Imanol-Mikel Barba Sabariego / DDS · Commits

Commit a947c3a7651405a65e9c49ca4c5b54b3be5d6166

Authored by Miguel Tuñón
1 parent 2ab0e00b

Added attack type evolution to main report boy and implemented the required func… 

…tions to draw graphs in the package
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Showing 14 changed files with 323 additions and 27 deletions
ISO27001effectiveness/R/Hackmageddon_Parser.R
  View file @a947c3a
... ... @@ -90,6 +90,8 @@ ProcessHMRaw <- function(dataset.raw, dateOffset){
90 90 dataset <- merge(x = dataset, y = Attack.config, by = "Attack", all.x = TRUE)
91 91 dataset <- subset(dataset, select = -c(Attack))
92 92  
  93 + dataset <- FilterMultiAttack(dataset)
  94 +
93 95 dataset
94 96 }
95 97  
... ... @@ -132,6 +134,46 @@ FilterMultiCountry &lt;- function(dataset.pre) {
132 134 }
133 135 }
134 136  
  137 +#' Look for rows with more than one attack and split into multiple
  138 +#'
  139 +#' @param dataset.pre data.frame to process
  140 +#'
  141 +#' @return data.frame
  142 +FilterMultiAttack <- function(dataset.pre) {
  143 +
  144 + #data.frame with multiple taget country rows
  145 + multi <- dataset.pre[grepl("/",dataset.pre$Attack.standar),]
  146 +
  147 + if (nrow(multi) == 0) { #Ignore if there are not multiple target rows
  148 +
  149 + dataset.pre
  150 + } else {
  151 +
  152 + #data.frame with every rows except multi ones
  153 + dataset <- dataset.pre[!grepl("/",dataset.pre$Attack.standar),]
  154 +
  155 + #Iterate over multi
  156 + for (i in 1:nrow(multi)) {
  157 + crow <- multi[i,] #current row
  158 +
  159 + country_s <- strsplit(toString(crow$Attack.standar), "/")[[1]] #each country target
  160 +
  161 + #Iterate over each country target
  162 + for (j in 1:length(country_s)) {
  163 + #Date <- crow[1]
  164 + #Attack.standar <- country_s[j]
  165 + #Target <- crow[3]
  166 + #Country <- crow[4]
  167 + new.row <- data.frame(crow)
  168 + new.row$Attack.standar <- country_s[j]
  169 + dataset <- rbind(dataset, new.row) #Append new row to output data.frame
  170 + }
  171 + }
  172 +
  173 + dataset
  174 + }
  175 +}
  176 +
135 177 #' Parse every excel file into a folder
136 178 #'
137 179 #' @param folder path to the folder to iterate
... ...
ISO27001effectiveness/R/ReportGraphs.R
  View file @a947c3a
... ... @@ -78,6 +78,181 @@ GetAttacksMonthEvolution &lt;- function(Attacks){
78 78 }
79 79  
80 80  
  81 +GetAttackTypePie <- function (Attacks){
  82 +
  83 + attack.pie <- group_by(Attacks, Attack.standar)
  84 + attack.pie <- as.data.frame(table(attack.pie$Attack.standar))
  85 + attack.pie <- setNames(attack.pie, c("Attack", "Count"))
  86 +
  87 + attack.pie <- attack.pie[attack.pie$Attack != "",]
  88 + attack.pie <- attack.pie[attack.pie$Count > (sum(attack.pie$Count) * 0.01),]
  89 +
  90 + graph1 <- ggplot(data=attack.pie,
  91 + aes(x=factor(1),
  92 + y=Count,
  93 + fill=Attack)) +
  94 + geom_col(width = 1, color='black') +
  95 +# geom_label(aes(label=paste( round(x = 100 - (sum(attack.pie$Count) / attack.pie$Count), digits = 2), "%")),
