max % to draw gray background
  753 + attack.pie.max <- data.frame(Attack = attack.pie.C1$Attack)
  754 + percs <- c()
  755 + for (AT in attack.pie.max$Attack){
  756 + percs <- c(percs, max(attack.pie.C1[attack.pie.C1$Attack == AT,]$perc,
  757 + attack.pie.C2[attack.pie.C2$Attack == AT,]$perc))
  758 + }
  759 + attack.pie.max$perc <- percs
  760 +
  761 + graph1 <- ggplot() +
  762 + geom_col(aes(x=Attack,
  763 + y=perc),
  764 + width = 1,
  765 + data = attack.pie.max,
  766 + color = "Black",
  767 + fill = "Grey") +
  768 + geom_col(aes(x=1.25:4.25,
  769 + y=perc,
  770 + fill = Country),
  771 + width = 0.4,
  772 + data = attack.pie.C1) +
  773 + geom_text(aes(x=1.25:4.25,
  774 + y=perc + 1,
  775 + label = paste(perc, "%")),
  776 + data = attack.pie.C1) +
  777 + geom_col(aes(x=0.75:3.75,
  778 + y=perc,
  779 + fill = Country2),
  780 + width = 0.4,
  781 + data = attack.pie.C2) +
  782 + geom_text(aes(x=0.75:3.75,
  783 + y=perc + 1,
  784 + label = paste(perc, "%")),
  785 + data = attack.pie.C2) +
  786 + scale_x_discrete("Attack", attack.pie.C1$Attack) +
  787 + ylab("% of total attacks") +
  788 + theme(plot.title = element_text(hjust = 0.5)) +
  789 + ggtitle(paste(Country, Country2, sep = " - "))
707 790  
708   - graph1 <- ggplot(data=attack.pie.US,
709   - aes(x=factor(1),
710   - y=Count,
711   - fill=Attack)) +
712   - geom_col(width = 1, color='black') +
713   - geom_label(aes(label=paste(round(100 * attack.pie.US$Count / sum(attack.pie.US$Count), 2), "%")),
714   - vjust=c(0),
715   - hjust=c(0)) +
716   - coord_polar(theta="y") +
717   - scale_x_discrete(labels = c("")) +
718   - scale_y_discrete(labels = c("")) +
719   - theme(plot.title = element_text(hjust = 0.5),
720   - axis.title.x=element_blank(),
721   - axis.title.y=element_blank()) +
722   - ggtitle("US")
723   -
724   - graph2 <- ggplot(data=attack.pie.JP,
725   - aes(x=factor(1),
726   - y=Count,
727   - fill=Attack)) +
728   - geom_col(width = 1, color='black') +
729   - geom_label(aes(label=paste(round(100 * attack.pie.JP$Count / sum(attack.pie.JP$Count), 2), "%")),
730   - vjust=c(0),
731   - hjust=c(0)) +
732   - coord_polar(theta="y") +
733   - scale_x_discrete(labels = c("")) +
734   - scale_y_discrete(labels = c("")) +
735   - theme(plot.title = element_text(hjust = 0.5),
736   - axis.title.x=element_blank(),
737   - axis.title.y=element_blank()) +
738   - ggtitle("JP")
739   -
740   - list(graph1, graph2)
  791 + graph1
741 792 }
742 793  
743   -GetContinentAttackEvolution <- function(Attacks){
744 794  
  795 +#' Return graph representing the evolution of attack types in a country
  796 +#'
  797 +#' @param Attacks data.frame with procesed source data
  798 +#' @param Country Country to represent
  799 +#'
  800 +#' @return list(Graph, data to represent attacktypes of the country 1b1)
  801 +GetContinentAttackEvolution <- function(Attacks, Country){
  802 +
  803 + #Extract Year from date
745 804 attack.evol <-mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>%
746   - group_by(Attack.standar, Country, Year)
  805 + group_by(Attack.standar, Country, Year) #Group by attack type, country and year
  806 + #Counting the attacks for each attack type, country and year
747 807 attack.evol <- as.data.frame(table(attack.evol$Country, attack.evol$Attack.standar, attack.evol$Year))
748 808 attack.evol <- setNames(attack.evol, c("Country", "Attack", "Year", "Count"))
749   -
  809 + #Removing rows without country or attack type
750 810 attack.evol <- attack.evol[attack.evol$Country != "",]
751 811 attack.evol <- attack.evol[attack.evol$Attack != "",]
752   -
  812 + #grouping non 'important' attack types in 'Otros'
753 813 attack.evol$Attack <- as.character(attack.evol$Attack)
754 814 attack.evol[attack.evol$Attack != "DDoS" &
755 815 attack.evol$Attack != "Defacement" &
756 816 attack.evol$Attack != "Injection",]$Attack <- "Otros"
757 817 attack.evol$Attack <- as.factor(attack.evol$Attack)
758   -
  818 + #Counting attacks of new tyoe
759 819 attack.evol <- group_by(attack.evol, Attack, Country, Year)
760 820 attack.evol <- summarise(attack.evol, Count = sum(Count))
  821 + #Filtering by specified country
  822 + attack.evol <- attack.evol[attack.evol$Country == Country,]
761 823  
762   - attack.evol.US <- attack.evol[attack.evol$Country == "US",]
763   - attack.evol.JP <- attack.evol[attack.evol$Country == "JP",]
764   -
765   - attack.evol.US.Otros <- attack.evol.US[attack.evol.US$Attack == "Otros",]
766   - attack.evol.JP.Otros <- attack.evol.JP[attack.evol.JP$Attack == "Otros",]
767   -
768   -
769   - graph1 <- ggplot2::qplot(main = "US",
770   - x = attack.evol.US$Year,
771   - y = attack.evol.US$Count,
772   - group = Attack,
773   - xlab = "Years",
774   - ylab = "Certifications",
775   - data = attack.evol.US,
