This R Notebook is the complement to my blog post Network Visualization of Breached Internet Services Using HaveIBeenPwned Data.

This notebook is licensed under the MIT License. If you use the code or data visualization designs contained within this notebook, it would be greatly appreciated if proper attribution is given back to this notebook and/or myself. Thanks! :)

1 Setup

Setup the R packages.

# must install ggnetwork using from source to avoid ggplot2 2.2.0 issue
# install.packages("ggnetwork", type="source")
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
library(readr)
library(igraph)

Attaching package: ‘igraph’

The following objects are masked from ‘package:dplyr’:

    %>%, as_data_frame, groups, union

The following objects are masked from ‘package:stats’:

    decompose, spectrum

The following object is masked from ‘package:base’:

    union
library(intergraph)
library(sna)
Loading required package: statnet.common
Loading required package: network
network: Classes for Relational Data
Version 1.13.0 created on 2015-08-31.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
                    Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
                    Martina Morris, University of Washington
                    Skye Bender-deMoll, University of Washington
 For citation information, type citation("network").
 Type help("network-package") to get started.


Attaching package: ‘network’

The following objects are masked from ‘package:igraph’:

    %c%, %s%, add.edges, add.vertices, delete.edges, delete.vertices,
    get.edge.attribute, get.edges, get.vertex.attribute,
    is.bipartite, is.directed, list.edge.attributes,
    list.vertex.attributes, set.edge.attribute, set.vertex.attribute

sna: Tools for Social Network Analysis
Version 2.4 created on 2016-07-23.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
 For citation information, type citation("sna").
 Type help(package="sna") to get started.


Attaching package: ‘sna’

The following objects are masked from ‘package:igraph’:

    betweenness, bonpow, closeness, components, degree, dyad.census,
    evcent, hierarchy, is.connected, neighborhood, triad.census
library(ggplot2)
library(ggnetwork)
library(plotly)

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following objects are masked from ‘package:igraph’:

    %>%, groups

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout
library(htmlwidgets)
library(RJSONIO)
sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS Sierra 10.12.2

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] RJSONIO_1.3-0        htmlwidgets_0.8      plotly_4.5.6        
 [4] ggnetwork_0.5.1      ggplot2_2.2.0        sna_2.4             
 [7] network_1.13.0       statnet.common_3.3.0 intergraph_2.0-2    
[10] igraph_1.0.1         readr_1.0.0          dplyr_0.5.0         

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.8       knitr_1.15.1      magrittr_1.5      sparklyr_0.5.1   
 [5] munsell_0.4.3     viridisLite_0.1.3 colorspace_1.3-2  R6_2.2.0         
 [9] plyr_1.8.4        stringr_1.1.0     httr_1.2.1        tools_3.3.2      
[13] grid_3.3.2        gtable_0.2.0      DBI_0.5-1         htmltools_0.3.5  
[17] lazyeval_0.2.0    yaml_2.1.14       assertthat_0.1    rprojroot_1.1    
[21] digest_0.6.10     tibble_1.2        tidyr_0.6.0       purrr_0.2.2      
[25] base64enc_0.1-3   ggrepel_0.6.5     evaluate_0.10     rmarkdown_1.3    
[29] stringi_1.1.2     scales_0.4.1      backports_1.0.4   jsonlite_1.1     
df <- read_csv('hibp_edges.csv')
Parsed with column specification:
cols(
  Source = col_character(),
  Target = col_character(),
  Weight = col_integer()
)
df %>% arrange(desc(Weight)) %>% head() %>% print()

There are 10816 edges.

df_totals <- read_csv('hibp_services.csv')
Parsed with column specification:
cols(
  Service = col_character(),
  Total = col_integer()
)
df_totals %>% arrange(desc(Total)) %>% head() %>% print()

There are 1,768,628,867 total records in the dataset. (expected value should ber # of records - # of records from sensitive breaches: about 1,989,141,353 - 221M = 1,768,141,353))

Combine the two dataframes together; this lets us filter the dataframes using vector operations.

df_merged <- df %>% left_join(df_totals, by = c("Source" = "Service")) %>% left_join(df_totals, by = c("Target" = "Service"))
df_merged %>% arrange(desc(Weight)) %>% tail() %>% print()

Keep only edges with 1% of the proportion in both of the services it connects.

df_merged <- df_merged %>% filter(Weight >= Total.x * 0.01,
                                  Weight >= Total.y * 0.01) %>%
                select(Source, Target, Weight)
df_merged %>% arrange(desc(Weight)) %>% tail() %>% print()

1.1 Breach Data

Get breach data from HaveIBeenPwned for better tooltips.

