Convenience functions for exploratory analysis on VERIS database (http://veriscommunity.net).
Small helper functions for working with the data frame objects from the VERIS Community Database (VCDB), typically converted from JSON using the verisr package (or, if unavailable, from this my fork of this package). This package replicates in base R or dplyr many of the helper functions originally implemented in the verisr package by Jay Jacobs.
The original package by Jay uses data.table code that is deprecated
and no longer works with recent versions of R. The author has stated his
desire to one day rewrite these functions in dplyr code but since effort
on that has been stagnant for a few years now this is a simple attempt
to recreate these helper functions in dplyr or base R code.
Install it from github and load the built-in dataset:
# install devtools from https://github.com/hadley/devtools
devtools::install_github("onlyphantom/verisr2")
library(verisr2)
data(vcdb)Inspecting the class of the data:
class(vcdb)## [1] "verisr" "data.frame"
Because the incidents are originally recorded in JSON, the transformed data is “wide” spanning across more than 2,430 variables as of this writing. The VERIS specification has intended for the data schema to be extended upon, and when expressed as a data frame, this wide format presents an opportunity for data analysis and exploratory exercises:
dim(vcdb)## [1] 8198 2436
Retrieve a list of variables (enumeration / factors) in the data frame from a specified “parent”:
getenum_stri(vcdb, "action.error.vector")[1:5]## [1] "action.error.vector.Carelessness"
## [2] "action.error.vector.Inadequate personnel"
## [3] "action.error.vector.Inadequate processes"
## [4] "action.error.vector.Inadequate technology"
## [5] "action.error.vector.Other"
The same function can also be performed with a vector of (character) strings instead of a single string value:
getenum_stri(vcdb, c("actor.internal.motive", "value_chain.money laundering.variety"))[8:12]## [1] "actor.internal.motive.NA"
## [2] "actor.internal.motive.Other"
## [3] "actor.internal.motive.Secondary"
## [4] "actor.internal.motive.Unknown"
## [5] "value_chain.money laundering.variety.Bank"
To get a frequency table, use getenum_tbl:
getenum_tbl(vcdb, c("action", "asset.variety"))## action.Malware action.Hacking action.Social
## 678 2185 554
## action.Physical action.Misuse action.Error
## 1565 1675 2374
## action.Environmental action.Unknown asset.variety.Server
## 8 237 3819
## asset.variety.Network asset.variety.User Dev asset.variety.Media
## 157 1478 2207
## asset.variety.Person asset.variety.Kiosk/Term asset.variety.Unknown
## 606 345 646
## asset.variety.Embedded
## 2
We can use getenum_df function to get both the count and the
proportion of assets where data loss has occured. This replicates the
original functionality from jayjacobs and vz-risk’s version but uses
base R in its underlying function:
getenum_df(vcdb, "asset.variety")## enum x n freq
## 1 Server 3819 8188 0.46641
## 2 Media 2207 8188 0.26954
## 3 User Dev 1478 8188 0.18051
## 4 Person 606 8188 0.07401
## 5 Kiosk/Term 345 8188 0.04213
## 6 Network 157 8188 0.01917
## 7 Embedded 2 8188 0.00024
## 8 Unknown 646 NA NA
Similarly, we can pass in a vector of two characters to the function, which will count the number of incidents across the two enumerations:
getenum_df(vcdb, c("action", "asset.variety"))## # A tibble: 64 x 3
## action asset.variety x
## <chr> <chr> <int>
## 1 Hacking Server 1890
## 2 Error Media 1395
## 3 Misuse Server 1030
## 4 Physical User Dev 706
## 5 Error Server 662
## 6 Social Person 554
## 7 Physical Media 478
## 8 Malware Server 453
## 9 Social Server 375
## 10 Malware User Dev 371
## # … with 54 more rows
enum2grid replicates the plotting function in jayjacobs version, and
will work with all recent versions of R:
enum2grid(vcdb, c("asset.variety", "actor.external.variety"))
Another example:
enum2grid(vcdb, c("action", "asset.variety"))
importveris() is a thin wrapper over the json2veris() function. In
later versions of vcdb incidents, the original function may result in a
dataframe where one or more of its variables is another level of nested
list object(s). This function eliminates these columns, so they’re in a
more ready state for most data analysis tasks:
vcdb_small <- importveris("~/Datasets/vcdb_small/")## [1] "veris dimensions"
## [1] 0 2437
## named integer(0)
## named integer(0)
collapse_vcdb() takes a vcdb data frame and turns it into a more
compact data frame that conforms to the “tidyverse” specifications. New
features are created from the original data, using values that best
represent each enumeration. An oversimplified diagram explaining this
process is as follow: 
tidy_vcdb <- collapse_vcdb(vcdb)
str(tidy_vcdb)## Loading verisr2
## 'data.frame': 8198 obs. of 15 variables:
## $ action : Factor w/ 9 levels "Environmental",..: 5 7 2 3 2 2 3 3 7 6 ...
## $ action.environmental.notes : chr NA NA NA NA ...
## $ action.environmental.variety: Factor w/ 4 levels "Fire","Humidity",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ action.error.notes : chr NA NA NA NA ...
