IoT Forensics: Amazon Echo as a Use Case

Li, Shancang; Choo, Kim‐Kwang Raymond; Sun, Qindong; Buchanan, William J.; Cao, Jiuxin

doi:10.1109/jiot.2019.2906946

Cited by 106 publications

(46 citation statements)

References 36 publications

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“…Chung et al [4], focused on the client-centric and cloud-native artifacts stored on companion clients of the Amazon Echo. Li et al [16], analyzed forensic artifacts retrieved from the Amazon Echo as a use case to demonstrate their proposed forensic analysis model. Forensic analysis of Amazon echo was also conducted in a study by Shin et al [17].…”

Section: Related Workmentioning

confidence: 99%

Forensic Investigation of Google Assistant

Akinbi

Berry

2020

SN COMPUT. SCI.

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Google Nest devices have seen a rise in demand especially with Google’s huge advantage in search engine results and a complex ecosystem that consists of a range of companion devices and compatible mobile applications integrated and interacting with its virtual assistant, Google Assistant. This study undertakes the forensics extraction and analysis of client-centric and cloud-native data remnants left behind on Android smartphones by the Google Home and Google Assistant apps used to control a Google Nest device. We identified the main database and file system storage location central to the Google Assistant ecosystem. From our analysis, we show forensic artifacts of interest associated with user account information, the chronology and copies of past voice conversations exchanged, and record of deleted data. The findings from this study describe forensic artifacts that could assist forensic investigators and can facilitate a criminal investigation.

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Section: Related Workmentioning

confidence: 99%

Forensic Investigation of Google Assistant

Akinbi

Berry

2020

SN COMPUT. SCI.

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“…A big concern is that the hash verification/validation is only for the image files or some specific files, but not for examination events, or each evidence items. The existing DF solutions significantly rely on the experiences of the investigators [14], [18].…”

Section: B Digital Forensics Challenges In Iot Environmentmentioning

confidence: 99%

“…In the IoT environment, digital forensics are facing a number of challenges, including: (1) Defining framework for digital forensics that can face the new challenges in new environment; (2) Guaranteeing the reliability, availability, recovery of dynamic digital evidence in complicated environment; (3) Privacy concerns and new privacy laws, such as the compliances of the General Data Protection Regulation (GDPR); and more. New research in digital forensics must address these above challenges in the procedural, social, and legal field [14], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Digital Forensics Investigation Framework in the Internet of Things and Social Systems

Qin

Min

2019

IEEE Trans. Comput. Soc. Syst.

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The decentralised nature of blockchain technologies can well match the needs of integrity and provenances of evidences collecting in digital forensics across jurisdictional borders. In this work, a novel blockchain based digital forensics investigation framework in the Internet of Things (IoT) and social systems environment is proposed, which can provide proof of existence and privacy preservation for evidence items examination. To implement such features, we present a block enabled forensics framework for IoT, namely IoT forensic chain (IoTFC), which can offer forensic investigation with good authenticity, immutability, traceability, resilience, and distributed trust between evidential entitles as well as examiners. The IoTFC can deliver a gurantee of traceability and track provenance of evidence items. Details of evidence identification, preservation, analysis, and presentation will be recorded in chains of block. The IoTFC can increase trust of both evidence items and examiners by providing transparency of the audit train. The use case demonstrated the effectiveness of proposed method.

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“…In the context of the widespread use of big data, the singlelabel system ignores the joint relationship of different fault types, and it is difficult to make a correct judgment for the location, type and degree of mechanical failure. Therefore, this paper put forward a multi-label system [22]. This system is a deep learning network for multi-task fault diagnosis, which is applied to classifying the fault signals of different categories of bearing and gear by establishing a one-dimensional convolution.…”

Section: Introductionmentioning

confidence: 99%

A multi-fault diagnosis method of gear-box running on edge equipment

et al. 2020

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Edge computing equipment is a new tool that has been widely used to monitor the operation state of industrial equipment and to diagnose and analyze faults. Therefore, the fault diagnosis algorithm used in the edge computing device plays an especially significant role in fault diagnosis. The application of deep learning method in mechanical fault diagnosis has been gradually popularized, because it has many advantages, such as strong classification ability and accurate feature extraction ability. However, many of the completed papers and models are based on single label system and are used to diagnose single target fault. The validation set is not rigorous enough, and it is difficult to accurately simulate the faults that may occur in the actual production process. Nowadays, in the era of big data, the single label system ignores the joint relationship of different fault types, and it is difficult to make a correct judgment for the location, type and degree of mechanical failure. Hence, in the process of experiment, we used the bearing data of Case Western Reserve University(CWRU) to ensure the wide range and large quantity of data sets. A fault diagnosis method of gear and bearing in the gear-box based on multi-task deep learning model is put forward. In this method, gear and bearing faults can be diagnosed simultaneously. Through a separate task layer, this method can adaptively extract the characteristics of distinct targets from the same signal, and add a Batch Normalization layer(BN) to accelerate the convergence speed of the network. Through experiments, we conclude that it is an effective method which can judge the fault situation of gear and bearing accurately in a variety of working conditions.

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IoT Forensics: Amazon Echo as a Use Case

Cited by 106 publications

References 36 publications

Forensic Investigation of Google Assistant

Forensic Investigation of Google Assistant

Blockchain-Based Digital Forensics Investigation Framework in the Internet of Things and Social Systems

A multi-fault diagnosis method of gear-box running on edge equipment

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