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Category Archives: Information Security

Paths To Discovery: Mapping Your Home Network

Gone are the days when one had a single computer plugged directly into a modem (cable, DSL or good ol’ Hayes). Even the days when there were just one or two computers connected via wires or invisible multi-gigahertz waves passing through the air are in the long gone by. Today (as you’ll see in the February 2016 [OUCH! newsletter](http://securingthehuman.sans.org/resources/newsletters/ouch/2016)), there are scads of devices of all kinds on your home network. How can you keep track of them all?

Some router & wireless access point vendors provide tools on their device “admin” pages to see what’s connected, but they are inconsistent at best (and usually pretty ugly & cumbersome to navigate to). Thankfully, app purveyors have jumped in to fill the gap. Here’s a list of free or “freemium” (basic features for free, advanced features cost extra) tools for mobile devices and Windows or OS X (if you’re running Linux at home, I’m assuming you’re familiar with the tools available for Linux).

### iOS

– Fing –
– iNet – Network Scanner –
– Network Analyzer Lite – wifi scanner, ping & net info –

### Android

– Fing – (google); (amazon)
– Pamn (google); (direct source)

### Windows

– Advanced IP Scanner
– Angry IP Scanner –
– Fing –
– MiTec Network Scanner –
– nmap –

### OS X

– Angry IP Scanner –
– Fing –
– IP Scanner –
– LanScan –
– nmap –

Some of these tools are easier to work with than others, but they all install pretty easily (though “Fing” and “nmap” work at the command-line on Windows & OS X, so if you’re not a “power user”, you may want to use other tools on those platforms). In most cases, it’s up to you to keep a copy of the output and perform your own “diffs”. One “pro” option for tools like “Fing” is the ability to have the tool store scan results “in the cloud” and perform this comparison for you.

Drop a note in the comments if you have other suggestions, but _vendors be warned_: I’ll be moderating all comments to help ensure no evil links or blatant product shilling makes it to reader eyeballs.

New Fake Vulnerability Service – SVE

The SVE service is a fake new service solely put out there to facilitate some inane puzzle. You can reference SVE-2015-9999 via this URL and hopefully this will make it possible for google to get it back in their index.

Moving From system() calls to Rcpp Interfaces

Over on the [Data Driven Security Blog](http://datadrivensecurity.info/blog/posts/2014/Apr/making-better-dns-txt-record-lookups-with-rcpp/) there’s a post on how to use `Rcpp` to interface with an external library (in this case `ldns` for DNS lookups). It builds on [another post](http://datadrivensecurity.info/blog/posts/2014/Apr/firewall-busting-asn-lookups/) which uses `system()` to make a call to `dig` to lookup DNS `TXT` records.

The core code is below and at both the aforementioned blog post and [this gist](https://gist.github.com/hrbrmstr/11286662). The post walks you though creating a simple interface and a future post will cover how to build a full package interface to an external library.

Using Twitter as a Data Source For Monitoring Password Dumps

I shot a quick post over at the [Data Driven Security blog](http://bit.ly/1hyqJiT) explaining how to separate Twitter data gathering from R code via the Ruby `t` ([github repo](https://github.com/sferik/t)) command. Using `t` frees R code from having to be a Twitter processor and lets the analyst focus on analysis and visualization, plus you can use `t` as a substitute for Twitter GUIs if you’d rather play at the command-line:

$ t timeline ddsecblog
   @DDSecBlog
   Monitoring Credential Dumps Plus Using Twitter As a Data Source http://t.co/ThYbjRI9Za
 
   @DDSecBlog
   Nice intro to R + stats // Data Analysis and Statistical Inference free @datacamp_com course
   http://t.co/FC44FF9DSp
 
   @DDSecBlog
   Very accessible paper & cool approach to detection // Nazca: Detecting Malware Distribution in
   Large-Scale Networks http://t.co/fqrSaFvUK2
 
   @DDSecBlog
   Start of a new series by new contributing blogger @spttnnh! // @AlienVault rep db Longitudinal
   Study Part 1 : http://t.co/XM7m4zP0tr
 
   ...

The DDSec post shows how to mine the well-formatted output from the @dumpmon Twitter bot to visualize dump trends over time:

and has the code in-line and over at the [DDSec github repo](https://github.com/ddsbook/blog/blob/master/extra/src/R/dumpmon.R) [R].

Data Driven Security Roundup: betaPERT, Shiny, Honeypots, Passwords & Reproducible Research

Jay Jacobs (@jayjacobs)—my co-author of the soon-to-be-released book [Data-Driven Security](http://amzn.to/ddsec)—& I have been hard at work over at the book’s [sister-blog](http://dds.ec/blog) cranking out code to help security domain experts delve into the dark art of data science.

We’ve covered quite a bit of ground since January 1st, but I’m using this post to focus more on what we’ve produced using R, since that’s our go-to language.

