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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$Attacks,
group = 1,
xlab = "Months",
ylab = "Attacks",
data = attacks.evol,
geom = "line") +
geom_point() +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = 'loess') +
scale_x_discrete(labels = c("2012", "", "", "", "", "", "", "", "", "", "", "",
"2013", "", "", "", "", "", "", "", "", "", "", "",
"2014", "", "", "", "", "", "", "", "", "", "", "",
"2015", "", "", "", "", "", "", "", "", "", "", "",
"2016", "", "", "", "", "", "", "", "", "", "", ""))
graph1
}
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GetAttackTypePie <- function (Attacks){
attack.pie <- group_by(Attacks, Attack.standar)
attack.pie <- as.data.frame(table(attack.pie$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$Count), digits = 2), "%")),
# vjust=c(0),
# hjust=c(0)) +
coord_polar(theta="y") +
scale_x_discrete(labels = c("")) +
scale_y_discrete(labels = c("")) +
theme(plot.title = element_text(hjust = 0.5),
axis.title.x=element_blank(),
axis.title.y=element_blank()) +
ggtitle("Attack type pie")
graph1
}
GetAttackTypeEvolution <- function(Attacks){
Attacks.pre <- mutate(Attacks, Year = format(Attacks$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)
}
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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$X2012),],
geom = "col",
fill = Continent)
graph1
}
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#' Return every graph used in the report file
#'
#' @param Cert_PerCountry data.frame with the processed data of ISO 27001 certifications
#' @param Attacks data.frame with the processed data of cyberattacks
#'
#' @return data.frame
#' @export
GetReportGraphs <- function(Cert_PerCountry,Attacks) {
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#2012
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graph1 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2012)",
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x = reorder(country_short,X2012),
y = X2012,
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xlab = "Country",
ylab = "Number of certifications",
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data = Cert_PerCountry[Cert_PerCountry$X2012 > mean(Cert_PerCountry$X2012),],
geom = "col",
fill = Continent)
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attacks2k12 <- Attacks[Attacks$Date < "2013-01-01" & Attacks$Date >= "2012-01-01",]
frameAttacks2k12 <- as.data.frame(table(attacks2k12$Country))
colnames(frameAttacks2k12) <- c("Country","Attacks")
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graph2 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2012)",
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x = reorder(Country,Attacks),
y = Attacks,
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xlab = "Country",
ylab = "Number of attacks",
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data = frameAttacks2k12[frameAttacks2k12$Attacks > mean(frameAttacks2k12$Attacks),],
geom = "col",
fill = Continent)
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Attacks2012ByMonth <- mutate(attacks2k12, month = format(attacks2k12$Date, "%m")) %>% group_by(month)
Attack2012FreqByMonth <- as.data.frame(table(Attacks2012ByMonth$month))
colnames(Attack2012FreqByMonth) <- c("Month", "Attacks")
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graph3 <- ggplot2::qplot(x = as.numeric(Month),
y = Attacks,
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main = "Global cyberattack progression by month (2012)",
data = Attack2012FreqByMonth,
geom = c("point", "smooth"),
xlim = c(1,12),
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xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
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#2013
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graph4 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2013)",
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x = reorder(country_short,X2013),
y = X2013,
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xlab = "Country",
ylab = "Number of certifications",
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data = Cert_PerCountry[Cert_PerCountry$X2013 > mean(Cert_PerCountry$X2013),]
, geom = "col",
fill = Continent)
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attacks2k13 <- Attacks[Attacks$Date < "2014-01-01" & Attacks$Date >= "2013-01-01",]
frameAttacks2k13 <- as.data.frame(table(attacks2k13$Country))
colnames(frameAttacks2k13) <- c("Country","Attacks")
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graph5 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2013)",
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x = reorder(Country,Attacks),
y = Attacks,
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xlab = "Country",
ylab = "Number of attacks",
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data = frameAttacks2k13[frameAttacks2k13$Attacks > mean(frameAttacks2k13$Attacks),]
, geom = "col",
fill = Continent)
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Attacks2013ByMonth <- mutate(attacks2k13, month = format(attacks2k13$Date, "%m")) %>% group_by(month)
Attack2013FreqByMonth <- as.data.frame(table(Attacks2013ByMonth$month))
colnames(Attack2013FreqByMonth) <- c("Month", "Attacks")
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graph6 <- ggplot2::qplot(x = as.numeric(Month),
y = Attacks,
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main = "Global cyberattack progression by month (2013)",
data = Attack2013FreqByMonth,
geom = c("point", "smooth"),
xlim = c(1,12),
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xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
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#2014
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graph7 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2014)",
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x = reorder(country_short,X2014),
y = X2014,
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xlab = "Country",
ylab = "Number of certifications",
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data = Cert_PerCountry[Cert_PerCountry$X2014 > mean(Cert_PerCountry$X2014),]
, geom = "col",
fill = Continent)
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attacks2k14 <- Attacks[Attacks$Date < "2015-01-01" & Attacks$Date >= "2014-01-01",]
frameAttacks2k14 <- as.data.frame(table(attacks2k14$Country))
colnames(frameAttacks2k14) <- c("Country","Attacks")
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graph8 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2014)",
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x = reorder(Country,Attacks),
y = Attacks,
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xlab = "Country",
ylab = "Number of attacks",
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data = frameAttacks2k14[frameAttacks2k14$Attacks > mean(frameAttacks2k14$Attacks),]
, geom = "col",
fill = Continent)
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Attacks2014ByMonth <- mutate(attacks2k14, month = format(attacks2k14$Date, "%m")) %>% group_by(month)
Attack2014FreqByMonth <- as.data.frame(table(Attacks2014ByMonth$month))
colnames(Attack2014FreqByMonth) <- c("Month", "Attacks")
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graph9 <- ggplot2::qplot(x = as.numeric(Month),
y = Attacks,
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main = "Global cyberattack progression by month (2014)",
data = Attack2014FreqByMonth,
geom = c("point", "smooth"),
xlim = c(1,12),
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xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
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#2015
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graph10 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2015)",
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x = reorder(country_short,X2015),
y = X2015,
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xlab = "Country",
ylab = "Number of certifications",
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data = Cert_PerCountry[Cert_PerCountry$X2015 > mean(Cert_PerCountry$X2015),]
, geom = "col",
fill = Continent)
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attacks2k15 <- Attacks[Attacks$Date < "2016-01-01" & Attacks$Date >= "2015-01-01",]
frameAttacks2k15 <- as.data.frame(table(attacks2k15$Country))
colnames(frameAttacks2k15) <- c("Country","Attacks")
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graph11 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2015)",
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x = reorder(Country,Attacks),
y = Attacks,
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xlab = "Country",
ylab = "Number of attacks",
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data = frameAttacks2k15[frameAttacks2k15$Attacks > mean(frameAttacks2k15$Attacks),]
, geom = "col",
fill = Continent)
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Attacks2015ByMonth <- mutate(attacks2k15, month = format(attacks2k15$Date, "%m")) %>% group_by(month)
Attack2015FreqByMonth <- as.data.frame(table(Attacks2015ByMonth$month))
colnames(Attack2015FreqByMonth) <- c("Month", "Attacks")
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graph12 <- ggplot2::qplot(x = as.numeric(Month),
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y = Attacks,
main = "Global cyberattack progression by month (2015)",
data = Attack2015FreqByMonth,
geom = c("point", "smooth"),
xlim = c(1,12),
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xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
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list(graph1,graph2,graph3,graph4,graph5,graph6,graph7,graph8,graph9,graph10,graph11,graph12)
}
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