Forensic Blogs

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Mobile App Forensics Final Update

Introduction

During this semester, the Mobile Forensics team analyzed social media apps such as Snapchat, Telegram, and LinkedIn. 

Snapchat

As for a conclusion on our Snapchat analysis, we couldn’t find much outside of prior research within the community. A big concern we had was how much data would remain on a device  twenty-four hours after it was generated. An immediate pull from the device yielded evidence of what stories the user viewed and also a log of messages exchanged with other users (but not the content of the message). This log showed who sent and received the message and the timestamp of the event. The text of messages was only viewable if either user had saved the message. Some pictures were also recovered that had the contents of stories that were viewed. This could provide some information on the interests of a user, but nothing incriminating. An interesting artifact found on the device that could not be decoded was location data found in  /data/data/com.snapchat.android/cache. We could not parse these files and believe they may related to ArcGIS.

We aquired Snapchat after a few days to see what information would still be available. Logs of conversations were not deleted and remained on the device. However, there were still no contents of the conversation again with the exception of any messages that either user saved. It appears Snapchat does not store data from the user directly on the phone, it may simply be processed and erased while in memory. There was little evidence of user activity.

Telegram

When testing Telegram we did two pulls of the tablets. We first did a pull with all three of the members and then a pull with just two members on the different operating systems. When we did the first pull, the data between the group was very easy to analyze, but the solo data was very confusing, so we did the second pull. When we tested Telegram, we were interested in the secret chats the most to see if we could find any information about them. Telegram advertises that the messages are encrypted and we were interested to see if we could verify this. The only chats that were encrypted were messages in a secret chat. This is definitely a note for a forensic investigator. When we did the pull, we could see each message in the chat log as well as any pictures and images. The one thing we could not find was any videos or voice messages that did not get saved.

LinkedIn

While analyzing LinkedIn, we once again didn’t find all the data we were looking for. We had hoped to be able to find the user’s whole work profile but that was not the case. We were able to pull and reconstruct all their chat messages, a summary of their profile, and users they connected with, but we couldn’t find any search history, viewed articles, or viewed jobs. Even when looking in the chat, we didn’t find images or voice messages in the same location as the other chats. We had some temporary files for images, but we weren’t able to confirm what the images were. They could have been images from the chat logs or they could have been images from an articles or profile.

Versions

Readers of previous blog posts may note that we were comparing differences in Android operating system versions. There has been little to no evidence found that the version of the OS has an impact on our examined applications. The only major change we found was occasionally an app on Android 6 would generate a few extra folders, but they were always empty. However, it is important to note the biggest changes would be found with differing application versions.

 

Different operating systems don’t affect the data we pulled because OS updates focus more on new features and security fixes rather than how app data is stored on the device. If we looked into different versions of the application then there would be differences in the pulls. The updates of the apps will have bug fixes as well as security fixes that make the app more secure. If we could test an older version of one of the apps to the most current update then we would find different data.

 

This is clear in the below screenshots:

Snapchat on Android 6

Snapchat on Android 7

As you can see the files may be slightly different. Any files that were not common between the two extractions were empty.

Conclusion

Our work this semester has been a good test of our examined applications to ensure that they work as advertised. One may believe that mainstream applications are secure because of their size and amount of users. Previous reports, which can be found here and here, have shown that Snapchat has been less secure in the past, and we have seen clear improvements in the amount of data that is stored on the device. With Telegram, the application works as it should and doesn’t store data on the phone to be viewed later on. However, this was only the case when using “secure messaging” and is not on by default. With LinkedIn there was little data we were able to recover from the phone. That by no means infers that LinkedIn is not storing your personal data. This simply means that that data is not stored on the device.

 

There has been a lot of hands on with tools such as ADB and Cellebrite to find efficient ways to examine these phones, and one should always question the applications they use every day with their private information. We are glad to have formed a plan of analysis for these apps, and look forward to seeing what research will be performed on the apps we use every day. As always, stay up to date with the LCDI on our social media.  Follow us on Twitter @ChampForensics, Instagram @ChampForensics, and Facebook @ChamplainLCDI.

The post Mobile App Forensics Final Update appeared first on The Leahy Center for Digital Investigation.

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EnCase Tool Eval Update 2

Introduction

This past month the EnCase team has been hard at work evaluating EnCase 8 compared to other digital forensics tools. We started by creating a Virtual Machine where we made a mock computer to be investigated. After this we took the information from the VM and began using it in our tools. We then began analyzing our data by searching through it looking for relevant information that could be useful to our investigation. After looking for artifacts we went on to search the keywords “e”, “asdfghjkl” and “cyanide”.

Results

Upon searching the keywords, we found a ton of relevant files but just as many irrelevant files. After we searched for “cyanide”, we began the process of looking for relevant files. We searched through the 131 files and found that 50 of these files were relevant to our data generation. We then compared our results to the results of the other teams. Over a two week span, we sat down with the members of the other teams and opened our VMs. We read off our results to each other, recording differences in the hits and files found by our tools. This was a time consuming process because we had to compare every single hit and then find the differences among the tools. Upon doing this we learned some of the strengths and weaknesses of EnCase compared to the other tools.

Conclusion

We are nearing the end of the semester and finishing up the work for our internships. All we have left to do is file carving to recover deleted data, finish up our final report, and we are done!

Stay tuned for updates by checking out @champforensicslcdi on Instagram and @ChampForensics on Twitter!

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Mobile App Forensics Intern Blog 2

Introduction

Over the past month, our team has analyzed the applications Expedia and Google Trips. These apps help users plan trips to locations abroad with features to order reservations and plan day trips. Our goal for analyzing these applications was to find out how much information they hold for forensic investigators. This will in turn give investigators an easier time catching suspects.

Findings

The application Expedia has very little, if any, information on the system itself. It appears to only store information if the user purchases a ticket. The only other thing we found was sometimes there may be a flight plan stored on the system, but that’s it.

Google Trips, on the other hand, stores most if not all of the information on the system itself. Specifically, it contains all reservations, day trips, and other user input on the system. The application also stores all locations and events of the city that the user is visiting. If a person uses this application rigorously it would provide investigators with a lot of information. The application relies on MIDs, or a set of identifiers provided by Google. When correlated with the locations using certain items in the database, one can easily find the location of the corresponding MID.

Conclusion

The team’s next project will involve game app forensics. What information do apps downloaded from the playstore keep? What is stored internally? The team’s goal is to find as much information as we can about internally stored device data from two game apps. The apps are unknown as of now. Stay tuned for updates by checking out @champforensicslcdi on Instagram and @ChampForensics on Twitter!

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