Jay used the blog to do a [long-form answer](http://datadrivensecurity.info/blog/posts/2014/Jan/severski/) to a question asked by @dseverski on the [SIRA](http://societyinforisk.org) mailing list and I piled on by adding a [Shiny app](http://datadrivensecurity.info/blog/posts/2014/Jan/solvo-mediocris/) into the mix (both posts make for a pretty `#spiffy` introduction to expert-opinion risk analyses in R).

Jay continued by [releasing a new honeypot data set](http://datadrivensecurity.info/blog/data/2014/01/marx.gz) and corresponding two-part[[1](http://datadrivensecurity.info/blog/posts/2014/Jan/blander-part1/),[2](http://datadrivensecurity.info/blog/posts/2014/Jan/blander-part2/)] post series to jump start analyses on that data. (There’s a D3 geo-visualization stuck in-between those posts if you’re into that sort of thing).

I got it into my head to start a project to build a [password dump analytics tool](http://datadrivensecurity.info/blog/posts/2014/Feb/ripal/) in R (with **much** more coming soon on that, including a full-on R package + Shiny app combo) and also continue the discussion we started in the book on the need for the infusion of reproducible research principles and practices in the information security domain by building off of @sucuri_security’s [Darkleech botnet](http://datadrivensecurity.info/blog/posts/2014/Feb/reproducible-research-sucuri-darkleech-data/) research.

You can follow along at home with the blog via it’s [RSS feed](http://datadrivensecurity.info/blog/feeds/all.atom.xml) or via the @ddsecblog Twitter account. You can also **play** along at home if you feel you have something to contribute. It’s as simple as a github pull request and some really straightforward markdown. Take a look the blog’s [github repo](https://github.com/ddsbook/blog) and hit me up (@hrbrmstr) for details if you’ve got something to share.

Data-Driven Security (The Book) Update #ShamelessSelfPromotion

Data-Driven-SecurityIf I made a Venn diagram of the cross-section of readers of this blog and the [Data Driven Security](http://dds.ec/) web sites it might be indistinguishable from a pure circle. However, just in case there are a few stragglers out there, I figured one more post on the fact that the new book by @jayjacobs & me is available _now_ in electronic form (not pre-order) wouldn’t hurt. The print book is still making it’s way from dead trees to store shelves and should be ready for the expected February 17th debut.

Here’s the list of links to e-tailers (man, I hate that term) who have it available for the various e-readers out there.

– [Amazon/Kindle](http://www.amazon.com/gp/product/B00I1Y7THY/ref=as_li_ss_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=B00I1Y7THY&linkCode=as2&tag=rudisdotnet-20)
– [B&N/Nook](http://www.barnesandnoble.com/w/data-driven-security-jay-jacobs/1117239036?ean=9781118793725)
– [Google Books](https://play.google.com/store/books/details?id=LQigAgAAQBAJ)
– [Kobo](http://store.kobobooks.com/en-US/ebook/data-driven-security)

If you happen to catch it out in the wild and not on this list, drop me (@hrbrmstr) a note `#pls`.

And, a huge thank you! to everyone for their kind accolades yesterday (esp to those who’ve purchased the book :-)

Preparing For The February 2014 Book Launch

Data Driven Security launches in February 2014. @jayjacobs & I have seen half of the book in PDF form so far and it’s almost unbelievable that this journey is almost over.

Data_Driven_Security___Amazon_Sales_Rank_Tracker

We setup a live Amazon “sales rank” tracker over at the book’s web site and provided some Python and JavaScript code to show folks how use the AWS API in conjunction with the dygraphs charting library to do the same for any ISBN. In the coming weeks, we’ll have a Google App Engine component you can clone to setup something similar without the need for your own server(s).

ZeroAccess Bots Desperately Seeking Freedom (Visualization)

I’ve been doing a bit of graphing (with real, non-honeypot network data) as part of the research for the book I’m writing with @jayjacobs and thought one of the images was worth sharing (especially since it may not make it into the book :-).

Threat_View
Click image for larger view

This is a static screen capture of a D3 force-directed graph made with R, igraph & Vega of four ZeroAccess infected nodes desperately (each node tried ~200K times over a couple days) trying to break free of a firewall over the course of 11 days. The red nodes are unique destination IPs and purple ones are in the AlienVault IP Reputation database. Jay & I have read and blogged a great deal about ZeroAccess over the past year and finally had the chance to see a live slice of how pervasive (and, noisy) the network is even with just a view from a few infected nodes.

While the above graphic is the composite view of all 11 days, the following one is from just a single day with only two infected nodes trying to communicate out (this is a pure, hastily-crafted R/igraph image):

Two ZeroAccess Infected Nodes
Click image for larger view

There are some common destinations among the two, but each has a large list of unique ones; even the best, open IP reputation database on the planet only included a handful of the malicious endpoints, which means you really need to be looking at holistic behavior modeling vs port/destination alone (I filtered out legit destination traffic for these views) if you’re trying to find egressing badness (but you hopefully already knew that).