Imanol-Mikel Barba Sabariego / DDS · Files

#----------------------------------------------------------------
#-------------------------General evolution----------------------
#----------------------------------------------------------------

GetAttacksEvolution <- function(Attacks){

  attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year)
  attacks.evol <- as.data.frame(table(attacks.evol$Year))
  colnames(attacks.evol) <- c("Year","Attacks")

  graph1 <- ggplot2::qplot(main = "Cyberattacks evolution",
                           x = attacks.evol$Year,
                           y = attacks.evol$Attacks,
                           group = 1,
                           xlab = "Years",
                           ylab = "Attacks",
                           data = attacks.evol,
                           geom = "line")  +
            geom_point() +
            geom_label(aes(label=attacks.evol$Attacks),
                       vjust=c(1.5, 0, 0, -0.5, -0.5),
                       hjust=c(0.5, -0.5, 1.5, 0, 0))+
            theme(plot.title = element_text(hjust = 0.5))

  graph1

}

GetCertsEvolution <- function(Certs){

  Certs.evol <- data.frame(Year = c(2011, 2012, 2013, 2014, 2015),
                  Certs = c(sum(Certs$X2011),
                            sum(Certs$X2012),
                            sum(Certs$X2013),
                            sum(Certs$X2014),
                            sum(Certs$X2015)))

  graph1 <- ggplot2::qplot(main = "ISO 27001 evolution",
                           x = Certs.evol$Year,
                           y = Certs.evol$Certs,
                           group = 1,
                           xlab = "Years",
                           ylab = "Certifications",
                           data = Certs.evol,
                          geom = "line") +
            geom_point() +
            geom_label(aes(label=Certs.evol$Certs),
                       vjust=c(0.2, -0.7, 0, 0, 0.5),
                       hjust=c(-0.6, 1, 1.2, 1.2, 1.2))+
            theme(plot.title = element_text(hjust = 0.5))

  graph1

}

GetAttacksMonthEvolution <- function(Attacks){

  attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y-%m")) %>% group_by(Year)
  attacks.evol <- as.data.frame(table(attacks.evol$Year))
  colnames(attacks.evol) <- c("Year","Attacks")

  graph1 <- ggplot2::qplot(main = "Cyberattacks evolution",
                           x = attacks.evol$Year,
                           y = attacks.evol$Attacks,
                           group = 1,
                           xlab = "Months",
                           ylab = "Attacks",
                           data = attacks.evol,
                           geom = "line")  +
    geom_point() +
    theme(plot.title = element_text(hjust = 0.5)) +
    geom_smooth(method = 'loess') +
    scale_x_discrete(labels = c("2012", "", "", "", "", "", "", "", "", "", "", "",
                                "2013", "", "", "", "", "", "", "", "", "", "", "",
                                "2014", "", "", "", "", "", "", "", "", "", "", "",
                                "2015", "", "", "", "", "", "", "", "", "", "", "",
                                "2016", "", "", "", "", "", "", "", "", "", "", ""))

  graph1

}

#----------------------------------------------------------------
#-------------------------Attack type evolution------------------
#----------------------------------------------------------------

GetAttackTypePie <- function (Attacks){

  attack.pie <- group_by(Attacks, Attack.standar)
  attack.pie <- as.data.frame(table(attack.pie$Attack.standar))
  attack.pie <- setNames(attack.pie, c("Attack", "Count"))

  attack.pie <- attack.pie[attack.pie$Attack != "",]
  attack.pie <- attack.pie[attack.pie$Count > (sum(attack.pie$Count) * 0.01),]

  graph1 <- ggplot(data=attack.pie,
         aes(x=factor(1),
             y=Count,
             fill=Attack)) +
    geom_col(width = 1, color='black') +
#    geom_label(aes(label=paste( round(x = 100 - (sum(attack.pie$Count) / attack.pie$Count), digits = 2), "%")),
#               vjust=c(0),
#               hjust=c(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 pie")

  graph1
}

GetAttackTypeEvolution <- 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$Attack.standar))
  Attacks.pre <- setNames(Attacks.pre, c("Year", "Attack", "Count"))
  Attacks.pre <- Attacks.pre[Attacks.pre$Attack != "",]
  Attacks.pre <- plyr::arrange(Attacks.pre, Year, Attack)


  b <- as.character(sort(unique(Attacks.pre$Attack)))