  96 +# vjust=c(0),
  97 +# hjust=c(0)) +
  98 + coord_polar(theta="y") +
  99 + scale_x_discrete(labels = c("")) +
  100 + scale_y_discrete(labels = c("")) +
  101 + theme(plot.title = element_text(hjust = 0.5),
  102 + axis.title.x=element_blank(),
  103 + axis.title.y=element_blank()) +
  104 + ggtitle("Attack type pie")
  105 +
  106 + graph1
  107 +}
  108 +
  109 +GetAttackTypeEvolution <- function(Attacks){
  110 + Attacks.pre <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year, Attack.standar)
  111 + Attacks.pre <- as.data.frame(table(Attacks.pre$Year, Attacks.pre$Attack.standar))
  112 + Attacks.pre <- setNames(Attacks.pre, c("Year", "Attack", "Count"))
  113 + Attacks.pre <- Attacks.pre[Attacks.pre$Attack != "",]
  114 + Attacks.pre <- plyr::arrange(Attacks.pre, Year, Attack)
  115 +
  116 +
  117 + b <- as.character(sort(unique(Attacks.pre$Attack)))
  118 +
  119 + Attacks.desg <- data.frame(Year = sort(unique(Attacks.pre$Year)))
  120 +
  121 + for (i in 1:length(b)) {
  122 + Attacks.desg <- cbind(Attacks.desg, Attacks.pre[Attacks.pre$Attack == b[i],3])
  123 + }
  124 +
  125 + Attacks.desg <- setNames(Attacks.desg, c("Year", gsub(" ", ".", b)))
  126 +
  127 + Attacks.desg <- select(Attacks.desg, Year, `Account.Hijacking`, `DDoS`, `Defacement`, `DNS`, `Injection`, `Malware`)
  128 +
  129 + graph1 <- ggplot(data = Attacks.desg,
  130 + aes_string(x = colnames(Attacks.desg)[1]))
  131 +
  132 + graph1 <- graph1 +
  133 +
  134 + #geom_line(aes(y = Zero.day, group = 1, colour = "Zero.day")) +
  135 + #geom_point(aes(y = Zero.day, group = 1, colour = "Zero.day")) +
  136 +
  137 + geom_line(aes(y = `Account.Hijacking`, group = 1, colour = "Account.Hijacking")) +
  138 + geom_point(aes(y = `Account.Hijacking`, group = 1, colour = "Account.Hijacking")) +
  139 +
  140 + geom_line(aes(y = `DDoS`, group = 1, colour = "DDoS")) +
  141 + geom_point(aes(y = `DDoS`, group = 1, colour = "DDoS")) +
  142 +
  143 + geom_line(aes(y = `Defacement`, group = 1, colour = "Defacement")) +
  144 + geom_point(aes(y = `Defacement`, group = 1, colour = "Defacement")) +
  145 +
  146 + geom_line(aes(y = `DNS`, group = 1, colour = "DNS")) +
  147 + geom_point(aes(y = `DNS`, group = 1, colour = "DNS")) +
  148 +
  149 + geom_line(aes(y = `Injection`, group = 1, colour = "Injection")) +
  150 + geom_point(aes(y = `Injection`, group = 1, colour = "Injection")) +
  151 +
  152 + geom_line(aes(y = `Malware`, group = 1, colour = "Malware")) +
  153 + geom_point(aes(y = `Malware`, group = 1, colour = "Malware")) +
  154 +
  155 + theme(plot.title = element_text(hjust = 0.5)) +
  156 + ggtitle("Attack type evolution") +
  157 + labs(colour = "Attack type") + xlab("Years") + ylab("Attacks")
  158 +
  159 + graph1
  160 +
  161 + }
  162 +
  163 +
  164 +GetAttackTypeTopEvolution <- function(Attacks){
  165 + Attacks.pre <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year, Attack.standar)
  166 + Attacks.pre <- as.data.frame(table(Attacks.pre$Year, Attacks.pre$Attack.standar))