776   - geom = "point",
777   - color = Attack) +
778   - geom_line() +
779   - theme(plot.title = element_text(hjust = 0.5))
780   -
781   - graph2 <- ggplot2::qplot(main = "JP",
782   - x = attack.evol.JP$Year,
783   - y = attack.evol.JP$Count,
784   - group = Attack,
  824 + #Graph
  825 + graph1 <- ggplot2::qplot(main = Country,
  826 + x = attack.evol$Year,
  827 + y = attack.evol$Count,
  828 + group = attack.evol$Attack,
785 829 xlab = "Years",
786   - ylab = "Certifications",
787   - data = attack.evol.JP,
  830 + ylab = "Attacks",
  831 + data = attack.evol,
788 832 geom = "point",
789 833 color = Attack) +
790 834 geom_line() +
791 835 theme(plot.title = element_text(hjust = 0.5))
792 836  
793   - graph3 <- ggplot2::qplot(main = "US",
794   - x = attack.evol.US.Otros$Year,
795   - y = attack.evol.US.Otros$Count,
796   - group = 1,
797   - xlab = "Years",
798   - ylab = "Certifications",
799   - data = attack.evol.US.Otros,
800   - geom = "point") +
801   - geom_line() +
802   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..)) +
803   - theme(plot.title = element_text(hjust = 0.5))
804   -
805   - graph4 <- ggplot2::qplot(main = "JP",
806   - x = attack.evol.JP.Otros$Year,
807   - y = attack.evol.JP.Otros$Count,
808   - group = 1,
809   - xlab = "Years",
810   - ylab = "Certifications",
811   - data = attack.evol.JP.Otros,
812   - geom = "point") +
813   - geom_line() +
814   - stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..)) +
815   - theme(plot.title = element_text(hjust = 0.5))
816   -
817   - list(graph1, graph2, graph3, graph4)
  837 + #list with graph and processed data to represent 1b1
  838 + list(graph1, attack.evol)
818 839  
819 840 }
820 841  
821   -#----------------------------------------------------------------
822   -#----------------------------------------------------------------
823   -#----------------------------------------------------------------
824   -
825 842  
826   -GetBaseCertsGraph <- function(Cert_PerCountry, year){
827   - graph1 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2012)",
828   - x = reorder(country_short,X2012),
829   - y = X2012,
830   - xlab = "Country",
831   - ylab = "Number of certifications",
832   - data = Cert_PerCountry[Cert_PerCountry$X2012 > mean(Cert_PerCountry$X2012),],
833   - geom = "col",
834   - fill = Continent)
835   - graph1
836   -}
837   -
838   -
839   -
840   -
841   -#' Return every graph used in the report file
842   -#'
843   -#' @param Cert_PerCountry data.frame with the processed data of ISO 27001 certifications
844   -#' @param Attacks data.frame with the processed data of cyberattacks
845   -#'
846   -#' @return data.frame
847   -#' @export
848   -GetReportGraphs <- function(Cert_PerCountry,Attacks) {
849   - #2012
850   - graph1 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2012)",
851   - x = reorder(country_short,X2012),
852   - y = X2012,
853   - xlab = "Country",
854   - ylab = "Number of certifications",
855   - data = Cert_PerCountry[Cert_PerCountry$X2012 > mean(Cert_PerCountry$X2012),],
856   - geom = "col",
857   - fill = Continent)
858   -
859   - attacks2k12 <- Attacks[Attacks$Date < "2013-01-01" & Attacks$Date >= "2012-01-01",]
860   - frameAttacks2k12 <- as.data.frame(table(attacks2k12$Country))
861   - colnames(frameAttacks2k12) <- c("Country","Attacks")
862   - graph2 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2012)",
863   - x = reorder(Country,Attacks),
864   - y = Attacks,
865   - xlab = "Country",
866   - ylab = "Number of attacks",
867   - data = frameAttacks2k12[frameAttacks2k12$Attacks > mean(frameAttacks2k12$Attacks),],
868   - geom = "col",
869   - fill = Continent)
870   -
871   - Attacks2012ByMonth <- mutate(attacks2k12, month = format(attacks2k12$Date, "%m")) %>% group_by(month)
872   - Attack2012FreqByMonth <- as.data.frame(table(Attacks2012ByMonth$month))
873   - colnames(Attack2012FreqByMonth) <- c("Month", "Attacks")
874   - graph3 <- ggplot2::qplot(x = as.numeric(Month),
875   - y = Attacks,
876   - main = "Global cyberattack progression by month (2012)",
877   - data = Attack2012FreqByMonth,
878   - geom = c("point", "smooth"),
879   - xlim = c(1,12),
880   - xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
881   -
882   - #2013
883   - graph4 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2013)",
884   - x = reorder(country_short,X2013),
885   - y = X2013,
886   - xlab = "Country",
887   - ylab = "Number of certifications",
888   - data = Cert_PerCountry[Cert_PerCountry$X2013 > mean(Cert_PerCountry$X2013),]
889   - , geom = "col",
890   - fill = Continent)
891   - attacks2k13 <- Attacks[Attacks$Date < "2014-01-01" & Attacks$Date >= "2013-01-01",]
892   - frameAttacks2k13 <- as.data.frame(table(attacks2k13$Country))
893   - colnames(frameAttacks2k13) <- c("Country","Attacks")
