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Forensicast: A Non-intrusive Approach & Tool For Logical Forensic Acquisition & Analysis of The Google Chromecast TV

Published: 17 August 2021 Publication History

Abstract

The era of traditional cable Television (TV) is swiftly coming to an end. People today subscribe to a multitude of streaming services. Smart TVs have enabled a new generation of entertainment, not only limited to constant on-demand streaming as they now offer other features such as web browsing, communication, gaming etc. These functions have recently been embedded into a small IoT device that can connect to any TV with High Definition Multimedia Interface (HDMI) input known as Google Chromecast TV. Its wide adoption makes it a treasure trove for potential digital evidence. Our work is the primary source on forensically interrogating Chromecast TV devices. We found that the device is always unlocked, allowing extraction of application data through the backup feature of Android Debug Bridge (ADB) without device root access. We take advantage of this minimal access and demonstrate how a series of artifacts can stitch together a detailed timeline, and we automate the process by constructing Forensicast – a Chromecast TV forensic acquisition and timelining tool. Our work targeted (n=112) of the most popular Android TV applications including 69% (77/112) third party applications and 31% (35/112) system applications. 65% (50/77) third party applications allowed backup, and of those 90% (45/50) contained time-based identifiers, 40% (20/50) invoked some form of logs/activity monitoring, 50% (25/50) yielded some sort of token/cookie, 8% (4/50) resulted in a device ID, 26% (13/50) produced a user ID, and 24% (12/50) created other information. 26% (9/35) system applications provided meaningful artifacts, 78% (7/9) provided time based identifiers, 22% (2/9) involved some form of logs/activity monitoring, 22% (2/9) yielded some form of token/cookie data, 22% (2/9) resulted in a device ID, 44% (4/9) provided a user ID, and 33% (3/9) created other information. Our findings also illustrated common artifacts found in applications that are related to developer and advertising utilities, mainly WebView, Firebase, and Facebook Analytics. Future work and open research problems are shared.

References

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Cited By

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  • (2023)Rule-based Entity Recognition for Forensic Timeline2023 Conference on Information Communications Technology and Society (ICTAS)10.1109/ICTAS56421.2023.10082742(1-6)Online publication date: Mar-2023
  • (2022)Cast Away: On the Security of DLNA Deployments in the SmartTV Ecosystem2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS57517.2022.00021(105-116)Online publication date: Dec-2022
  • (2022)Forensic Timeline Analysis of iOS Devices2022 International Conference on Engineering and Emerging Technologies (ICEET)10.1109/ICEET56468.2022.10007150(1-5)Online publication date: 27-Oct-2022

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cover image ACM Other conferences
ARES '21: Proceedings of the 16th International Conference on Availability, Reliability and Security
August 2021
1447 pages
ISBN:9781450390514
DOI:10.1145/3465481
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2021

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Author Tags

  1. ADB
  2. Android TV
  3. Artifacts
  4. Digital Forensics
  5. Google TV

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ARES 2021

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Overall Acceptance Rate 228 of 451 submissions, 51%

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Cited By

View all
  • (2023)Rule-based Entity Recognition for Forensic Timeline2023 Conference on Information Communications Technology and Society (ICTAS)10.1109/ICTAS56421.2023.10082742(1-6)Online publication date: Mar-2023
  • (2022)Cast Away: On the Security of DLNA Deployments in the SmartTV Ecosystem2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS57517.2022.00021(105-116)Online publication date: Dec-2022
  • (2022)Forensic Timeline Analysis of iOS Devices2022 International Conference on Engineering and Emerging Technologies (ICEET)10.1109/ICEET56468.2022.10007150(1-5)Online publication date: 27-Oct-2022

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