  167 + Attacks.pre <- setNames(Attacks.pre, c("Year", "Attack", "Count"))
  168 + Attacks.pre <- Attacks.pre[Attacks.pre$Attack != "",]
  169 + Attacks.pre <- plyr::arrange(Attacks.pre, Year, Attack)
  170 +
  171 + b <- as.character(sort(unique(Attacks.pre$Attack)))
  172 +
  173 + Attacks.desg <- data.frame(Year = sort(unique(Attacks.pre$Year)))
  174 +
  175 + for (i in 1:length(b)) {
  176 + Attacks.desg <- cbind(Attacks.desg, Attacks.pre[Attacks.pre$Attack == b[i],3])
  177 + }
  178 +
  179 + Attacks.desg <- setNames(Attacks.desg, c("Year", b))
  180 +
  181 + Attacks.desg <- select(Attacks.desg, Year, `Account Hijacking`, `DDoS`, `Defacement`, `DNS`, `Injection`, `Malware`)
  182 +
  183 + graph1 <- ggplot(data = Attacks.desg,
  184 + aes(x = Year, y = `DDoS`, group = 1)) +
  185 +
  186 + geom_line() +
  187 + geom_point() +
  188 +
  189 + theme(plot.title = element_text(hjust = 0.5)) +
  190 + ggtitle("DDoS") +
  191 + xlab("Years") + ylab("Attacks")+
  192 + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
  193 +
  194 + graph2 <- ggplot(data = Attacks.desg,
  195 + aes(x = Year, y = `Defacement`, group = 1)) +
  196 +
  197 + geom_line() +
  198 + geom_point() +
  199 +
  200 + theme(plot.title = element_text(hjust = 0.5)) +
  201 + ggtitle("Defacement") +
  202 + xlab("Years") + ylab("Attacks")+
  203 + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
  204 +
  205 + graph3 <- ggplot(data = Attacks.desg,
  206 + aes(x = Year, y = `Injection`, group = 1)) +
  207 +
  208 + geom_line() +
  209 + geom_point() +
  210 +
  211 + theme(plot.title = element_text(hjust = 0.5)) +
  212 + ggtitle("Injection") +
  213 + xlab("Years") + ylab("Attacks")+
  214 + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
  215 +
  216 + graph4 <- ggplot(data = Attacks.desg,
  217 + aes(x = Year, y = `Account Hijacking`, group = 1)) +
  218 +
  219 + geom_line() +
  220 + geom_point() +
  221 +
  222 + theme(plot.title = element_text(hjust = 0.5)) +
  223 + ggtitle("Account Hijacking") +
  224 + xlab("Years") + ylab("Attacks")+
  225 + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
  226 +
  227 + graph5 <- ggplot(data = Attacks.desg,
  228 + aes(x = Year, y = `Malware`, group = 1)) +
  229 +
  230 + geom_line() +
  231 + geom_point() +
  232 +
  233 + theme(plot.title = element_text(hjust = 0.5)) +
  234 + ggtitle("Malware") +
  235 + xlab("Years") + ylab("Attacks")+
  236 + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
  237 +
  238 + graph6 <- ggplot(data = Attacks.desg,
  239 + aes(x = Year, y = `DNS`, group = 1)) +
  240 +
  241 + geom_line() +
  242 + geom_point() +
  243 +
  244 + theme(plot.title = element_text(hjust = 0.5)) +
  245 + ggtitle("DNS") +
  246 + xlab("Years") + ylab("Attacks")+
  247 + stat_smooth(method = "lm", se = FALSE, aes(outfit=fit<<-..y..))
  248 +
  249 +
  250 + list(graph1, graph2, graph3, graph4, graph5, graph6)
  251 +
  252 +}
  253 +
  254 +
  255 +
81 256  
82 257  
83 258 GetBaseCertsGraph <- function(Cert_PerCountry, year){
... ...
