We’ve got some new and updated R packages that are (hopefully) helpful to security folks who are endeavouring to use R in their quest to find and prevent malicious activity. All packages now incorporate a testthat workflow and are fully roxygen-ized and present some best practices in R package development (a post on that very topic is pending).

We’ll start with the old and work our way to the new…

Changes to the resolv package

I’ve updated resolv for the newest Rcpp and for a better build on linux and OS X systems (still no Windows compatibiity). The package also includes vectorized versions of the core resolv_ functions. Here’s an example:

library(resolv)
library(data.table)
library(plyr)

# Read in the Alexa top 1 million list
alexa <- fread("data/top-1m.csv") # http://s3.amazonaws.com/alexa-static/top-1m.csv.zip
str(alexa)

## Classes ‘data.table’ and 'data.frame':  1000000 obs. of  2 variables:
##  $ V1: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ V2: chr  "google.com" "facebook.com" "youtube.com" "yahoo.com" ...
##  - attr(*, ".internal.selfref")=<externalptr>

# How many of the Alexa top *1,000* have spf records?
alexa.txt <- TXT(alexa[1:1000]$V2) # this takes a few seconds as it's not performed in parallel

# iterate over the results, testing for presence spf records in each 
table(sapply(alexa.txt, function(x) { grepl("spf", unlist(x[1]));  }))

## FALSE  TRUE 
##   487   513

Doing all 1M would take a short while, but it’d be an interesting experiment to run (then, analyze the records to see which services these sites trust with their mail sending).

Updates to the netintel package

The netintel package is back from the dead! (thanks to a helpful push by David Severski).

All core functions have been re-written and the package now uses httr and data.table in some places for better realiability and speed. Functions that take AS numbers as parameters automagically strip or add the AS prefix as needed. To remind or introduce you to some of the workings:

# continuing from the previous code

library(netintel)

# get the IP addresses of the top 10 alexa domains
alexa.a <- A(alexa[1:10]$V2)

# retrieve the AS information
origin <- BulkOrigin(as.character(unlist(alexa.a)))

##       AS              IP       BGP.Prefix CC Registry  Allocated                                                              AS.Name
## 1  15169   74.125.22.100   74.125.22.0/24 US     arin 2007-03-13                                              GOOGLE - Google Inc.,US
## 2  15169   74.125.22.101   74.125.22.0/24 US     arin 2007-03-13                                              GOOGLE - Google Inc.,US
## 3  15169   74.125.22.102   74.125.22.0/24 US     arin 2007-03-13                                              GOOGLE - Google Inc.,US
## 4  15169   74.125.22.113   74.125.22.0/24 US     arin 2007-03-13                                              GOOGLE - Google Inc.,US
## 5  15169   74.125.22.138   74.125.22.0/24 US     arin 2007-03-13                                              GOOGLE - Google Inc.,US
## 6  15169   74.125.22.139   74.125.22.0/24 US     arin 2007-03-13                                              GOOGLE - Google Inc.,US
## 7  32934  173.252.110.27  173.252.96.0/19 US     arin 2011-02-28                                         FACEBOOK - Facebook, Inc.,US
## 8  15169   64.233.171.91  64.233.171.0/24 US     arin 2003-08-18                                              GOOGLE - Google Inc.,US
## 9  15169   64.233.171.93  64.233.171.0/24 US     arin 2003-08-18                                              GOOGLE - Google Inc.,US
## 10 15169  64.233.171.136  64.233.171.0/24 US     arin 2003-08-18                                              GOOGLE - Google Inc.,US
## 11 15169  64.233.171.190  64.233.171.0/24 US     arin 2003-08-18                                              GOOGLE - Google Inc.,US
## 12 36646  98.138.253.109    98.138.0.0/16 US     arin 2007-12-07                                                 YAHOO-NE1 - Yahoo,US
## 13 26101   98.139.183.24  98.139.128.0/17 US     arin 2007-12-07                                                  YAHOO-3 - Yahoo!,US
## 14 36647   206.190.36.45  206.190.32.0/20 US     arin                                                            YAHOO-GQ1 - Yahoo,US
## 15  4808 123.125.114.144  123.125.64.0/18 CN    apnic 2007-01-29 CHINA169-BJ CNCGROUP IP network China169 Beijing Province Network,CN
## 16 23724  220.181.111.85  220.181.96.0/19 CN    apnic 2002-10-30      CHINANET-IDC-BJ-AP IDC, China Telecommunications Corporation,CN
## 17 23724  220.181.111.86  220.181.96.0/19 CN    apnic 2002-10-30      CHINANET-IDC-BJ-AP IDC, China Telecommunications Corporation,CN
## 18 14907  208.80.154.224  208.80.152.0/22 US     arin 2007-07-23                             WIKIMEDIA - Wikimedia Foundation Inc.,US
## 19 13414    199.16.156.6  199.16.156.0/22 US     arin 2010-07-09                                    TWITTER-NETWORK - Twitter Inc.,US
## 20 13414   199.16.156.70  199.16.156.0/22 US     arin 2010-07-09                                    TWITTER-NETWORK - Twitter Inc.,US
## 21 13414  199.16.156.102  199.16.156.0/22 US     arin 2010-07-09                                    TWITTER-NETWORK - Twitter Inc.,US
## 22 13414  199.16.156.198  199.16.156.0/22 US     arin 2010-07-09                                    TWITTER-NETWORK - Twitter Inc.,US
## 23  4837  125.39.240.113    125.36.0.0/14 CN    apnic 2005-12-30                      CHINA169-BACKBONE CNCGROUP China169 Backbone,CN
## 24 17623  163.177.65.160  163.177.65.0/24 CN    apnic 2011-03-30                          CNCGROUP-SZ China Unicom Shenzen network,CN
## 25 16509   72.21.194.212   72.21.192.0/19 US     arin 2004-12-30                                      AMAZON-02 - Amazon.com, Inc.,US
## 26 16509   72.21.215.232   72.21.192.0/19 US     arin 2004-12-30                                      AMAZON-02 - Amazon.com, Inc.,US
## 27 16509   176.32.98.166   176.32.96.0/21 IE  ripencc 2011-05-23                                      AMAZON-02 - Amazon.com, Inc.,US
## 28 16509  205.251.242.54 205.251.240.0/22 US     arin 2010-08-27                                      AMAZON-02 - Amazon.com, Inc.,US
## 29 37963   42.120.194.11    42.120.0.0/16 CN    apnic 2011-02-21     CNNIC-ALIBABA-CN-NET-AP Hangzhou Alibaba Advertising Co.,Ltd.,CN