894   - graph5 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2013)",
895   - x = reorder(Country,Attacks),
896   - y = Attacks,
897   - xlab = "Country",
898   - ylab = "Number of attacks",
899   - data = frameAttacks2k13[frameAttacks2k13$Attacks > mean(frameAttacks2k13$Attacks),]
900   - , geom = "col",
901   - fill = Continent)
902   -
903   - Attacks2013ByMonth <- mutate(attacks2k13, month = format(attacks2k13$Date, "%m")) %>% group_by(month)
904   - Attack2013FreqByMonth <- as.data.frame(table(Attacks2013ByMonth$month))
905   - colnames(Attack2013FreqByMonth) <- c("Month", "Attacks")
906   - graph6 <- ggplot2::qplot(x = as.numeric(Month),
907   - y = Attacks,
908   - main = "Global cyberattack progression by month (2013)",
909   - data = Attack2013FreqByMonth,
910   - geom = c("point", "smooth"),
911   - xlim = c(1,12),
912   - xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
913   -
914   - #2014
915   - graph7 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2014)",
916   - x = reorder(country_short,X2014),
917   - y = X2014,
918   - xlab = "Country",
919   - ylab = "Number of certifications",
920   - data = Cert_PerCountry[Cert_PerCountry$X2014 > mean(Cert_PerCountry$X2014),]
921   - , geom = "col",
922   - fill = Continent)
923   - attacks2k14 <- Attacks[Attacks$Date < "2015-01-01" & Attacks$Date >= "2014-01-01",]
924   - frameAttacks2k14 <- as.data.frame(table(attacks2k14$Country))
925   - colnames(frameAttacks2k14) <- c("Country","Attacks")
926   - graph8 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2014)",
927   - x = reorder(Country,Attacks),
928   - y = Attacks,
929   - xlab = "Country",
930   - ylab = "Number of attacks",
931   - data = frameAttacks2k14[frameAttacks2k14$Attacks > mean(frameAttacks2k14$Attacks),]
932   - , geom = "col",
933   - fill = Continent)
934   -
935   - Attacks2014ByMonth <- mutate(attacks2k14, month = format(attacks2k14$Date, "%m")) %>% group_by(month)
936   - Attack2014FreqByMonth <- as.data.frame(table(Attacks2014ByMonth$month))
937   - colnames(Attack2014FreqByMonth) <- c("Month", "Attacks")
938   - graph9 <- ggplot2::qplot(x = as.numeric(Month),
939   - y = Attacks,
940   - main = "Global cyberattack progression by month (2014)",
941   - data = Attack2014FreqByMonth,
942   - geom = c("point", "smooth"),
943   - xlim = c(1,12),
944   - xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
945   -
946   - #2015
947   - graph10 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2015)",
948   - x = reorder(country_short,X2015),
949   - y = X2015,
950   - xlab = "Country",
951   - ylab = "Number of certifications",
952   - data = Cert_PerCountry[Cert_PerCountry$X2015 > mean(Cert_PerCountry$X2015),]
953   - , geom = "col",
954   - fill = Continent)
955   - attacks2k15 <- Attacks[Attacks$Date < "2016-01-01" & Attacks$Date >= "2015-01-01",]
956   - frameAttacks2k15 <- as.data.frame(table(attacks2k15$Country))
957   - colnames(frameAttacks2k15) <- c("Country","Attacks")
958   - graph11 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2015)",
959   - x = reorder(Country,Attacks),
960   - y = Attacks,
961   - xlab = "Country",
962   - ylab = "Number of attacks",
963   - data = frameAttacks2k15[frameAttacks2k15$Attacks > mean(frameAttacks2k15$Attacks),]
964   - , geom = "col",
965   - fill = Continent)
966   -
967   - Attacks2015ByMonth <- mutate(attacks2k15, month = format(attacks2k15$Date, "%m")) %>% group_by(month)
968   - Attack2015FreqByMonth <- as.data.frame(table(Attacks2015ByMonth$month))
969   - colnames(Attack2015FreqByMonth) <- c("Month", "Attacks")
970   - graph12 <- ggplot2::qplot(x = as.numeric(Month),
971   - y = Attacks,
972   - main = "Global cyberattack progression by month (2015)",
973   - data = Attack2015FreqByMonth,
974   - geom = c("point", "smooth"),
975   - xlim = c(1,12),
976   - xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
977   -
978   -
979   -
980   -
981   - list(graph1,graph2,graph3,graph4,graph5,graph6,graph7,graph8,graph9,graph10,graph11,graph12)
982   -}
... ...
ISO27001effectiveness/Report.Rmd
1 1 ---
2   -title: "Estudio sobre la efectividad de la certificación ISO 27001"
  2 +title: "Estudio sobre la efectividad de la certificación ISO 27001"
3 3 output:
4 4 html_document: default
5 5 pdf_document: default
... ... @@ -199,193 +199,279 @@ Por otro lado tenemos _DNS_, cuya tendencia también tiene una pendiente negativ
199 199  
200 200 ### Evolución geográfica
201 201  
202   -Este apartado estudiará la relación entre la certificación ISO 27001 y los ataques producidos, pero teniendo en cuenta la variable geográfica, ya que es posible que la certificación, aunque sea internacional, se implemente de una mejor o peor forma según la región. En primer lugar se generalizará por continente.