# http://stackoverflow.com/questions/16947643/getting-imported-json-data-into-a-data-frame-in-r
df_hibp <- fromJSON(content = "https://haveibeenpwned.com/api/v2/breaches")
df_hibp <- do.call("rbind", lapply(df_hibp, as.data.frame))
df_hibp <- df_hibp %>% select(Title, Name, Domain, BreachDate, PwnCount) %>% unique()
df_hibp %>% head() %>% print()
df_hibp <- df_hibp %>% mutate(text = paste(Title, paste(format(PwnCount, big.mark=",", trim=T), "Pwns"), format(as.Date(BreachDate), "%b %d, %Y"), sep="<br>"))
df_hibp %>% select(text) %>% head() %>% print()

Build the graph network.

net <- graph.data.frame(df_merged, directed = FALSE)
V(net)$degree <- centralization.degree(net)$res
V(net)$weighted_degree <- graph.strength(net, weights=V(net)$Weight)
V(net)$text <- df_hibp$text[match(V(net)$name, df_hibp$Name)]
net
IGRAPH UN-- 98 316 -- 
+ attr: name (v/c), degree (v/n), weighted_degree (v/n), text (v/c),
| Weight (e/n)
+ edges (vertex names):
 [1] WarInc         --WildStar      Dropbox        --iMesh        
 [3] MajorGeeks     --Malwarebytes  AndroidForums  --Plex         
 [5] GamerzPlanet   --NextGenUpdate VBulletin      --WHMCS        
 [7] Aipai          --NetEase       Nival          --WildStar     
 [9] Avast          --BlackHatWorld MoDaCo         --Xbox-Scene   
[11] CivilOnline    --Tianya        Nihonomaru     --iPmart       
[13] Nihonomaru     --WIIUISO       Nival          --XSplit       
+ ... omitted several edges
V(net)$group <- membership(cluster_walktrap(net, weights=E(net)$Weight))
V(net)$centrality <- eigen_centrality(net, weights=E(net)$Weight)$vector

Build the ggnetwork.

# ggnetwork sets default nodes randomly; set seed for reproducibility
set.seed(123)
df_net <- ggnetwork(net, layout = "fruchtermanreingold", weights="Weight", niter=50000)
df_net %>% head() %>% print()
plot <- ggplot(df_net, aes(x = x, y = y, xend = xend, yend = yend)) +
    geom_edges(aes(alpha = Weight), size=0.25) +
    geom_nodes(aes(fill = as.factor(group), size = degree), shape = 21, color = "#1a1a1a", stroke=0.2) +
    ggtitle("Network Graph of Breaches from HaveIBeenPwned Database (by @minimaxir)") +
    geom_nodelabel_repel(aes(color = as.factor(group), label = vertex.names),
                          family = "Open Sans Condensed Bold", size=1.5, box.padding = unit(0.05, "lines"),
                          label.padding= unit(0.1, "lines"), segment.size=0.1, label.size=0.2) +
    scale_alpha_continuous(range=c(0.1,1)) +
    theme_blank() +
    guides(size=FALSE, color=FALSE, alpha=FALSE, fill=FALSE) +
    theme(plot.title = element_text(family="Source Sans Pro", size=8, hjust=0.5),
            legend.title = element_text(family="Source Sans Pro"),
            legend.text = element_text(family="Source Sans Pro"))
Ignoring unknown parameters: segment.color
plot

ggsave("hibp.png", plot, "png", width=6, height=4.5, dpi=300)