You could then look up each peer and see how “connected” the top 10 are.

Introducing iptools

The iptools package is a set of tools for a working with IPv4 addresses. The aim is to provide functionality not presently available with any existing R package and to do so with as much speed as possible. To that end, many of the operations are written in Rcpp and require installation of the Boost libraries. A current, lofty goal is to mimic most of the functionality of the Python iptools module and make IP addresses first class R objects.

While resolv provides many helpful DNS functions, it is dependent upon the ldns library, which may not ever work well under Windows+Rcpp. The iptools package provides minimally featured functions for IPv4 PTR/A record lookups in an effort to (hopefully) make it usable under Windows.

The package also uses the v1 GeoLite MaxMind library to perform basic geolocation of a given IPv4 address. You must manually install both the maxmind library (brew install geoip on OS X, sudo apt-get install libgeoip-dev on Ubuntu) and the GeoLiteCity.dat http://geolite.maxmind.com/download/geoip/database/GeoLiteCity.dat.gz & GeoLiteASNum.dat http://geolite.maxmind.com/download/geoip/database/GeoLiteASNum.dat.gz files for the geolocation/ASN functions to work. If there’s interest in porting to the newer library/GeoLite2 format, I’ll consider updating the package.

The following functions are implemented:

Revolver-ish

• gethostbyaddr - Returns all PTR records associated with an IPv4 address
• gethostsbyaddr - Vectorized version of gethostbyaddr
• gethostbyname - Returns all A records associated with a hostname
• gethostsbyname - Vectorized version of gethostbyname

IP int/string conversion

• ip2long - Character (dotted-decimal) IPv4 Address Conversion to long integer
• long2ip - Intger IPv4 Address Conversion to Character

Validation

• validateIP - Validate IPv4 addresses in dotted-decimal notation
• validateCIDR - Validate IPv4 CIDRs in dotted-decimal slash notation

Geo/ASN Lookup

• geoip - Perform (local) maxmind geolocation on IPv4 addresses (see ?geoip for details)
• asnip - Perform (local) maxmind AS # & org lookup on IPv4 addresses (see ?asnip for details)

Testing

• randomIPs - generate a vector of valid, random IPv4 addresses (very helpful for testing)

The following data sets are included:

• ianaports - IANA Service Name and Transport Protocol Port Number Registry
• ianaipv4spar - IANA IPv4 Special-Purpose Address Registry
• ianaipv4assignments - IANA IPv4 Address Space Registry
• ianarootzonetlds - IANA Root Zone Database
• ianaprotocolnumbers - IANA Protocol Numbers

iptools Installation

devtools::install_git("https://gitlab.dds.ec/bob.rudis/iptools.git")

NOTE: Under Ubuntu (it probably applies to other variants), this only works with the current version (1.55) of the boost library, which I installed via the launchpad boost-latest package:

sudo add-apt-repository ppa:boost-latest/ppa
# sudo apt-get install python-software-properties if "add-apt-repository" is not found
sudo apt-get update
sudo apt-get install boost1.55 # might need to use 1.54 on some systems

homebrew (OS X) users can do: brew install boost and it should #justwork.

The first person(s) to get this working under Windows/mingw + boost/Rcpp gets a free copy of our book

We’ll give you an opportunity to play with iptools before covering some examples.

You are also encouraged to drop a note in the comments here or on github with any issues, suggestions or contributions. We’ve not quite worked out how we’ll be handling public gitlab issues/comments yet, but it’s on the TODO list.