  202 +Este apartado estudiará la relación entre la certificación ISO 27001 y los ataques producidos, pero teniendo en cuenta la variable geográfica, ya que es posible que la certificación, aunque sea internacional, se implemente o funcione de una mejor o peor forma según la región. En primer lugar se generalizará por continente.
203 203  
204 204 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
205 205 ContinentPies <- ISO27001effectiveness::GetContinentPie(Attacks,
206 206 c(-2.5, 0, 2, 0, -2.5),
207 207 c(1.4, 1, 0.5, 0.5, 0),
208 208 Cert_PerCountry,
209   -c(0, -1.5, -2.5, -2.5, -2.5),
210   -c(0.5, 0.2, 0.5, 1.8, -0.5))
  209 +c(-2.5, -2.5, 0, -1.5, -2.5),
  210 +c(-0.5, 1.9, 1, 0.25, 0.6))
211 211  
212   -ContinentPies[[1]]
  212 +ContinentPies[[2]]
213 213 ```
214 214 ```{r fig.width=4.5, fig.height=3}
215   -ContinentPies[[3]]
  215 +ContinentPies[[1]]
216 216 ```
217 217  
218   -Se puede observar a simple vista que los continentes que reciben más ataques, por una cuestión lógica de superficie e intereses, son por orden América, Asia y Europa. En cambio los continentes que mas certificaciones ISO 27001 obtienen son por orden Asia, Europa y América. Tanto África como Oceania podemos descartarlos en este estudio ya que sus porcentajes no son relevantes. Observemos ahora cómo influye esto en el tiempo.
  218 +Se puede observar a simple vista que los continentes que reciben más ataques, por lo que podría ser una cuestión lógica de superficie y/o intereses, son por orden: América, Asia y Europa. En cambio, los continentes que mas certificaciones ISO 27001 obtienen son los mismos, pero en distinto orden: Asia, Europa y América.
  219 +
  220 +Tanto África como Oceania serán descartados en el resto del estudio ya que sus porcentajes no parecen relevantes.
219 221  
220 222 ```{r fig.width=4.5, fig.height=4,out.extra='style="float:left"'}
221   -ISO27001effectiveness::GetContinentCertsEvolution(Cert_PerCountry)
  223 +ContinentCertEvolution <- ISO27001effectiveness::GetContinentCertsEvolution(Cert_PerCountry, ContinentPies[[3]])
  224 +
  225 +ContinentCertEvolution[[1]]
222 226 ```
223 227 ```{r fig.width=4.5, fig.height=4}
224   -ISO27001effectiveness::GetContinentAttacksEvolution(Attacks)
  228 +ContinentAttacksEvolution <- ISO27001effectiveness::GetContinentAttacksEvolution(Attacks, ContinentPies[[3]])
  229 +
  230 +ContinentAttacksEvolution[[1]]
225 231 ```
226 232  
227   -Se puede observar que la tendencia de las certificaciones es creciente mientras que la de los ataques es decreciente, pero procederemos a comparar cada una individualmente para poder demostrarlo numéricamente y no solo aparentemente.
  233 +Se puede observar que en conjunto la pendiente de la tendencia de las certificaciones es creciente, mientras que la de los ataques es decreciente. No obstante, se procederá a comparar cada continente individualmente para poder observarlo con más precisión.
228 234  
229 235 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
230   -topAttacks <- ISO27001effectiveness::GetContinentAttacksTopEvolution(Attacks)
231   -topCerts <- ISO27001effectiveness::GetContinentCertsTopEvolution(Cert_PerCountry)
232   -topCerts[[1]]
233   -slope_Ame_Cert <- (fit[5] - fit[1]) / 4
  236 +Ame_Cert <- GetContinentCertsSingleEvolution(ContinentCertEvolution[[2]], "Americas", "2013", 1400, 0.5)
  237 +Ame_Cert[[1]]
  238 +slope_Ame_Cert <- Ame_Cert[[2]]
234 239 ```
235 240 ```{r fig.width=4.5, fig.height=3}
236   -topAttacks[[1]]
237   -slope_Ame_Att <- (fit[5] - fit[1]) / 4
  241 +Ame_Att <- GetContinentAttacksSigleEvolution(ContinentAttacksEvolution[[2]], "Americas", "2014", 850, 0)
  242 +Ame_Att[[1]]
  243 +slope_Ame_Att <- Ame_Att[[2]]
238 244 ```
239 245  
240   -La pendiente de los ataques para _América_ es `r slope_Ame_Att` mientras que la pendiente de las certificaciones es `r slope_Ame_Cert`.
  246 +Para _América_, la pendiente de la tendencia en las certificaciones es `r slope_Ame_Cert`, mientras que la pendiente de los ataques es `r slope_Ame_Att`.