ISO27001effectiveness/Report.Rmd
  View file @a947c3a
... ... @@ -10,11 +10,13 @@ output:
10 10 library(knitr)
11 11  
12 12 knitr::opts_chunk$set(echo = FALSE)
  13 +knitr::opts_chunk$set(warning = FALSE)
  14 +knitr::opts_chunk$set(message = FALSE)
13 15  
14 16 devtools::load_all(".")
15 17 ```
16 18  
17   -```{r include=TRUE, warning=FALSE, results='hide', message=FALSE, cache=TRUE}
  19 +```{r include=TRUE, results='hide', cache=TRUE}
18 20 source("./Main.R")
19 21 ```
20 22  
... ... @@ -80,16 +82,16 @@ Para llegar hasta el fueron necesarios procesos algo más complejos, pero se res
80 82  
81 83 En primer lugar, para saber si este estudio tiene sentido, se observará la evolución temporal tanto de los ciberataques reportados como de las certificaciones realizadas en busca de patrones que puedan indicar una relación entre ambas evoluciones.
82 84  
83   -```{r fig.width=4.5, fig.height=4,echo=FALSE,out.extra='style="float:left"'}
  85 +```{r fig.width=4.5, fig.height=4,out.extra='style="float:left"'}
84 86 ISO27001effectiveness::GetCertsEvolution(Cert_PerCountry)
85 87 ```
86   -```{r fig.width=4.5, fig.height=4,echo=FALSE}
  88 +```{r fig.width=4.5, fig.height=4}
87 89 ISO27001effectiveness::GetAttacksEvolution(Attacks)
88 90 ```
89 91  
90 92 Como se puede observar en las gráficas, el número de empresas que obtienen la certificación crece anualmente, mientras que el numero de ataques es un poco más inestable. Del año 2013 al 2014 el número de ataques se reduce drásticamente, 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 explicación plausible es que justo en el año 2013 se produjo una revisión de la certificación, produciendose la ISO 27001:2013 que sustituyó a su predecesora 27001:2005, los cambios realizados pueden consultarse en la [web oficial](http://www.iso.org/iso/home/store/catalogue_tc/catalogue_detail.htm?csnumber=54534). Para indagar un poco sobre este tema a continuación se representa la evolución de los ataques mes a mes, al aumentar la precisión en el eje X se puede observar mejor la tendencia y abstraernos un poco de esos errores humanos.
91 93  
92   -```{r fig.width=9, fig.height=4,echo=FALSE}
  94 +```{r fig.width=9, fig.height=4}
93 95 ISO27001effectiveness::GetAttacksMonthEvolution(Attacks)
94 96 ```
95 97  
... ... @@ -98,9 +100,68 @@ También se podría relacionar la evolución de los ataques con la evolución de
98 100  
99 101 ### Evolución por tipo de ataque
100 102  
  103 +Podría darse el caso de que la certificación tenga uan efectividad mayor contra ciertas técnicas de ataque y que dicha efectividad se camufle entre el resto de técnicas, 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 tendrá que centrar en una pequeña parte de ellos, los más empleados. Para llevar a cabo esta elección se representan a continuación aquellos que representan al menos un 1% del total de ataques producidos.
101 104  
  105 +```{r fig.width=9, fig.height=5}
  106 +ISO27001effectiveness::GetAttackTypePie(Attacks)
  107 +```
  108 +
  109 +Como se puede observar en el gráfico anterior, la mayor parte de los ataques registrados en nuestra fuente de datos emplean las siguientes técnicas, que serán las estudiadas a continuación:
  110 +
  111 +* Injection: Cualquier tipo de inyeccion ya sea de código, SQL, etc
  112 +
  113 +* Defacement: Consiste básicamente en modificar la apariencia de una página web.
  114 +
  115 +* DDoS: Trata de saturar un servicio mediante miles de conexiones para evitar que los usuarios legítimos puedan acceder con normalidad.
