Forensic Blogs

An aggregator for digital forensics blogs

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Wearable Forensics Team 1

Smart Watches Can Solve Crimes

This semester, The Senator Patrick Leahy Center for Digital Investigation at Champlain College (LCDI) is continuing research from the spring of 2016 about wearable technology and the impact devices like the Apple Watch and Fitbit have on forensic investigations. The team hopes to create a guide law enforcement and forensic analysts can use to find information that could aid a criminal investigation. This could include data on the user’s location, movement, heart rate, and more.

Why Wearables?

These devices have exploded in popularity in recent years, with over 102 million wearable smart devices sold in 2016. As a result, forensic investigators and law enforcement have used data from these devices, especially Fitbit, to aid investigations and prosecute criminals in homicide cases.

Despite these successes, there is still little information available on how to pull information from the wearable devices themselves. Most often, investigations utilize data pulled from the paired phones or the account information stored in the cloud. The research team at the LCDI hopes to directly image the devices and see what information is available. This would provide a standard for cases where the phone isn’t available or information can’t be released by the company.

However, if the team is unable to pull information directly from the wearable devices, they will continue the research from the 2016 wearables team and investigate data available on the paired phones and information stored by the company in the cloud. These devices and accounts include various different databases with valuable information that can aid in criminal investigations.

Four Devices to Test

The team will work with four smart watches with fitness capabilities: the Samsung Galaxy Watch, the Fitbit Versa, the Garmin Fenix 5, and the Apple Watch Series 4.  These four devices are the top smartwatches currently available. This week, the team began with datagen for the Galaxy Watch and the Fitbit Versa. This included testing the movement and heart rate sensors, GPS, and third party applications. Beyond testing each device as a smart watch, a team member took the first two devices home for a night. Check back on the team’s next blog post to see what artifacts they were able to find!

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Mobile App Intern Final Blog Post

Project Wrap Up

The Mobile App Intern team chose 3 travel apps to analyze. Kayak, Expedia, and Google Trips. All three apps stored their data within the internal storage of each device it was downloaded onto. However, Expedia proved to show very little artifacts that could be useful for forensic analysts. Most of the data kept by Expedia is not data meant for the user or analysts’; it is meant for the app itself (data logs etc). Google Trips saved the most user data out of all three of the apps. It kept user info (username and password hash), trip details (title, locations, etc), and location data. Kayak mainly stored location data, the names of hotels, and rental company information. For example, if one were to use Kayak in order to rent a Mercedes-Benz for $60 USD a day, they can set what dates to rent said car and Kayak will keep all of that information about the car and company stored. The same was true for plane and bus tickets. Company, price, dates of departure/arrival, and user timestamps are all stored.

Final Thoughts

The Mobile App Intern Team is grateful to the LCDI to have been given the chance to utilize their devices in order to perform projects and research. The team was able to acquire new skills (like rooting Android devices) and read many useful journal articles relating to mobile forensics. 

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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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