From 58c7c07c3462d14e1136c6937b11e7785ecbff00 Mon Sep 17 00:00:00 2001 From: mituga93 Date: Wed, 28 Dec 2016 20:38:20 +0100 Subject: [PATCH] Added geolotacion evolution to main report body and implemented the required functions to draw graphs in the package --- ISO27001effectiveness/DESCRIPTION | 5 +++-- ISO27001effectiveness/R/Libraries.R | 1 + ISO27001effectiveness/R/ReportGraphs.R | 257 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++--- ISO27001effectiveness/Report.Rmd | 81 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-- ISO27001effectiveness/Report.html | 37 +++++++++++++++++++++++++++++-------- ISO27001effectiveness/Report_cache/html/__packages | 1 + ISO27001effectiveness/Report_cache/html/unnamed-chunk-10_17b6bd20fff5afd30e114864f634b0a1.RData 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a/ISO27001effectiveness/DESCRIPTION b/ISO27001effectiveness/DESCRIPTION index 90fd11a..dc50cb8 100644 --- a/ISO27001effectiveness/DESCRIPTION +++ b/ISO27001effectiveness/DESCRIPTION @@ -2,7 +2,7 @@ Package: ISO27001effectiveness Type: Package Title: Study about how ISO27001 certifications affect cyberattacks Version: 0.1.0 -Author: Miguel Tu帽on, Patricia Lopez, Sara Queipo, Imanol-Mikel Barba +Author: Miguel Tu駉n, Patricia Lopez, Sara Queipo, Imanol-Mikel Barba Maintainer: The DDS development team Description: Compare the cyberattacks reported in hackmaggedon website with data obtained from the official ISO survey to 27001. Working around countries, @@ -14,4 +14,5 @@ RoxygenNote: 5.0.1 Imports: xlsx, ggplot2, countrycode, - dplyr + dplyr, + tidyr diff --git a/ISO27001effectiveness/R/Libraries.R b/ISO27001effectiveness/R/Libraries.R index 80ec13a..c0d38ba 100644 --- a/ISO27001effectiveness/R/Libraries.R +++ b/ISO27001effectiveness/R/Libraries.R @@ -2,3 +2,4 @@ library(xlsx) library(ggplot2) library(countrycode) library(dplyr) +library(tidyr) diff --git a/ISO27001effectiveness/R/ReportGraphs.R b/ISO27001effectiveness/R/ReportGraphs.R index 10c09e5..c4d8152 100644 --- a/ISO27001effectiveness/R/ReportGraphs.R +++ b/ISO27001effectiveness/R/ReportGraphs.R @@ -1,3 +1,8 @@ + +#---------------------------------------------------------------- +#-------------------------General evolution---------------------- +#---------------------------------------------------------------- + GetAttacksEvolution <- function(Attacks){ attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year) @@ -49,7 +54,6 @@ GetCertsEvolution <- function(Certs){ } - GetAttacksMonthEvolution <- function(Attacks){ attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y-%m")) %>% group_by(Year) @@ -77,6 +81,9 @@ GetAttacksMonthEvolution <- function(Attacks){ } +#---------------------------------------------------------------- +#-------------------------Attack type evolution------------------ +#---------------------------------------------------------------- GetAttackTypePie <- function (Attacks){ @@ -101,7 +108,7 @@ GetAttackTypePie <- function (Attacks){ theme(plot.title = element_text(hjust = 0.5), axis.title.x=element_blank(), axis.title.y=element_blank()) + - ggtitle("Attack type pie") + ggtitle("Attacks pie") graph1 } @@ -160,7 +167,6 @@ GetAttackTypeEvolution <- function(Attacks){ } - GetAttackTypeTopEvolution <- function(Attacks){ Attacks.pre <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year, Attack.standar) Attacks.pre <- as.data.frame(table(Attacks.pre$Year, Attacks.pre$Attack.standar)) @@ -251,8 +257,253 @@ GetAttackTypeTopEvolution <- function(Attacks){ } +#---------------------------------------------------------------- +#-------------------------Geolocal evolution--------- +#---------------------------------------------------------------- + +GetContinentPie <- function (Attacks, Cert_PerCountry){ + + attack.pie <- group_by(Attacks, Continent) + attack.pie <- as.data.frame(table(attack.pie$Continent)) + attack.pie <- setNames(attack.pie, c("Continent", "Count")) + + attack.pie <- attack.pie[attack.pie$Continent != "",] + #attack.pie <- attack.pie[attack.pie$Count > (sum(attack.pie$Count) * 0.01),] + + graph1 <- ggplot(data=attack.pie, + aes(x=factor(1), + y=Count, + fill=Continent)) + + geom_col(width = 1, color='black') + + geom_label(aes(label=paste(round(100 * attack.pie$Count / sum(attack.pie$Count), 2), "%")), + vjust=c(-2.5, 0, 2, 0, -2.5), + hjust=c(1.4, 1, 0.5, 0.5, 0)) + + coord_polar(theta="y") + + scale_x_discrete(labels = c("")) + + scale_y_discrete(labels = c("")) + + theme(plot.title = element_text(hjust = 0.5), + axis.title.x=element_blank(), + axis.title.y=element_blank()) + + ggtitle("Attacks") + + cert.pie <- mutate(Cert_PerCountry, Total = X2011 + X2012 + X2013 + X2014 + X2015) %>% + group_by(Continent) + cert.pie <- data.frame(Continent = c("Asia", "Europe", "Africa", "Americas", "Oceania"), + Count = c(sum((cert.pie[cert.pie$Continent == "Asia",])$Total), + sum((cert.pie[cert.pie$Continent == "Europe",])$Total), + sum((cert.pie[cert.pie$Continent == "Africa",])$Total), + sum((cert.pie[cert.pie$Continent == "Americas",])$Total), + sum((cert.pie[cert.pie$Continent == "Oceania",])$Total))) + graph2 <- ggplot(data=cert.pie, + aes(x=factor(1), + y=Count, + fill=Continent)) + + geom_col(width = 1, color='black') + + geom_label(aes(label=paste(round(100 * cert.pie$Count / sum(cert.pie$Count), 2), "%")), + vjust=c(0, -1.5, -2.5, -2.5, -2.5), + hjust=c(0.5, 0.2, 0.5, 1.8, -0.5)) + + coord_polar(theta="y") + + scale_x_discrete(labels = c("")) + + scale_y_discrete(labels = c("")) + + theme(plot.title = element_text(hjust = 0.5), + axis.title.x=element_blank(), + axis.title.y=element_blank()) + + ggtitle("ISO 27001") + + list(graph1, graph2) +} + +GetContinentAttacksEvolution <- function(Attacks){ + + attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Continent, Year) + attacks.evol <- as.data.frame(table(attacks.evol$Continent, attacks.evol$Year)) + colnames(attacks.evol) <- c("Continent", "Year","Attacks") + + attacks.evol <- attacks.evol[attacks.evol$Continent != "Oceania",] + attacks.evol <- attacks.evol[attacks.evol$Continent != "Africa",] + + graph1 <- ggplot2::qplot(main = "Cyberattacks evolution", + x = attacks.evol$Year, + y = attacks.evol$Attacks, + group = Continent, + xlab = "Years", + ylab = "Attacks", + data = attacks.evol, + geom = "point", + color = Continent) + + geom_line() + + theme(plot.title = element_text(hjust = 0.5)) + + graph1 + +} + +GetContinentCertsEvolution <- function(Cert_PerCountry){ + + certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>% group_by(Continent, Year) + + certs.evol <- summarise(certs.evol, + #Continent = Continent, + #Year = Year, + Certs = sum(Certs)) + certs.evol$Year <- substr(certs.evol$Year,2,5) + + certs.evol <- certs.evol[certs.evol$Continent != "Oceania",] + certs.evol <- certs.evol[certs.evol$Continent != "Africa",] + + graph1 <- ggplot2::qplot(main = "ISO 27001 evolution", + x = certs.evol$Year, + y = certs.evol$Certs, + group = Continent, + xlab = "Years", + ylab = "Certifications", + data = certs.evol, + geom = "point", + color = Continent) + + geom_line() + + theme(plot.title = element_text(hjust = 0.5)) + + graph1 + +} + +GetContinentAttacksTopEvolution <- function(Attacks){ + attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year, Continent) + attacks.evol <- as.data.frame(table(attacks.evol$Year, attacks.evol$Continent)) + attacks.evol <- setNames(attacks.evol, c("Year", "Continent", "Count")) + attacks.evol <- attacks.evol[attacks.evol$Continent != "",] + + attacks.evol <- spread(attacks.evol, "Continent", "Count") + + graph1 <- ggplot(data = attacks.evol, + aes(x = Year, y = Americas, group = 1)) + + + geom_line() + + geom_point() + + + theme(plot.title = element_text(hjust = 0.5)) + + ggtitle("Americas") + + xlab("Years") + ylab("Attacks")+ + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..)) + + graph2 <- ggplot(data = attacks.evol, + aes(x = Year, y = Asia, group = 1)) + + + geom_line() + + geom_point() + + + theme(plot.title = element_text(hjust = 0.5)) + + ggtitle("Asia") + + xlab("Years") + ylab("Attacks")+ + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..)) + + graph3 <- ggplot(data = attacks.evol, + aes(x = Year, y = Europe, group = 