  116 +
  117 +* Account Hijacking: Con lo que obtienen datos o credenciales de cuentas ajenas.
  118 +
  119 +* Malware: Programas que se ejecutan en el sistema victima para llevar a cabo actividades maliciosas.
  120 +
  121 +* DNS: Ataques basados en los servidores DNS de la victima, como el poisoning, que consigue retornar las IPs que no debería a cada petición.
  122 +
  123 +```{r fig.width=9, fig.height=4}
  124 +ISO27001effectiveness::GetAttackTypeEvolution(Attacks)
  125 +```
  126 +
  127 +Como podemos observar en la evolución temporal reflejada en el gráfico, existen ciertas técnicas 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 continuación.
  128 +
  129 +```{r fig.width=3, fig.height=3,out.extra='style="float:left"'}
  130 +top <- ISO27001effectiveness::GetAttackTypeTopEvolution(Attacks)
  131 +top[[3]]
  132 +slope3 <- (fit[5] - fit[1]) / 4
  133 +```
  134 +```{r fig.width=3, fig.height=3,out.extra='style="float:left"'}
  135 +top[[1]]
  136 +slope1 <- (fit[5] - fit[1]) / 4
  137 +```
  138 +```{r fig.width=3, fig.height=3}
  139 +top[[2]]
  140 +slope2 <- (fit[5] - fit[1]) / 4
  141 +```
  142 +```{r fig.width=3, fig.height=3,out.extra='style="float:left"'}
  143 +top[[6]]
  144 +slope6 <- (fit[5] - fit[1]) / 4
  145 +```
  146 +```{r fig.width=3, fig.height=3,out.extra='style="float:left"'}
  147 +top[[4]]
  148 +slope4 <- (fit[5] - fit[1]) / 4
  149 +```
  150 +```{r fig.width=3, fig.height=3}
  151 +top[[5]]
  152 +slope5 <- (fit[5] - fit[1]) / 4
  153 +```
  154 +
  155 +Los tipos de ataque están ordenados por la pendiente de su tendencia, de menor a mayor, para reflejar cuáles están descendiendo más rápido y por lo tanto cuáles podrían reflejar mejor el aumento de certificaciones ISO27001 expedidas.
  156 +
  157 +Tanto _Malware_ como _Account Hijacking_ tienen una tendencia con pendiente positiva, `r slope4` y `r slope5` respectivamente, por lo que quedan descartadas para este estudio.
  158 +
  159 +La tendencia con una mayor pendiente negativa es _Injection_ (`r slope3`) y junto con _DDoS_ (`r slope1`), que también tiene una tendencia llamativa, pueden ser los que estén mas relacionados con la efectividad de la certificación ISO 27001.
  160 +
  161 +Por otro lado tenemos _DNS_ cuya tendencia también tiene una pendiente negativa (`r slope6`), pero es muy moderada y además podemos observar que los casos reportados en nuestra fuente de datos son mas bien escasos.
102 162  
103 163  
  164 +### Evolución por tipo de ataque
104 165  
105 166 De los datos mostrados se pueden hacer diferentes observaciones:
106 167  
... ...