241 247  
242 248 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
243   -topCerts[[2]]
244   -slope_Asi_Cert <- (fit[5] - fit[1]) / 4
  249 +Asi_Cert <- GetContinentCertsSingleEvolution(ContinentCertEvolution[[2]], "Asia", "2013", 13500, 0.5)
  250 +Asi_Cert[[1]]
  251 +slope_Asi_Cert <- Asi_Cert[[2]]
245 252 ```
246 253 ```{r fig.width=4.5, fig.height=3}
247   -topAttacks[[2]]
248   -slope_Asi_Att <- (fit[5] - fit[1]) / 4
  254 +Asi_Att <- GetContinentAttacksSigleEvolution(ContinentAttacksEvolution[[2]], "Asia", "2014", 400, 0)
  255 +Asi_Att[[1]]
  256 +slope_Asi_Att <- Asi_Att[[2]]
249 257 ```
250 258  
251   -La pendiente de los ataques para _Asia_ es `r slope_Asi_Att` mientras que la pendiente de las certificaciones es `r slope_Asi_Cert`.
  259 +Para _Asia_, la pendiente de la tendencia en las certificaciones es `r slope_Asi_Cert`, mientras que la pendiente de los ataques es `r slope_Asi_Att`.
252 260  
253 261 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
254   -topCerts[[3]]
255   -slope_Eu_Cert <- (fit[5] - fit[1]) / 4
  262 +Eu_Cert <- GetContinentCertsSingleEvolution(ContinentCertEvolution[[2]], "Europe", "2013", 9500, 0.5)
  263 +Eu_Cert[[1]]
  264 +slope_Eu_Cert <- Eu_Cert[[2]]
256 265 ```
257 266 ```{r fig.width=4.5, fig.height=3}
258   -topAttacks[[3]]
259   -slope_Eu_Att <- (fit[5] - fit[1]) / 4
  267 +Eu_Att <- GetContinentAttacksSigleEvolution(ContinentAttacksEvolution[[2]], "Europe", "2014", 375, 0)
  268 +Eu_Att[[1]]
  269 +slope_Eu_Att <- Eu_Att[[2]]
260 270 ```
261 271  
262   -La pendiente de los ataques para _Europa_ es `r slope_Eu_Att` mientras que la pendiente de las certificaciones es `r slope_Eu_Cert`.
263   -
264   -Todas las pendientes podrían representar la relación que buscamos, en la que un aumento en las certificaciones produce un descenso en los ataques.
  272 +Para _Europa_, la pendiente de la tendencia en las certificaciones es `r slope_Eu_Cert`, mientras que la pendiente de los ataques es `r slope_Eu_Att`.
265 273  
266   -El análisis puede aumentar en profundidad estableciendo superficies geográficas más pequeñas y asi obtener más precisión, observemos lo que ocurre a nivel de paises. Se mostrarán a continuación los paises que superan aproximadamente un 2% de los ataques/certificaciones totales ya que la lista total de paises es demasiado extensa.
  274 +El análisis continuará estableciendo superficies geográficas más pequeñas, aumentando así la precisión, para poder observar lo que ocurre a nivel de paises. Se mostrarán a continuación los que superan aproximádamente un 2% de las certificaciones totales y el 1,5% para los ataques (porque _Estados Unidos_ recibe la gran mayoría y si no saldría solo), ya que la lista completa es demasiado extensa. Los nombres serán representados con los dos caracteres correspondientes al estándar ISO.
267 275  
268 276 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
269   -top <- GetCountriesCol(Attacks, Cert_PerCountry)
270   -top[[1]]
  277 +CountryCol <- GetCountriesCol(Attacks,0, 0.5,
  278 + Cert_PerCountry, 0, 0.5)
  279 +CountryCol[[2]]
271 280 ```
272 281 ```{r fig.width=4.5, fig.height=3}
273   -top[[2]]
  282 +CountryCol[[1]]
274 283 ```
275 284  
276   -Como se puede observar en la parte de certificaciones destaca de largo Japón sobre los demás, que se encuentra bastante bajo en la lista de ataques. Y al reves pasa algo parecido, en los ataques destaca Estados Unidos por mucho mientras que ese mismo pais está muy bajo en certificaciones. A continuación observaremos la evolución temporal del top 3 paises en ataques recibidos y en certificaciones obtenidas, varios de ellos coinciden, tenemos por la parte de las certificaciones a _Japón_, por la parte de los ataques a _Estados Unidos_, y común a ambas _Reino Unido_ e _India_.
  285 +Como se puede observar, en la parte de certificaciones destaca de largo _Japón_ sobre los demás, que a su vez se encuentra bastante bajo en la lista de ataques. Y, de un modo totalmente contrario, en los ataques destaca _Estados Unidos_ por mucho, mientras que este mismo está muy bajo en certificaciones.
  286 +
  287 +El estudio continuará en profundidad con el top 3 de paises en número de certificaciones y en número de ataques recibidos, aunque algunos de ellos coincidan.
277 288  
278 289 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
279   -topAttacks <- ISO27001effectiveness::GetCountriesAttacksTopEvolution(Attacks)
280   -topCerts <- ISO27001effectiveness::GetCountriesCertsTopEvolution(Cert_PerCountry)
281   -topCerts[[1]]
282   -slope_US_Cert <- (fit[5] - fit[1]) / 4
  290 +US_Cert <- GetCountriesCertsSingleEvolution(Cert_PerCountry,
  291 + CountryCol[[3]][1],
  292 + "2013", 875, 0.5)
  293 +
  294 +US_Cert[[1]]
  295 +slope_US_Cert <- US_Cert[[2]]
283 296 ```
284 297 ```{r fig.width=4.5, fig.height=3}
285   -topAttacks[[1]]
286   -slope_US_Att <- (fit[5] - fit[1]) / 4
  298 +US_Att <- GetCountriesAttacksSingleEvolution(Attacks,
  299 + CountryCol[[3]][1],
  300 + "2014", 725, 0)
  301 +
  302 +US_Att[[1]]
  303 +slope_US_Att <- US_Att[[2]]
287 304 ```
288 305  
289   -La pendiente de los ataques para _Estados Unidos_ es `r slope_US_Att` mientras que la pendiente de las certificaciones es `r slope_US_Cert`.