1)) + + + geom_line() + + geom_point() + + + theme(plot.title = element_text(hjust = 0.5)) + + ggtitle("Europe") + + xlab("Years") + ylab("Attacks")+ + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..)) + + + list(graph1, graph2, graph3) + +} + +GetContinentCertsTopEvolution <- function(Attacks){ + + certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>% group_by(Continent, Year) + certs.evol <- summarise(certs.evol, + #Continent = Continent, + #Year = Year, + Certs = sum(Certs)) + certs.evol$Year <- substr(certs.evol$Year,2,5) + + certs.evol <- spread(certs.evol, "Continent", "Certs") + + graph1 <- ggplot(data = certs.evol, + aes(x = Year, y = Americas, group = 1)) + + + geom_line() + + geom_point() + + + theme(plot.title = element_text(hjust = 0.5)) + + ggtitle("Americas") + + xlab("Years") + ylab("Certifications")+ + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..)) + + graph2 <- ggplot(data = certs.evol, + aes(x = Year, y = Asia, group = 1)) + + + geom_line() + + geom_point() + + + theme(plot.title = element_text(hjust = 0.5)) + + ggtitle("Asia") + + xlab("Years") + ylab("Certifications")+ + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..)) + + graph3 <- ggplot(data = certs.evol, + aes(x = Year, y = Europe, group = 1)) + + + geom_line() + + geom_point() + + + theme(plot.title = element_text(hjust = 0.5)) + + ggtitle("Europe") + + xlab("Years") + ylab("Certifications")+ + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..)) + + + list(graph1, graph2, graph3) + +} + +GetCountriesCol <- function(Attacks, Cert_PerCountry){ + + certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>% group_by(Continent, country_short) + + certs.evol <- summarise(certs.evol, + Certs = sum(Certs)) + + graph1 <- ggplot2::ggplot(aes(x = reorder(country_short, Certs), + y = Certs), + data = certs.evol[certs.evol$Certs > (sum(certs.evol$Certs) * 0.02),]) + + geom_col(aes(fill = Continent)) + + theme(plot.title = element_text(hjust = 0.5)) + + ggtitle("ISO 27001") + + xlab("Country") + ylab("Certifications") + + attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year, Continent, Country) + attacks.evol <- as.data.frame(table(attacks.evol$Year, attacks.evol$Continent, attacks.evol$Country)) + attacks.evol <- setNames(attacks.evol, c("Year", "Continent", "Country", "Count")) + attacks.evol <- attacks.evol[attacks.evol$Continent != "",] + attacks.evol <- attacks.evol[attacks.evol$Country != "",] + attacks.evol <- attacks.evol[attacks.evol$Country != "AU",] + + attacks.evol <- attacks.evol[attacks.evol$Count > (sum((attacks.evol[attacks.evol$Country != "US",])$Count) * 0.01),] + attacks.evol <- arrange(attacks.evol, Count) + + + graph2 <- ggplot2::ggplot(aes(x = reorder(Country, Count), + y = Count), + data = attacks.evol) + + geom_col(aes(fill = Continent)) + + theme(plot.title = element_text(hjust = 0.5)) + + ggtitle("Attacks") + + xlab("Country") + ylab("Attacks") + + list(graph1, graph2) + + } +#---------------------------------------------------------------- +#---------------------------------------------------------------- +#---------------------------------------------------------------- GetBaseCertsGraph <- function(Cert_PerCountry, year){ diff --git a/ISO27001effectiveness/Report.Rmd b/ISO27001effectiveness/Report.Rmd index ebf19a2..f906f9f 100644 --- a/ISO27001effectiveness/Report.Rmd +++ b/ISO27001effectiveness/Report.Rmd @@ -5,13 +5,14 @@ output: pdf_document: default --- -```{r setup, include=TRUE, echo=FALSE, warning=FALSE, results='hide', message=FALSE} +```{r setup, include=TRUE, echo=FALSE, warning=FALSE, results='hide', message=FALSE ,cache=FALSE} library(knitr) knitr::opts_chunk$set(echo = FALSE) knitr::opts_chunk$set(warning = FALSE) knitr::opts_chunk$set(message = FALSE) +knitr::opts_chunk$set(cache = FALSE) devtools::load_all(".") ``` @@ -161,7 +162,83 @@ La tendencia con una mayor pendiente negativa es _Injection_ (`r slope3`) y junt Por otro lado tenemos _DNS_ cuya tendencia tambi茅n tiene una pendiente negativa (`r slope6`), pero es muy moderada y adem谩s podemos observar que los casos reportados en nuestra fuente de datos son mas