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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.

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Application Forensics Update 2

Introduction

Over the past fifteen weeks, the App Forensics team investigated several pieces of mainstream monitoring software. We are now focusing on new software, getting it operational, and investigating its internal workings. Examining how the software interacts with the device is central to our larger motive of understanding the programs. For example if they’re safe, and what a company can access.

Progress

The App Forensics team is working hard on investigating the newest monitoring software, FlexiSpy. We are currently combing through data pulled from a tablet with the software and comparing it to a normal tablet that did not have the software installed. By doing this, we hope to find specific changes and additions made by the software that clues us into the program’s operations. In addition to this our team has also performed several network captures of data transferred to and from the tablets. This shows us which servers the software is talking to and what it’s sending and receiving.  

Conclusion

Once we go through the data, we should be able to get a good handle on what the software is doing. We will continue to analyze the data collected from the tablets and look for changes and behaviors in the tablets to better understand what the software is changing.  Moving forward, we will compare the data we generate on this software to the others we analyzed and draw conclusions about what they have in common.

Stay tuned for more updates to come and follow us on Twitter @ChampForensics, Instagram @ChampForensics, and Facebook @ChamplainLCDI.

The post Application Forensics Update 2 appeared first on The Leahy Center for Digital Investigation.

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Application Analysis

Introduction:

The Application Analysis team is a group of technical interns at the Leahy Center for Digital Investigation. The LCDI offers  great opportunities for students to gain knowledge and skills in digital forensics and cybersecurity. This project is how four intern students have gone about testing some consumer mobile tracking & monitoring software.

Experience:

The Application Analysis team has currently been researching four different mobile tracking & monitoring programs available in today’s market. The programs we are looking at are mSpy, FlexiSpy, Mobistealth, and Highster Mobile. We are researching the specifications that each of these applications claims to have. We have five Nexus 7 tablets, four of which are rooted using Nexus Root Toolkit and one that is being used as the control device. A control device is a device that you leave in its original state to compare with other devices so you can see what has changed. Sometimes unexpected things will change and that is how you can confirm that it has been altered. We have a laptop that is being used to monitor the traffic via WireShark and also used as the control panel for the software.   

Our team is using the following apps: Google Hangouts, Facebook, Facebook Messenger, Kik, and Skype. We are generating data by sending information between the rooted device and the control device using these various applications. We are seeing if we are able to view all the information that we generated and are checking on whether any data is not collected.

In addition, we are testing if video calls are recorded and sent to the parents’ account. We have set a keyword to test the capability of specific programs to see if it alerts the parent when the keyword is used. Since the first program that we tested has the capability of Geo-Fencing, we decided to test for this capability as well. Geo-fencing means if a device leaves a certain location, there would be an alert sent to notify the parent that the device has left the specified location.

Conclusion:

We have created a variety of questions that we would like to look further into with each of the programs, including if the software can be hidden on the device. Stay tuned to read further updates on this project and the information we continue to gather from our devices.

 

To learn more about this and other blogs of the LCDI visit us here: LCDI Blog.

Stay in the loop on our current and upcoming projects and events by following us on Facebook,  Twitter, or Instagram. 

 

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