  306 +La pendiente de la tendencia en los ataques para _`r CountryCol[[3]][1]`_ es `r slope_US_Att` mientras que la pendiente de las certificaciones es `r slope_US_Cert`.
290 307  
291 308 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
292   -topCerts[[2]]
293   -slope_GB_Cert <- (fit[5] - fit[1]) / 4
  309 +GB_Cert <- GetCountriesCertsSingleEvolution(Cert_PerCountry,
  310 + CountryCol[[3]][2],
  311 + "2013", 2400, 0.5)
  312 +
  313 +GB_Cert[[1]]
  314 +slope_GB_Cert <- GB_Cert[[2]]
294 315 ```
295 316 ```{r fig.width=4.5, fig.height=3}
296   -topAttacks[[2]]
297   -slope_GB_Att <- (fit[5] - fit[1]) / 4
  317 +GB_Att <- GetCountriesAttacksSingleEvolution(Attacks,
  318 + CountryCol[[3]][2],
  319 + "2014", 125, 0.5)
  320 +
  321 +GB_Att[[1]]
  322 +slope_GB_Att <- GB_Att[[2]]
298 323 ```
299 324  
300   -La pendiente de los ataques para _Reino Unido_ es `r slope_GB_Att` mientras que la pendiente de las certificaciones es `r slope_GB_Cert`.
  325 +La pendiente de los ataques para _`r CountryCol[[3]][2]`_ es `r slope_GB_Att` mientras que la pendiente de las certificaciones es `r slope_GB_Cert`.
301 326  
302 327 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
303   -topCerts[[3]]
304   -slope_IN_Cert <- (fit[5] - fit[1]) / 4
  328 +IN_Cert <- GetCountriesCertsSingleEvolution(Cert_PerCountry,
  329 + CountryCol[[3]][3],
  330 + "2013", 1875, -0.2)
  331 +
  332 +IN_Cert[[1]]
  333 +slope_IN_Cert <- IN_Cert[[2]]
305 334 ```
306 335 ```{r fig.width=4.5, fig.height=3}
307   -topAttacks[[3]]
308   -slope_IN_Att <- (fit[5] - fit[1]) / 4
  336 +IN_Att <- GetCountriesAttacksSingleEvolution(Attacks,
  337 + CountryCol[[3]][3],
  338 + "2014", 60, -0.3)
  339 +
  340 +IN_Att[[1]]
  341 +slope_IN_Att <- IN_Att[[2]]
309 342 ```
310 343  
311   -La pendiente de los ataques para _India_ es `r slope_IN_Att` mientras que la pendiente de las certificaciones es `r slope_IN_Cert`.
  344 +La pendiente de los ataques para _`r CountryCol[[3]][3]`_ es `r slope_IN_Att` mientras que la pendiente de las certificaciones es `r slope_IN_Cert`.
312 345  
313 346 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
314   -topCerts[[4]]
315   -slope_JP_Cert <- (fit[5] - fit[1]) / 4
  347 +JP_Cert <- GetCountriesCertsSingleEvolution(Cert_PerCountry,
  348 + CountryCol[[3]][4],
  349 + "2013", 7600, 1)
  350 +
  351 +JP_Cert[[1]]
  352 +slope_JP_Cert <- JP_Cert[[2]]
316 353 ```
317 354 ```{r fig.width=4.5, fig.height=3}
318   -topAttacks[[4]]
319   -slope_JP_Att <- (fit[5] - fit[1]) / 4
  355 +JP_Att <- GetCountriesAttacksSingleEvolution(Attacks,
  356 + CountryCol[[3]][4],
  357 + "2014", 30, -0.5)
  358 +
  359 +JP_Att[[1]]
  360 +slope_JP_Att <- JP_Att[[2]]
320 361 ```
321 362  
322   -La pendiente de los ataques para _Japón_ es `r slope_JP_Att` mientras que la pendiente de las certificaciones es `r slope_JP_Cert`.
  363 +La pendiente de los ataques para _`r CountryCol[[3]][4]`_ es `r slope_JP_Att` mientras que la pendiente de las certificaciones es `r slope_JP_Cert`.
323 364  
324 365 ### Evolución geográfica y tipo de ataque
325 366  
326   -Hasta ahora se han analizado por separado estas dos variables, pero la respuesta podria encontrarse en una combinacion de las mismas. Para ello se analizarán los tipos de ataque reportados en el pais en el que parece que la certificación es más efectiva, _Japón_, y en el que menos, _Estados Unidos_.
  367 +Hasta ahora se han analizado por separado el tipo de ataque y la localización geofráfica, pero la respuesta podría encontrarse en una combinación de las mismas. Para ello se analizarán los tipos de ataque reportados en el país con más certificaciones, _Japón_, y en el que más ataques recibe, _Estados Unidos_.