bien escasos. -### Evoluci贸n por tipo de ataque +### Evoluci贸n geogr谩fica + +Este apartado estudiar谩 la relaci贸n entre la certificaci贸n ISO 27001 y los ataques producidos, pero teniendo en cuenta la variable geogr谩fica, ya que es posible que la certificaci贸n, aunque sea internacional, se implemente de una mejor o peor forma seg煤n la regi贸n geogr谩fica. En primer lugar observaremos la comparaci贸n por continente. + +```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'} +graphs <- ISO27001effectiveness::GetContinentPie(Attacks, Cert_PerCountry) +graphs[[1]] +``` +```{r fig.width=4.5, fig.height=3} +graphs[[2]] +``` + +Se puede observar a simple vista que los continentes que reciben m谩s ataques, por una cuesti贸n l贸gica de superficie e intereses, son por orden Am茅rica, Asia y Europa. En cambio los continentes que mas certificaciones ISO 27001 obtienen son por orden Asia, Europa y Am茅rica. Tanto 脕frica como Oceania podemos descartarlos en este estudio ya que sus porcentajes no son relevantes. Observemos ahora c贸mo influye esto en el tiempo. + +```{r fig.width=4.5, fig.height=4,out.extra='style="float:left"'} +ISO27001effectiveness::GetContinentCertsEvolution(Cert_PerCountry) +``` +```{r fig.width=4.5, fig.height=4} +ISO27001effectiveness::GetContinentAttacksEvolution(Attacks) +``` + +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 num茅ricamente y no solo aparentemente. + +```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'} +topAttacks <- ISO27001effectiveness::GetContinentAttacksTopEvolution(Attacks) +topCerts <- ISO27001effectiveness::GetContinentCertsTopEvolution(Cert_PerCountry) +topCerts[[1]] +slope_Ame_Cert <- (fit[5] - fit[1]) / 4 +``` +```{r fig.width=4.5, fig.height=3} +topAttacks[[1]] +slope_Ame_Att <- (fit[5] - fit[1]) / 4 +``` + +La pendiente de los ataques para _Am茅rica_ es `r slope_Ame_Att` mientras que la pendiente de las certificaciones es `r slope_Ame_Cert`. + +```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'} +topCerts[[2]] +slope_Asi_Cert <- (fit[5] - fit[1]) / 4 +``` +```{r fig.width=4.5, fig.height=3} +topAttacks[[2]] +slope_Asi_Att <- (fit[5] - fit[1]) / 4 +``` + +La pendiente de los ataques para _Asia_ es `r slope_Asi_Att` mientras que la pendiente de las certificaciones es `r slope_Asi_Cert`. + +```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'} +topCerts[[3]] +slope_Eu_Cert <- (fit[5] - fit[1]) / 4 +``` +```{r fig.width=4.5, fig.height=3} +topAttacks[[3]] +slope_Eu_Att <- (fit[5] - fit[1]) / 4 +``` + +La pendiente de los ataques para _Europa_ es `r slope_Eu_Att` mientras que la pendiente de las certificaciones es `r slope_Eu_Cert`. + +Todas las pendientes podr铆an representar la relaci贸n que buscamos, en la que un aumento en las certificaciones produce un descenso en los ataques. + +El an谩lisis puede aumentar en profundidad estableciendo superficies geogr谩ficas m谩s peque帽as y asi obtener m谩s precisi贸n, observemos lo que ocurre a nivel de paises. Se mostrar谩n a continuaci贸n los paises que superan aproximadamente un 2% de los ataques/certificaciones totales ya que la lista total de paises es demasiado extensa. + +```{r fig.width=4.5, fig.height=3,out.extra='style="float:left"'} +top <- GetCountriesCol(Attacks, Cert_PerCountry) +top[[1]] +``` +```{r fig.width=4.5, fig.height=3} +top[[2]] +``` + +Como se puede observar en la parte de certificaciones destaca de largo Jap贸n sobre los dem谩s, 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 est谩 muy bajo en certificaciones. A continuaci贸n observaremos la evoluci贸n temporal del top 3 paises en ataques recibidos y en certificaciones obtenidas, varios de ellos coinciden, tenemos por la parte de las certificaciones a _Jap贸n_, por la parte de los ataques a _Estados Unidos_, y com煤n a ambas _Reino Unido_ e _India_. + + + + + +# Viejo De los datos mostrados se pueden hacer diferentes observaciones: diff --git a/ISO27001effectiveness/Report.html b/ISO27001effectiveness/Report.html index 8641296..044d798 100644 --- a/ISO27001effectiveness/Report.html +++ b/ISO27001effectiveness/Report.html @@ -340,15 +340,15 @@ $(document).ready(function () {