  Attacks.desg <- data.frame(Year = sort(unique(Attacks.pre$Year)))

  for (i in 1:length(b)) {
    Attacks.desg <- cbind(Attacks.desg, Attacks.pre[Attacks.pre$Attack == b[i],3])
  }

  Attacks.desg <- setNames(Attacks.desg, c("Year", gsub(" ", ".", b)))

  Attacks.desg <- select(Attacks.desg, Year, `Account.Hijacking`, `DDoS`, `Defacement`, `DNS`, `Injection`, `Malware`)

  graph1 <- ggplot(data = Attacks.desg,
                   aes_string(x = colnames(Attacks.desg)[1]))

  graph1 <- graph1 +

    #geom_line(aes(y = Zero.day, group = 1, colour = "Zero.day")) +
    #geom_point(aes(y = Zero.day, group = 1, colour = "Zero.day")) +

    geom_line(aes(y = `Account.Hijacking`, group = 1, colour = "Account.Hijacking")) +
    geom_point(aes(y = `Account.Hijacking`, group = 1, colour = "Account.Hijacking")) +

    geom_line(aes(y = `DDoS`, group = 1, colour = "DDoS")) +
    geom_point(aes(y = `DDoS`, group = 1, colour = "DDoS")) +

    geom_line(aes(y = `Defacement`, group = 1, colour = "Defacement")) +
    geom_point(aes(y = `Defacement`, group = 1, colour = "Defacement")) +

    geom_line(aes(y = `DNS`, group = 1, colour = "DNS")) +
    geom_point(aes(y = `DNS`, group = 1, colour = "DNS")) +

    geom_line(aes(y = `Injection`, group = 1, colour = "Injection")) +
    geom_point(aes(y = `Injection`, group = 1, colour = "Injection")) +

    geom_line(aes(y = `Malware`, group = 1, colour = "Malware")) +
    geom_point(aes(y = `Malware`, group = 1, colour = "Malware")) +

    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle("Attack type evolution") +
    labs(colour = "Attack type") + xlab("Years") + ylab("Attacks")

  graph1

  }

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$Attack.standar))
  Attacks.pre <- setNames(Attacks.pre, c("Year", "Attack", "Count"))
  Attacks.pre <- Attacks.pre[Attacks.pre$Attack != "",]
  Attacks.pre <- plyr::arrange(Attacks.pre, Year, Attack)

  b <- as.character(sort(unique(Attacks.pre$Attack)))

  Attacks.desg <- data.frame(Year = sort(unique(Attacks.pre$Year)))

  for (i in 1:length(b)) {
    Attacks.desg <- cbind(Attacks.desg, Attacks.pre[Attacks.pre$Attack == b[i],3])
  }

  Attacks.desg <- setNames(Attacks.desg, c("Year", b))

  Attacks.desg <- select(Attacks.desg, Year, `Account Hijacking`, `DDoS`, `Defacement`, `DNS`, `Injection`, `Malware`)

  graph1 <- ggplot(data = Attacks.desg,
                   aes(x = Year, y = `DDoS`, group = 1)) +

    geom_line() +
    geom_point() +

    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle("DDoS") +
    xlab("Years") + ylab("Attacks")+
    stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))

  graph2 <- ggplot(data = Attacks.desg,
                   aes(x = Year, y = `Defacement`, group = 1)) +

    geom_line() +
    geom_point() +

    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle("Defacement") +
    xlab("Years") + ylab("Attacks")+
    stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))

  graph3 <- ggplot(data = Attacks.desg,
                   aes(x = Year, y = `Injection`, group = 1)) +

    geom_line() +
    geom_point() +

    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle("Injection") +
    xlab("Years") + ylab("Attacks")+
    stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))