  368 +
  369 +```{r fig.width=9, fig.height=4}
  370 +ISO27001effectiveness::GetContinentAttackCol(Attacks, "US", "JP")
  371 +```
  372 +
  373 +Como se pudo apreciar en un apartado previo, la ISO 27001 parece especialmente efectiva contra las técnicas de _Defacement_, _DDoS_ e _Injection_, en los gráficos previos se puede observar que _Estados Unidos_ tiene un menor porcentaje de este tipo de ataques con respecto a _Japón_. Para valorarlo mejor se representará a continuación cómo evolucionan con el tiempo en ambos paises.
327 374  
328 375 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
329   -graphs <- ISO27001effectiveness::GetContinentAttackPie(Attacks)
330   -graphs[[1]]
  376 +US_Att_Year <- ISO27001effectiveness::GetContinentAttackEvolution(Attacks, "US")
  377 +
  378 +US_Att_Year[[1]]
331 379 ```
332 380 ```{r fig.width=4.5, fig.height=3}
333   -graphs[[2]]
  381 +JP_Att_Year <- ISO27001effectiveness::GetContinentAttackEvolution(Attacks, "JP")
  382 +JP_Att_Year[[1]]
334 383 ```
335 384  
336   -Como vimos en el apartado previo, la ISO 27001 parece especialmente efectiva contra las técnicas de _Defacement_, _DDoS_ e _Injection_, en los gráficos previos podemos observar como para _Estados Unidos_ tiene un menor porcentaje de este tipo de ataques con respecto a _Japón_. Para valorarlo mejor se representará a continuación cómo evolucionan con el tiempo.
  385 +Como se puede observar, en _Estados Unidos_ se presenta una tendencia mayor a recibir los tipos de ataques que parecen menos afectados por la ISO 27001, mientras que en _Japón_ parecen matenerse. Se representarán las tendecias para cada técnica en ambos paises.
337 386  
338 387 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
339   -graphs <- ISO27001effectiveness::GetContinentAttackEvolution(Attacks)
340   -graphs[[1]]
  388 +US_dds_Year <- GetAttackTypeSigleEvolution(US_Att_Year[[2]],
  389 + "DDoS",
  390 + "2014",
  391 + 75, 0)
  392 +US_dds_Year[[1]]
  393 +slope_US_dds <- US_dds_Year[[2]]
341 394 ```
342 395 ```{r fig.width=4.5, fig.height=3}
343   -graphs[[2]]
  396 +JP_dds_Year <- GetAttackTypeSigleEvolution(JP_Att_Year[[2]],
  397 + "DDoS",
  398 + "2014",
  399 + 4.4, 0)
  400 +JP_dds_Year[[1]]
  401 +slope_JP_dds <- JP_dds_Year[[2]]
344 402 ```
345 403  
346   -Como se puede observar, en _Estados Unidos_ se presenta una tendencia mayor a recibir los tipos de ataques que parecen menos afectados por la ISO 27001, mientras que en _Japón_ parecen matenerse. Se representarán las tendecias en ambos paises.
  404 +Para _DDoS_, la pendiente de la tendencia es menor en _Estados Unidos_(`r slope_US_dds`) que en _Japón_(`r slope_JP_dds`).
347 405  
348 406 ```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
349   -graphs[[3]]
350   -slope_US_Oth <- (fit[5] - fit[1]) / 4
  407 +US_def_Year <- GetAttackTypeSigleEvolution(US_Att_Year[[2]],
  408 + "Defacement",
  409 + "2014",
  410 + 46, 0)
  411 +US_def_Year[[1]]
  412 +slope_US_def <- US_def_Year[[2]]
351 413 ```
352 414 ```{r fig.width=4.5, fig.height=3}
353   -graphs[[4]]
354   -slope_JP_Oth <- (fit[5] - fit[1]) / 4
  415 +JP_def_Year <- GetAttackTypeSigleEvolution(JP_Att_Year[[2]],
  416 + "Defacement",
  417 + "2014",
  418 + 1.5, 0)
  419 +JP_def_Year[[1]]
  420 +slope_JP_def <- JP_def_Year[[2]]
355 421 ```
356 422  
357   -Efectivamente, la tendencia en _Estados Unidos_ tiene una pendiente mayor (`r slope_US_Oth`) que en Japón que es cercana al 0 (`r slope_JP_Oth`) lo que, aunque la gráfica sea irregular debido a la baja cantidad de casos, implica una constancia.
358   -
359   -# Viejo
360   -
361   -De los datos mostrados se pueden hacer diferentes observaciones:
362   -
363   -* De 2014 a 2015, USA pasa de tener 654 a 1247 empresas con certificación ISO 27001, sin embargo la cifra de ciberataques se mantiene constante de 383 a 386 ataques recibidos. De 2013 a 2014 por ejemplo, pasa de recibir 505 a 383 ataques pese a sólo haber pasado de 566 a 654 empresas con dicha certificación.
364   -* Japón tiene un número inusualmente alto de empresas con la ISO 27001, no obstante sufre una cantidad de ciberataques comparativa a la de Israel, que tiene muchas menos empresas con la certificación.
365   -
366   -Ambas observaciones son de especial interés ya que ponen de relieve situaciones en las que de ser efectivo reformar los sistemas para cumplir la ISO 27001, debería poder apreciarse un efecto en la cantidad de ciberataques recibidos. En el caso de los USA, sólo podría explicarse mediante alguna de las siguientes hipótesis:
  423 +Para _Defacement_, la pendiente de la tendencia es menor en _Estados Unidos_(`r slope_US_def`) que en _Japón_(`r slope_JP_def`), aunque es muy parecida.