Evoluci贸n general

En primer lugar, para saber si este estudio tiene sentido, se observar谩 la evoluci贸n temporal tanto de los ciberataques reportados como de las certificaciones realizadas en busca de patrones que puedan indicar una relaci贸n entre ambas evoluciones.

-

+

Como se puede observar en las gr谩ficas, el n煤mero de empresas que obtienen la certificaci贸n crece anualmente, mientras que el numero de ataques es un poco m谩s inestable. Del a帽o 2013 al 2014 el n煤mero de ataques se reduce dr谩sticamente, esto puede deberse a un problema en la fuente de datos, ya que al depender directamente de un ser humano pueden existir intervalos en los que se hayan registrado menos datos (por problemas del administrador, como falta de tiempo o interes). Otra explicaci贸n plausible es que justo en el a帽o 2013 se produjo una revisi贸n de la certificaci贸n, produciendose la ISO 27001:2013 que sustituy贸 a su predecesora 27001:2005, los cambios realizados pueden consultarse en la web oficial. Para indagar un poco sobre este tema a continuaci贸n se representa la evoluci贸n de los ataques mes a mes, al aumentar la precisi贸n en el eje X se puede observar mejor la tendencia y abstraernos un poco de esos errores humanos.

-

+

Podemos observar que la tendencia de los ataques tiene una pendiente negativa, mas pronunciada en el momento en que se realiza una actualizaci贸n de las normas (27001:2013) y que presenta algo parecido, aunque muy moderada, a una recuperaci贸n, pasado un periodo de tiempo. Tambi茅n se podr铆a relacionar la evoluci贸n de los ataques con la evoluci贸n de los certificados, si tenemos en cuenta el desplazamiento de un a帽o para establecer la relaci贸n de causalidad. Por ejemplo podemos observar como los a帽os en que menos crece el n煤mero de certificaciones (2012 y 2014), los ataques aumentan ligeramente en los a帽os siguientes (2013 y 2015), mientras que en los a帽os que mayor es el aumento de las mismas (2013 y 2015) la cantidad de ataques producidos disminuye para los a帽os consecutivos (2014 y 2016).

Evoluci贸n por tipo de ataque

Podr铆a darse el caso de que la certificaci贸n tenga uan efectividad mayor contra ciertas t茅cnicas de ataque y que dicha efectividad se camufle entre el resto de t茅cnicas, por ello contemplamos el estudio individual de los diferentes tipos de ataques definidos en nuestra fuente de datos. Existen multitud de ellos por lo que el estudio se tendr谩 que centrar en una peque帽a parte de ellos, los m谩s empleados. Para llevar a cabo esta elecci贸n se representan a continuaci贸n aquellos que representan al menos un 1% del total de ataques producidos.

-

+

Como se puede observar en el gr谩fico anterior, la mayor parte de los ataques registrados en nuestra fuente de datos emplean las siguientes t茅cnicas, que ser谩n las estudiadas a continuaci贸n:

  • Injection: Cualquier tipo de inyeccion ya sea de c贸digo, SQL, etc

    • @@ -358,13 +358,35 @@ $(document).ready(function () {

  • Malware: Programas que se ejecutan en el sistema victima para llevar a cabo actividades maliciosas.

  • DNS: Ataques basados en los servidores DNS de la victima, como el poisoning, que consigue retornar las IPs que no deber铆a a cada petici贸n.