  graph4 <- ggplot(data = Attacks.desg,
                   aes(x = Year, y = `Account Hijacking`, group = 1)) +

    geom_line() +
    geom_point() +

    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle("Account Hijacking") +
    xlab("Years") + ylab("Attacks")+
    stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))

  graph5 <- ggplot(data = Attacks.desg,
                   aes(x = Year, y = `Malware`, group = 1)) +

    geom_line() +
    geom_point() +

    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle("Malware") +
    xlab("Years") + ylab("Attacks")+
    stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))

  graph6 <- ggplot(data = Attacks.desg,
                   aes(x = Year, y = `DNS`, group = 1)) +

    geom_line() +
    geom_point() +

    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle("DNS") +
    xlab("Years") + ylab("Attacks")+
    stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))


  list(graph1, graph2, graph3, graph4, graph5, graph6)

}

#----------------------------------------------------------------
#-------------------------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){
  graph1 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2012)",
                           x = reorder(country_short,X2012),
                           y = X2012,
                           xlab = "Country",
                           ylab = "Number of certifications",
                           data = Cert_PerCountry[Cert_PerCountry$X2012 > mean(Cert_PerCountry$X2012),],
                           geom = "col",
                           fill = Continent)
  graph1
}

#' Return every graph used in the report file
#'
#' @param Cert_PerCountry data.frame with the processed data of ISO 27001 certifications
#' @param Attacks data.frame with the processed data of cyberattacks
#'
#' @return data.frame
#' @export
GetReportGraphs <- function(Cert_PerCountry,Attacks) {

  #2012

  graph1 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2012)",

                  x = reorder(country_short,X2012),
                  y = X2012,

                  xlab = "Country",
                  ylab = "Number of certifications",

                  data = Cert_PerCountry[Cert_PerCountry$X2012 > mean(Cert_PerCountry$X2012),],
                  geom = "col",
                  fill = Continent)

  attacks2k12 <- Attacks[Attacks$Date < "2013-01-01" & Attacks$Date >= "2012-01-01",]
  frameAttacks2k12 <- as.data.frame(table(attacks2k12$Country))
  colnames(frameAttacks2k12) <- c("Country","Attacks")

  graph2 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2012)",

                  x = reorder(Country,Attacks),
                  y = Attacks,

                  xlab = "Country",
                  ylab = "Number of attacks",

                  data = frameAttacks2k12[frameAttacks2k12$Attacks > mean(frameAttacks2k12$Attacks),],
                  geom = "col",
                  fill = Continent)

  Attacks2012ByMonth <- mutate(attacks2k12, month = format(attacks2k12$Date, "%m")) %>% group_by(month)
  Attack2012FreqByMonth <- as.data.frame(table(Attacks2012ByMonth$month))
  colnames(Attack2012FreqByMonth) <- c("Month", "Attacks")

  graph3 <- ggplot2::qplot(x = as.numeric(Month),
                  y = Attacks,

                  main = "Global cyberattack progression by month (2012)",
                  data = Attack2012FreqByMonth,
                  geom = c("point", "smooth"),
                  xlim = c(1,12),

                  xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)

  #2013

  graph4 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2013)",

                  x = reorder(country_short,X2013),
                  y = X2013,

                  xlab = "Country",
                  ylab = "Number of certifications",

                  data = Cert_PerCountry[Cert_PerCountry$X2013 > mean(Cert_PerCountry$X2013),]
                  , geom = "col",
                  fill = Continent)

  attacks2k13 <- Attacks[Attacks$Date < "2014-01-01" & Attacks$Date >= "2013-01-01",]
  frameAttacks2k13 <- as.data.frame(table(attacks2k13$Country))
  colnames(frameAttacks2k13) <- c("Country","Attacks")

  graph5 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2013)",

                  x = reorder(Country,Attacks),
                  y = Attacks,

                  xlab = "Country",
                  ylab = "Number of attacks",

                  data = frameAttacks2k13[frameAttacks2k13$Attacks > mean(frameAttacks2k13$Attacks),]
                  , geom = "col",
                  fill = Continent)