367 424  
368   -* Entre 2013 y 2014 se produjo un número especialmente alto de ciberataques a USA
369   -* Se crean más empresas de las que logran certificarse, y además, sufren ataques antes de obtenerla
  425 +```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
  426 +US_inj_Year <- GetAttackTypeSigleEvolution(US_Att_Year[[2]],
  427 + "Injection",
  428 + "2014",
  429 + 155, 0)
  430 +US_inj_Year[[1]]
  431 +slope_US_inj <- US_inj_Year[[2]]
  432 +```
  433 +```{r fig.width=4.5, fig.height=3}
  434 +JP_inj_Year <- GetAttackTypeSigleEvolution(JP_Att_Year[[2]],
  435 + "Injection",
  436 + "2014",
  437 + 9, 0)
  438 +JP_inj_Year[[1]]
  439 +slope_JP_inj <- JP_inj_Year[[2]]
  440 +```
370 441  
371   -La primera hipótesis puede ser comprobada con los datos que disponemos. A continuación se muestra una línea temporal de ciberataques globales entre los años 2012 y 2015.
  442 +Para _Injection_, la pendiente de la tendencia es menor en _Estados Unidos_(`r slope_US_inj`) que en _Japón_(`r slope_JP_inj`).
372 443  
  444 +```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'}
  445 +US_Oth_Year <- GetAttackTypeSigleEvolution(US_Att_Year[[2]],
  446 + "Otros",
  447 + "2014",
  448 + 100, 0)
  449 +US_Oth_Year[[1]]
  450 +slope_US_Oth <- US_Oth_Year[[2]]
  451 +```
  452 +```{r fig.width=4.5, fig.height=3}
  453 +JP_Oth_Year <- GetAttackTypeSigleEvolution(JP_Att_Year[[2]],
  454 + "Otros",
  455 + 3.5,
  456 + 3.5, 0)
  457 +JP_Oth_Year[[1]]
  458 +slope_JP_Oth <- JP_Oth_Year[[2]]
  459 +```
373 460  
374   -Observamos que se produjo lo contrario, bajó el número de ciberataques.
  461 +Para _Otros_, la pendiente de la tendencia es mayor en _Estados Unidos_(`r slope_US_inj`) que en _Japón_(`r slope_JP_inj`).
375 462  
376 463 ## Conclusiones
377   -Por lo observado anteriormente, se puede concluir que:
378 464  
379   -* La cantidad de ciberataques que recibe un país no se mitiga por el número de empresas que han obtenido la certificación ISO 270001
380 465  
381   -Esto no lleva a concluir que obtener la certificación ISO 27001 no es efectiva para reducir el número de ciberataques, sino que probablemente dependa de factores externos ajenos a este estudio
382 466  
383 467 ## Trabajo futuro
384   -Como trabajo futuro querríamos poder seguir en la línea de investigación acerca de los siguientes puntos:
  468 +Como trabajo futuro, el estudio podría seguir diversas líneas de investigación, resumidas en los siguientes puntos:
385 469  
386   -* ¿Qué factores producen un aumento o recesión en la cantidad de ciberataques recibidos?
387   -* ¿Qué sectores industriales reciben más ciberataques?
388   -* ¿Cuáles de esos sectores son los que más certificaciones obtienen?
  470 +* Mejorar la fuente de datos de ciberataques. Una fuente cuya administración, recopilación y mantenimiento no dependiera de una sola persona y fuera más constante, homogenea y detallada.
  471 +* Conectar los datos sobre web sites certificados por pais que nos provee la fuente de datos de certificaciones.
  472 +* Homogenizar los nombres y conectar los datos sobre sectores industriales que nos proveen ambas fuentes.
  473 +* Investigación más a fondo de los cambios realizados sobre la norma 27001, como la 27001:2013, para relacionarlos con las irregularidades en las curvas de ataques.
  474 +* Incorporar nuevas variables que puedan afectar al estudio, como parches importantes u otras normas parecidas.
389 475  
390 476 Creemos que la investigación de estas cuestiones puede dar más robustez a las conclusiones expuestas en este estudio.
391 477  
... ...
ISO27001effectiveness/Report.html
... ... @@ -11,7 +11,7 @@
11 11  
12 12  
13 13  
14   -<title>Estudio sobre la efectividad de la certificación ISO 27001</title>
  14 +<title>Estudio sobre la efectividad de la certificación ISO 27001</title>
15 15  
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... ... @@ -115,7 +115,7 @@ $(document).ready(function () {
115 115  
116 116  
117 117  
118   -<h1 class="title toc-ignore">Estudio sobre la efectividad de la certificación ISO 27001</h1>
  118 +<h1 class="title toc-ignore">Estudio sobre la efectividad de la certificación ISO 27001</h1>
119 119  
120 120 </div>
121 121  
... ... @@ -372,74 +372,63 @@ $(document).ready(function () {
372 372 </div>
373 373 <div id="evolucion-geografica" class="section level3">
374 374 <h3>Evolución geográfica</h3>
375   -<p>Este apartado estudiará la relación entre la certificación ISO 27001 y los ataques producidos, pero teniendo en cuenta la variable geográfica, ya que es posible que la certificación, aunque sea internacional, se implemente de una mejor o peor forma según la región. En primer lugar se generalizará por continente.</p>
376   -<p><img 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" 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