ISO27001effectiveness/Report.html
  View file @a947c3a
... ... @@ -347,6 +347,24 @@ $(document).ready(function () {
347 347 </div>
348 348 <div id="evolucion-por-tipo-de-ataque" class="section level3">
349 349 <h3>Evolución por tipo de ataque</h3>
  350 +<p>Podría darse el caso de que la certificación tenga uan efectividad mayor contra ciertas técnicas de ataque y que dicha efectividad se camufle entre el resto de técnicas, 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 tendrá que centrar en una pequeña parte de ellos, los más empleados. Para llevar a cabo esta elección se representan a continuación aquellos que representan al menos un 1% del total de ataques producidos.</p>
  351 +<p><img 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" width="864" /></p>
  352 +<p>Como se puede observar en el gráfico anterior, la mayor parte de los ataques registrados en nuestra fuente de datos emplean las siguientes técnicas, que serán las estudiadas a continuación:</p>
  353 +<ul>
  354 +<li><p>Injection: Cualquier tipo de inyeccion ya sea de código, SQL, etc</p></li>
  355 +<li><p>Defacement: Consiste básicamente en modificar la apariencia de una página web.</p></li>
  356 +<li><p>DDoS: Trata de saturar un servicio mediante miles de conexiones para evitar que los usuarios legítimos puedan acceder con normalidad.</p></li>
  357 +<li><p>Account Hijacking: Con lo que obtienen datos o credenciales de cuentas ajenas.</p></li>
  358 +<li><p>Malware: Programas que se ejecutan en el sistema victima para llevar a cabo actividades maliciosas.</p></li>
  359 +<li><p>DNS: Ataques basados en los servidores DNS de la victima, como el poisoning, que consigue retornar las IPs que no debería a cada petición.</p></li>
  360 +</ul>
  361 +<p><img 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" width="864" /></p>
  362 +<p>Como podemos observar en la evolución temporal reflejada en el gráfico, existen ciertas técnicas 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 continuación.</p>
  363 +<p><img 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" 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" 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" 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" 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" 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" width="288" /></p>
  364 +<p>Los tipos de ataque están ordenados por la pendiente de su tendencia, de menor a mayor, para reflejar cuáles están descendiendo más rápido y por lo tanto cuáles podrían reflejar mejor el aumento de certificaciones ISO27001 expedidas.</p>
  365 +<p>Tanto <em>Malware</em> como <em>Account Hijacking</em> tienen una tendencia con pendiente positiva, 10.1 y 20.2 respectivamente, por lo que quedan descartadas para este estudio.</p>
  366 +<p>La tendencia con una mayor pendiente negativa es <em>Injection</em> (-173.5) y junto con <em>DDoS</em> (-49.3), que también tiene una tendencia llamativa, pueden ser los que estén mas relacionados con la efectividad de la certificación ISO 27001.</p>
  367 +<p>Por otro lado tenemos <em>DNS</em> cuya tendencia también tiene una pendiente negativa (-3.8), pero es muy moderada y además podemos observar que los casos reportados en nuestra fuente de datos son mas bien escasos.</p>
350 368 <p>De los datos mostrados se pueden hacer diferentes observaciones:</p>
351 369 <ul>
352 370 <li>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.</li>
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ISO27001effectiveness/data/hackmageddon/AttackTypeConfig.csv
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... ... @@ -2,22 +2,22 @@ HeartBleed Vulnerability;Vulnerability
2 2 Account Hijacking;Account Hijacking