## $ action.error.variety : Factor w/ 18 levels "Capacity shortage",..: 18 18 6 18 10 10 18 18 18 18 ...
## $ action.error.vector : Factor w/ 8 levels "Carelessness",..: 8 8 8 8 1 1 8 8 8 8 ...
## $ action.hacking.cve : chr NA NA NA NA ...
## $ action.hacking.notes : chr NA NA NA NA ...
## $ action.hacking.result : Factor w/ 5 levels "Elevate","Exfiltrate",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ action.hacking.variety : Factor w/ 8 levels "Brute force",..: 6 6 6 5 6 6 6 6 6 6 ...
## $ action.hacking.vector : Factor w/ 11 levels "Backdoor or C2",..: 9 9 9 11 9 9 11 11 9 9 ...
## $ action.malware.cve : chr NA NA NA NA ...
## $ action.malware.name : chr NA NA NA NA ...
## $ action.malware.notes : chr NA NA NA NA ...
## $ action.malware.result : Factor w/ 5 levels "Elevate","Exfiltrate",..: 5 5 5 5 5 5 5 5 5 5 ...
Note that the new data frame is a lot more compact, with 175 instead of the original 2,430+ variables:
dim(tidy_vcdb)## [1] 8198 175
Where the original VCDB has a shape that resembles a “sparse matrix”, this new “tidy” data frame now has most variables as factor and numeric values. Obviously some loss of fidelity happens (a 2500-column data matrix where most values are 0 are reduced to 175-column where only the representative value is stored in each dimension / enumeration):
##
## c("ordered", "factor") character factor
## 1 59 105
## numeric
## 10
The data (both the originalo vcdb and its tidy variant) also works
well with the rest of tidyverse. An example is to use the data in
conjunction with dplyr and ggplot2:
vcdb %>%
group_by(attribute.confidentiality.data_disclosure.Yes) %>%
dplyr::count(timeline.incident.year) %>%
ungroup() %>%
mutate(
breach = ifelse(attribute.confidentiality.data_disclosure.Yes,
"Breach", "Incident")
) %>% filter(
timeline.incident.year > 2000
) %>% ggplot(aes(x=timeline.incident.year, y=n, group=breach)) +
geom_col(aes(fill=breach), position = "dodge") +
scale_x_continuous(expand=c(0,0), breaks=seq(2000, 2018, 3)) +
scale_y_continuous(expand=c(0,0)) +
scale_fill_brewer(palette = 11) +
labs(title="VCDB Confidentiality Breaches", caption="Confidentiality breaches where data disclosure occured"
Existing functions in this package already allow us with country-level inspection pretty effortlessly:
summary(tidy_vcdb$actor.external.country, maxsum=8)## Unknown US Multiple RU CN PK SY (Other)
## 7320 220 180 110 43 40 36 249
We can use the collapsed dataframe (result of collapse_vcdb) to
perform our inspection:
usvictim <- subset(tidy_vcdb, victim.country=="US")
head(usvictim$notes)## [1] "lincoln financial securities Corporation is a subsidiary of Lincoln national Corporation"
## [2] "Limited information provided and there have been no follow-up articles."
## [3] "HHS Breach Tool"
## [4] "The Sentry email mistake was modeled seperately. "
## [5] "I can't discern who was breached here. It says DoD. But it also says satellite manufacturer. I'm assuming the latter working for DoD"
## [6] "The final record count was obtained from the HHS Breachtool record for this incident. It was listed under the partner rather than Owensboro, strangely."
As of version 0.4.0, the new function involving_country() allows us to
query even more effectively for all incidents where a specified country
is involved:
us <- involving_country(data = vcdb, "US")
head(us$discovery_notes)## [1] "In June 2014, Epic Systems Corp. in Verona received an email that no software company can ignore: Employees of a company working for one of its customers had gained unauthorized access to a restricted website and may have stolen documents that contained trade secrets."
## [2] "actor was arrested on drug charge and they found skimmer and cards, notified employer."
## [3] "Committed ID fraud against her own maid of honor and used her real phone number when establishing fraudulent lines of credit."
## [4] "the FBI was investigating after 2.5GB of data taken from its servers was dumped online and swiftly shared on social media. The union's national site, fop.net, remained offline on Thursday evening"
## [5] "Engel said, though, that the university didn’t confirm that data had been breached or learn about its apparent scope until external investigators notified officials July 31, 2018."
## [6] "We have disabled the malware and have reconfigured our point-of-sale and payment card processing systems to enhance the security of these systems"
By default, involving_country returns all columns of every incident
involving that country. If we would like to retrieve only the columns
where one or more notes are present (discovery notes, incident notes,
impact notes, actor notes etc - more than 30 of such columns), then set
notes_only to TRUE. The function helpfully drops any incident (rows)
where no notes are present:
# only notes-type columns
us_small <- involving_country(vcdb, "US", notes_only=TRUE)
dim(us_small)## [1] 1928 30
-
A big appreciation to Jay Jacobs for the original
verisrpackage. While it hasn’t receive any updates in recent years, the project has been a tremendous help and starting point. -
Thanks to the Verizon RISK Team and the community behind The VERIS Community Database
-
Thanks to Hadley Wickham, the contributors and all maintainers of packages used in this project
The project is licensed under GPL-2. Please feel free to fork, submit pull requests or open issues.