1.2 Plotly

Make a second graph for more fine-tuned parameters. (and removing geom_nodelabel_repel)

In Plotly, alpha must be a factor variable due to http://stackoverflow.com/a/37498249. This introduces other bugs, so it was converted to a static value.

plot <- ggplot(df_net, aes(x = x, y = y, xend = xend, yend = yend)) +
    geom_edges(size=0.2, alpha=0.2) +
    geom_nodes(aes(fill = as.factor(group), size = degree, text = text), shape = 21, color = "#1a1a1a", stroke=0.1, text=text) +
    ggtitle("Network Graph of Breaches from HaveIBeenPwned Database (by @minimaxir)") +
    scale_alpha_discrete(range=c(0,0.5)) +
    scale_size_continuous(range=c(2,6)) +
    theme_blank() +
    theme(plot.title = element_text(family="Source Sans Pro", size=10),
            legend.title = element_text(family="Source Sans Pro"),
            legend.text = element_text(family="Source Sans Pro"),
            legend.position="none")
Ignoring unknown parameters: textIgnoring unknown aesthetics: text
plot %>% ggplotly(tooltip="text") %>% toWebGL()
plot %>% ggplotly(tooltip="text", height=400) %>% toWebGL() %>% saveWidget("hibp-interactive.html", selfcontained=F, libdir="plotly")

2 LICENSE

The MIT License (MIT)

Copyright (c) 2016 Max Woolf

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

---
title: "Network Visualization of Breached Internet Services Using HaveIBeenPwned Data"
author: "Max Woolf (@minimaxir)"
date: "December 19th, 2016"
output:
  html_notebook:
    highlight: tango
    mathjax: null
    number_sections: yes
    theme: spacelab
    toc: yes
    toc_float: yes
---

This R Notebook is the complement to my blog post [Network Visualization of Breached Internet Services Using HaveIBeenPwned Data](http://minimaxir.com/2016/12/pwned-network/).

This notebook is licensed under the MIT License. If you use the code or data visualization designs contained within this notebook, it would be greatly appreciated if proper attribution is given back to this notebook and/or myself. Thanks! :)

# Setup

Setup the R packages.

```{r}

# must install ggnetwork using from source to avoid ggplot2 2.2.0 issue
# install.packages("ggnetwork", type="source")

library(dplyr)
library(readr)
library(igraph)
library(intergraph)
library(sna)
library(ggplot2)
library(ggnetwork)
library(plotly)
library(htmlwidgets)
library(RJSONIO)

sessionInfo()
```

```{r}
df <- read_csv('hibp_edges.csv')
df %>% arrange(desc(Weight)) %>% head() %>% print()
```

There are `r df %>% nrow()` edges.

```{r}
df_totals <- read_csv('hibp_services.csv')
df_totals %>% arrange(desc(Total)) %>% head() %>% print()
```

There are **`r df_totals %>% select(Total) %>% sum() %>% format(big.mark=",")`** total records in the dataset. ([expected value](https://www.troyhunt.com/heres-1-4-billion-records-from-have-i-been-pwned-for-you-to-analyse/) should ber # of records - # of records from sensitive breaches: about 1,989,141,353 - 221M = `r (1989141353 - 221*10^6) %>% format(big.mark=",")`))

Combine the two dataframes together; this lets us filter the dataframes using vector operations.

```{r}
df_merged <- df %>% left_join(df_totals, by = c("Source" = "Service")) %>% left_join(df_totals, by = c("Target" = "Service"))

df_merged %>% arrange(desc(Weight)) %>% tail() %>% print()
```

Keep only edges with 1% of the proportion in both of the services it connects.

```{r}
df_merged <- df_merged %>% filter(Weight >= Total.x * 0.01,
                                  Weight >= Total.y * 0.01) %>%
                select(Source, Target, Weight)

df_merged %>% arrange(desc(Weight)) %>% tail() %>% print()
```