3 3 Account Hijacking (via a third party vendor);Account Hijacking
4 4 Account Hijacking via Telegram Vulnerability;Account Hijacking
5   -Apache Vulnerability;Web server
6   -ATM Malware;ATM
7   -Backdoor via Joomla plugin vulnerability;Web server
8   -Banking Trojan;ATM
  5 +Apache Vulnerability;Vulnerability
  6 +ATM Malware;Vulnerability
  7 +Backdoor via Joomla plugin vulnerability;Vulnerability
  8 +Banking Trojan;Vulnerability
9 9 BGP Poisoning;Network
10   -Bitcoin Software Vulnerability;Bitcoin
11   -Bitcoin Vulnerability;Bitcoin
  10 +Bitcoin Software Vulnerability;Vulnerability
  11 +Bitcoin Vulnerability;Vulnerability
12 12 Botnet;Bots
13 13 Bots;Bots
14 14 Brute Force;Brute Force
15 15 Brute Force?;Brute Force
16   -CMS Vulnerability;Web server
  16 +CMS Vulnerability;Vulnerability
17 17 Code Backdoor;Vulnerability
18 18 Command Injection;Injection
19 19 Credential Stuffing;Account Hijacking
20   -Cripto Currency Vulnerability;Bitcoin
  20 +Cripto Currency Vulnerability;Vulnerability
21 21 DDoS;DDoS
22 22 Defacement;Defacement
23 23 Defacement;Defacement
... ... @@ -31,10 +31,10 @@ DNS Hijacking;DNS
31 31 DNS Poisoning;DNS
32 32 DoS;DDoS
33 33 Electronic Device;
34   -Exploited Undisclosed Vulnerability;0 day
35   -Fraudulent Transaction;Bitcoin
36   -FTP Vulnerability;FTP
37   -GPS Spoofing;GPS
  34 +Exploited Undisclosed Vulnerability;Zero day
  35 +Fraudulent Transaction;Vulnerability
  36 +FTP Vulnerability;Vulnerability
  37 +GPS Spoofing;Vulnerability
38 38 Heartbleed Vulnerability Exploit;Vulnerability
39 39 HTML redirection;Redirection
40 40 Iframe Injection;Injection
... ... @@ -42,10 +42,10 @@ iFrame Redirection;Injection
42 42 ImageMagick Vulnerability;Vulnerability
43 43 injected iFrame;Injection
44 44 Jamming;DDoS
45   -Joomla Vulnerability;Web server
  45 +Joomla Vulnerability;Vulnerability
46 46 LFI;Injection
47 47 LogMeIn Vulnerability;Vulnerability
48   -Magento Vulnerability;Web server
  48 +Magento Vulnerability;Vulnerability
49 49 Malicious App;Malware
50 50 Malicious Code Injection;Injection
51 51 Malicious iFrame;Malware
... ... @@ -63,15 +63,15 @@ Malware (Win32/Albatook);Malware
63 63 MITM;MITM
64 64 Mobile Interference;Mobile
65 65 Mobile Malware;Mobile
66   -Mongo DB Vulnerability;DB
67   -Outdated Joomla;Web server
68   -PHP Backdoor;Web server
  66 +Mongo DB Vulnerability;Vulnerability
  67 +Outdated Joomla;Vulnerability
  68 +PHP Backdoor;Vulnerability
69 69 POS Malware;Malware
70 70 PoS Malware;Malware
71 71 PoS Malware?;Malware
72 72 Random Number Generator Vulnerability;Vulnerability
73 73 RDP Vulnerability;Vulnerability
74   -Reverse ATM Attack;ATM
  74 +Reverse ATM Attack;Vulnerability
75 75 Road Sign Hacking;
76 76 Rootkit;Rootkit
77 77 SEO Poisoning;SEO Poisoning
... ... @@ -90,19 +90,19 @@ Targeted Attack;
90 90 Unauthorized Access;Account Hijacking
91 91 Unauthorized Code;Account Hijacking
92 92 Unautorized Access;Account Hijacking
93   -Undisclosed Vulnerability;0 day
94   -Undisclosed Vulnerability in the e-commerce platform;0 day
  93 +Undisclosed Vulnerability;Zero day
  94 +Undisclosed Vulnerability in the e-commerce platform;Zero day
95 95 Unknown;
96 96 Unknown;
97 97 Unknown (Payment Card Processing System Compromised);
98 98 Unknown (Unspecified Vulnerability);Vulnerability
99   -Unknown 0-day;0 day
  99 +Unknown 0-day;Zero day
100 100 Unknown Malware;Malware
101 101 Unknown Vulnerability;Vulnerability
102 102 Unspecified Bug;Vulnerability
103 103 vBulletin Vulnerability;Vulnerability
104   -Website Compromising;Web server
  104 +Website Compromising;Vulnerability
105 105 Wordpress Vulnerability;Vulnerability
106 106 XSS;Injection
107   -Zimbra 0-day;0 day
  107 +Zimbra 0-day;Zero day
108 108 Zimbra Vulnerability;Vulnerability
... ...