Anyone working in the field of Digital Forensics is aware that a substantial portion of time is dedicated to reverse engineering passwords. That is, in most cases a digital forensics investigator receives a password-protected handheld device, or a laptop with an encrypted hard disk, or a Microsoft Word document which has been password protected.
It is then the task of the investigator to try to retrieve the evidence, and that in turns requires reverse engineering the password; in some cases this can be achieved by recovering the hash of the password, which is stored somewhere (the locations are often known) on the device’s memory.
In order to obtain the password from the hash, we have to run a brute-force search algorithm that guesses passwords (the guesses can be more or less educated, depending on what is known about the case). Sometimes we get lucky. There are two programs that are used extensively for this purpose: John the Ripper and hashcat.
As we have been studying methods for recovering passwords from hashes, we have been using AWS EC2 instances in order to run experiments and help HTTF with their efforts. Together with senior capstone students as well as graduate students in Cybersecurity, we have been creating a set of guidelines and best practices to help in the recovery of passwords from hashes. AWS EC2 instances are ideal as they can be crafted to the needs and resources of a particular case. For example we are currently running a
t2.2xlarge instance on a case where we have to recover the password of a Microsoft Word document; we have also used a
p2.16xlarge with GPU-based parallel compute capabilities, but it costs $14/hour of usage, and so we deploy it in a very surgical manner.
“Storage Evaluator And Knowledge Extraction Reader”
On Monday August 7, at 6pm, in DEL NORTE 1530, the COMP 524 (Cybersecurity) students will present their final project, a technical solution for the SoCal High Technology Task Force in Ventura. This project implements a digital forensic tool with strict performance requirements.
We used GitHub as the software repository, Dropbox Paper for the documentation Wiki, and AWS S3 for distribution of the production version of the software.
You are cordially invited to attend; the presentation will take about two hours, and there will be snacks (Short link to this post: https://wp.me/p7D4ee-FJ).
My student Mattias Huber presenting a tool for detecting Malware at the CSU Channel Islands Computer Science Capstone Showcase on May 11, 2017.
This tool can be used to upload a target file, directory, or URL to Virus Total, a website that scans the target with around 60 virus scanners from the industry. If the target is not already in the Virus Total database, the scan will be queued and completed shortly. As this is an asynchronous process, this tool is useful in uploading jobs, checking if jobs have completed, and displaying reports on completed jobs. The system also keeps track of all files uploaded, performs checks on already uploaded files to save bandwidth, saves all completed reports in a list, and all positive reports in a separate list.
Utilizing Amazon Web Services (AWS), Elastic Compute Cloud (EC2), and Simple Storage Service (S3), this system can be set up allow users to place files into a S3 bucket which will then be scanned automatically and user can be notified of any possible positives found.
- The User places a file they wish to scan into the S3 bucket, such as
- A dedicated EC2 instance watches the bucket, detects the new file, and uploads the file to Virus Total.
- The EC2 instance waits until Virus Total returns a completed report.
- If any positives are found the instance notifies the user, otherwise the report is added to the completed list.
Virus total has a public API that is limited to 6 uploads per minute, but CSU Channel Islands was granted research API access which is limited to 600 uploads per minute.
Mattias is going to make this tool available for everyone through GitHub.
Capstone Showcase Spring 2017; see here for more details and here for more pictures. And click here for the presentation poster.
I can’t say how happy I am to have AWS. I just got the account set up, started my first instance, and run a simulation for a very interesting project that I am working on with Ryan McIntyre (a student in CS). What took about 15min on my Mac Pro quad core, took 1m40s on the AWS instance.
This is a brave new world! 🙂 . Here us the summary of the experiment:
~/EdgeGraph/EdgeGraph$ time python3 cover_vs_edges.py
How many vertices? 7
Checking up to 21 edges...
0 / 21 edges complete.
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elapsed time: --- 96.4 seconds ---