## Breach Data

Get breach data from HaveIBeenPwned for better tooltips.

```{r}
# http://stackoverflow.com/questions/16947643/getting-imported-json-data-into-a-data-frame-in-r

df_hibp <- fromJSON(content = "https://haveibeenpwned.com/api/v2/breaches")
df_hibp <- do.call("rbind", lapply(df_hibp, as.data.frame))
df_hibp <- df_hibp %>% select(Title, Name, Domain, BreachDate, PwnCount) %>% unique()

df_hibp %>% head() %>% print()
```

```{r}
df_hibp <- df_hibp %>% mutate(text = paste(Title, paste(format(PwnCount, big.mark=",", trim=T), "Pwns"), format(as.Date(BreachDate), "%b %d, %Y"), sep="<br>"))

df_hibp %>% select(text) %>% head() %>% print()
```

Build the graph network.

```{r}
net <- graph.data.frame(df_merged, directed = FALSE)

V(net)$degree <- centralization.degree(net)$res
V(net)$weighted_degree <- graph.strength(net, weights=V(net)$Weight)
V(net)$text <- df_hibp$text[match(V(net)$name, df_hibp$Name)]

net
```

```{r}
V(net)$group <- membership(cluster_walktrap(net, weights=E(net)$Weight))
V(net)$centrality <- eigen_centrality(net, weights=E(net)$Weight)$vector
```

Build the ggnetwork.

```{r}
# ggnetwork sets default nodes randomly; set seed for reproducibility
set.seed(123)

df_net <- ggnetwork(net, layout = "fruchtermanreingold", weights="Weight", niter=50000)
df_net %>% head() %>% print()
```

```{r}
plot <- ggplot(df_net, aes(x = x, y = y, xend = xend, yend = yend)) +
    geom_edges(aes(alpha = Weight), size=0.25) +
    geom_nodes(aes(fill = as.factor(group), size = degree), shape = 21, color = "#1a1a1a", stroke=0.2) +
    ggtitle("Network Graph of Breaches from HaveIBeenPwned Database (by @minimaxir)") +
    geom_nodelabel_repel(aes(color = as.factor(group), label = vertex.names),
                          family = "Open Sans Condensed Bold", size=1.5, box.padding = unit(0.05, "lines"),
                          label.padding= unit(0.1, "lines"), segment.size=0.1, label.size=0.2) +
    scale_alpha_continuous(range=c(0.1,1)) +
    theme_blank() +
    guides(size=FALSE, color=FALSE, alpha=FALSE, fill=FALSE) +
    theme(plot.title = element_text(family="Source Sans Pro", size=8, hjust=0.5),
            legend.title = element_text(family="Source Sans Pro"),
            legend.text = element_text(family="Source Sans Pro"))

plot
```

```{r}
ggsave("hibp.png", plot, "png", width=6, height=4.5, dpi=300)
```

## Plotly

Make a second graph for more fine-tuned parameters. (and removing `geom_nodelabel_repel`)

In Plotly, `alpha` must be a factor variable due to http://stackoverflow.com/a/37498249. This introduces other bugs, so it was converted to a static value.

```{r}
plot <- ggplot(df_net, aes(x = x, y = y, xend = xend, yend = yend)) +
    geom_edges(size=0.2, alpha=0.2) +
    geom_nodes(aes(fill = as.factor(group), size = degree, text = text), shape = 21, color = "#1a1a1a", stroke=0.1, text=text) +
    ggtitle("Network Graph of Breaches from HaveIBeenPwned Database (by @minimaxir)") +
    scale_alpha_discrete(range=c(0,0.5)) +
    scale_size_continuous(range=c(2,6)) +
    theme_blank() +
    theme(plot.title = element_text(family="Source Sans Pro", size=10),
            legend.title = element_text(family="Source Sans Pro"),
            legend.text = element_text(family="Source Sans Pro"),
            legend.position="none")

plot %>% ggplotly(tooltip="text") %>% toWebGL()
```

```{r}
plot %>% ggplotly(tooltip="text", height=400) %>% toWebGL() %>% saveWidget("hibp-interactive.html", selfcontained=F, libdir="plotly")
```

# LICENSE

The MIT License (MIT)

Copyright (c) 2016 Max Woolf

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.