We know that our customers are concerned about the “Shellshock” BASH vulnerability and whether it affects our EnCase software, our Tableau hardware products, or any of our corporate systems. This is a legitimate concern, and because we have the utmost concern for your organizational and data security, we want to give you all the information you need regarding it. Below we address one by one the key areas that you may be wondering about.
It’s fast, it's fourth-generation, and it’s a forensic investigator’s dream come true: We’re talking about the brand new Tableau TD2u forensic duplicator, which can image at speeds in excess of 15 gigabytes per minute while concurrently generating MD5 and SHA-1 hashes.
In Part 1 of this post, I shared a method that lets you use Python scripts by configuring a file viewer in EnCase. We used Didier Stevens’ pdf-parser as an example. I also showed how EnScript could be used to greater effect by allowing us to capture the output of pdf-parser directly in a bookmark without having to manually copy and paste. Both of these techniques reduce effort by leveraging capabilities of both EnCase and the Python language.
In this post, I’ll take the same principles and apply them into an EnScript that provides a little more flexibility and functionality. Our goal is to have a GUI that gives you control over the exact functionality you want from the pdf-parser tool.
As a co-author and instructor for Guidance Software’s EnScript Programming course, I spend a lot of time teaching investigators in person around the globe. Investigators are faced with a dizzying variety of challenges. We work together in class, coming up with solutions that send EnCase off to do our bidding. EnCase and EnScript allow us to “bottle” the result of our efforts to share with other investigators (e.g. categorizing internet history, detecting files hidden by rootkits).
Python is used similarly. The interweb hosts great tools written in Python to accomplish all measures of tasks facing DFIR examiners. The community benefits from the hours of work that go into each and every .py that gets baked. It seemed to me that there should be a way for EnCase and Python to work together, so I put together a brief tutorial.
As a DFIR examiner, poring over internet history records is a well-loathed daily activity. We spend hours looking at these lists trying to find an interesting URL that moves our case one direction or another. Sometimes we can use a filtering mechanism to remove URLs that we know for certain are uninteresting, but keeping a list like this up to date is a manual task. I used Websense to assist with this type of work at my previous job, but I have also had brief experiences with Blue Coat. as well.