-

+

Como podemos observar en la evoluci贸n temporal reflejada en el gr谩fico, existen ciertas t茅cnicas que mas o menos son constantes en el tiempo, y existen otras que tienen o podrian tener una pendiente negativa clara. Nos centraremos en aquellos tipos de ataques que muestran dicha tendencia al descenso y que podremos observar a continuaci贸n.

-

+

Los tipos de ataque est谩n ordenados por la pendiente de su tendencia, de menor a mayor, para reflejar cu谩les est谩n descendiendo m谩s r谩pido y por lo tanto cu谩les podr铆an reflejar mejor el aumento de certificaciones ISO27001 expedidas.

Tanto Malware como Account Hijacking tienen una tendencia con pendiente positiva, 10.1 y 20.2 respectivamente, por lo que quedan descartadas para este estudio.

-

La tendencia con una mayor pendiente negativa es Injection (-173.5) y junto con DDoS (-49.3), que tambi茅n tiene una tendencia llamativa, pueden ser los que est茅n mas relacionados con la efectividad de la certificaci贸n ISO 27001.

+

La tendencia con una mayor pendiente negativa es Injection (-173.5) y junto con DDoS (-49.2), que tambi茅n tiene una tendencia llamativa, pueden ser los que est茅n mas relacionados con la efectividad de la certificaci贸n ISO 27001.

Por otro lado tenemos DNS cuya tendencia tambi茅n tiene una pendiente negativa (-3.8), pero es muy moderada y adem谩s podemos observar que los casos reportados en nuestra fuente de datos son mas bien escasos.

+
+
+

Evoluci贸n geogr谩fica

+

Este apartado estudiar谩 la relaci贸n entre la certificaci贸n ISO 27001 y los ataques producidos, pero teniendo en cuenta la variable geogr谩fica, ya que es posible que la certificaci贸n, aunque sea internacional, se implemente de una mejor o peor forma seg煤n la regi贸n geogr谩fica. En primer lugar observaremos la comparaci贸n por continente.

+

+

Se puede observar a simple vista que los continentes que reciben m谩s ataques, por una cuesti贸n l贸gica de superficie e intereses, son por orden Am茅rica, Asia y Europa. En cambio los continentes que mas certificaciones ISO 27001 obtienen son por orden Asia, Europa y Am茅rica. Tanto 脕frica como Oceania podemos descartarlos en este estudio ya que sus porcentajes no son relevantes. Observemos ahora c贸mo influye esto en el tiempo.

+

+

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 num茅ricamente y no solo aparentemente.

+

+

La pendiente de los ataques para Am茅rica es -100.8 mientras que la pendiente de las certificaciones es 275.2.

+

+

La pendiente de los ataques para Asia es -58.4 mientras que la pendiente de las certificaciones es 1008.4.

+

+

La pendiente de los ataques para Europa es -57.4 mientras que la pendiente de las certificaciones es 1209.4.

+

Todas las pendientes podr铆an representar la relaci贸n que buscamos, en la que un aumento en las certificaciones produce un descenso en los ataques.

+

El an谩lisis puede aumentar en profundidad estableciendo superficies geogr谩ficas m谩s peque帽as y asi obtener m谩s precisi贸n, observemos lo que ocurre a nivel de paises. Se mostrar谩n a continuaci贸n los paises que superan aproximadamente un 2% de los ataques/certificaciones totales ya que la lista total de paises es demasiado extensa.

+

+

Como se puede observar en la parte de certificaciones destaca de largo Jap贸n sobre los dem谩s, 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 est谩 muy bajo en certificaciones. A continuaci贸n observaremos la evoluci贸n temporal del top 3 paises en ataques recibidos y en certificaciones obtenidas.

+
+ +
+

Viejo

De los datos mostrados se pueden hacer diferentes observaciones:

  • De 2014 a 2015, USA pasa de tener 654 a 1247 empresas con certificaci贸n 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 s贸lo haber pasado de 566 a 654 empresas con dicha certificaci贸n.
    • @@ -377,8 +399,6 @@ $(document).ready(function () {

La primera hip贸tesis puede ser comprobada con los datos que disponemos. A continuaci贸n se muestra una l铆nea temporal de ciberataques globales entre los a帽os 2012 y 2015.

Observamos que se produjo lo contrario, baj贸 el n煤mero de ciberataques.

-
-

Conclusiones

Por lo observado anteriormente, se puede concluir que:

@@ -397,6 +417,7 @@ $(document).ready(function () {

Creemos que la investigaci贸n de estas cuestiones puede dar m谩s robustez a las conclusiones expuestas en este estudio.

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