  Attacks2013ByMonth <- mutate(attacks2k13, month = format(attacks2k13$Date, "%m")) %>% group_by(month)
  Attack2013FreqByMonth <- as.data.frame(table(Attacks2013ByMonth$month))
  colnames(Attack2013FreqByMonth) <- c("Month", "Attacks")

  graph6 <- ggplot2::qplot(x = as.numeric(Month),
                  y = Attacks,

                  main = "Global cyberattack progression by month (2013)",
                  data = Attack2013FreqByMonth,
                  geom = c("point", "smooth"),
                  xlim = c(1,12),

                  xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)

  #2014

  graph7 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2014)",

                  x = reorder(country_short,X2014),
                  y = X2014,

                  xlab = "Country",
                  ylab = "Number of certifications",

                  data = Cert_PerCountry[Cert_PerCountry$X2014 > mean(Cert_PerCountry$X2014),]
                  , geom = "col",
                  fill = Continent)

  attacks2k14 <- Attacks[Attacks$Date < "2015-01-01" & Attacks$Date >= "2014-01-01",]
  frameAttacks2k14 <- as.data.frame(table(attacks2k14$Country))
  colnames(frameAttacks2k14) <- c("Country","Attacks")

  graph8 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2014)",

                  x = reorder(Country,Attacks),
                  y = Attacks,

                  xlab = "Country",
                  ylab = "Number of attacks",

                  data = frameAttacks2k14[frameAttacks2k14$Attacks > mean(frameAttacks2k14$Attacks),]
                  , geom = "col",
                  fill = Continent)

  Attacks2014ByMonth <- mutate(attacks2k14, month = format(attacks2k14$Date, "%m")) %>% group_by(month)
  Attack2014FreqByMonth <- as.data.frame(table(Attacks2014ByMonth$month))
  colnames(Attack2014FreqByMonth) <- c("Month", "Attacks")

  graph9 <- ggplot2::qplot(x = as.numeric(Month),
                  y = Attacks,

                  main = "Global cyberattack progression by month (2014)",
                  data = Attack2014FreqByMonth,
                  geom = c("point", "smooth"),
                  xlim = c(1,12),

                  xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)

  #2015

  graph10 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2015)",

                  x = reorder(country_short,X2015),
                  y = X2015,

                  xlab = "Country",
                  ylab = "Number of certifications",

                  data = Cert_PerCountry[Cert_PerCountry$X2015 > mean(Cert_PerCountry$X2015),]
                  , geom = "col",
                  fill = Continent)

  attacks2k15 <- Attacks[Attacks$Date < "2016-01-01" & Attacks$Date >= "2015-01-01",]
  frameAttacks2k15 <- as.data.frame(table(attacks2k15$Country))
  colnames(frameAttacks2k15) <- c("Country","Attacks")

  graph11 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2015)",

                  x = reorder(Country,Attacks),
                  y = Attacks,

                  xlab = "Country",
                  ylab = "Number of attacks",

                  data = frameAttacks2k15[frameAttacks2k15$Attacks > mean(frameAttacks2k15$Attacks),]
                  , geom = "col",
                  fill = Continent)

  Attacks2015ByMonth <- mutate(attacks2k15, month = format(attacks2k15$Date, "%m")) %>% group_by(month)
  Attack2015FreqByMonth <- as.data.frame(table(Attacks2015ByMonth$month))
  colnames(Attack2015FreqByMonth) <- c("Month", "Attacks")

  graph12 <- ggplot2::qplot(x = as.numeric(Month),

                  y = Attacks,
                  main = "Global cyberattack progression by month (2015)",
                  data = Attack2015FreqByMonth,
                  geom = c("point", "smooth"),
                  xlim = c(1,12),

                  xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)

  list(graph1,graph2,graph3,graph4,graph5,graph6,graph7,graph8,graph9,graph10,graph11,graph12)
}