Smart vehicle forensics: Challenges and case study

Le-Khac, Nhien-An; Jacobs, Daniel C.; Nijhoff, John; Bertens, Karsten; Choo, Kim‐Kwang Raymond

doi:10.1016/j.future.2018.05.081

Cited by 68 publications

(30 citation statements)

References 11 publications

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“…For example, artifacts collected from a vehicle's infotainment system such as location and timestamp information, driving routes, and music played, can helped to pinpoint a suspect's location at a given point in time. In addition, the suspect's personal data such as call logs, contact lists, SMS messages, pictures, and videos, can potentially be forensically recovered (Le-Khac et al, 2020).…”

Section: Vehiclesmentioning

confidence: 99%

“…Increasingly, vehicles such as autonomous vehicles, self‐driving vehicles, or unmanned (aerial, ground and water) vehicles have built‐in data acquisition, data storage, and data dissemination/communication capabilities, which can be useful in a forensic investigation (Le‐Khac, Jacobs, Nijhoff, Bertens, & Choo, 2020). For example, artifacts collected from a vehicle's infotainment system such as location and timestamp information, driving routes, and music played, can helped to pinpoint a suspect's location at a given point in time.…”

Section: Potential Forensic Artifactsmentioning

confidence: 99%

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The need for Internet of Things digital forensic black‐boxes

Chung

Choo

2020

WIREs Forensic Science

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In our interconnected cyber-physical world, the types and number of Internet of Things (IoT) will also increase. Such devices are also generally capable of capturing a broad range of information, including digital artifacts that can facilitate a digital investigation during a cyber security incident (e.g., data breach). In other words, IoT devices are potential evidence acquisition sources. We posit the importance of having a digital forensic black-box, conceptually similar to the cockpit voice recorder (also known as a flight recorder) on aircrafts, to facilitate digital investigations. Using a smart home comprising many different IoT devices (e.g., smart home devices, smart vehicles, and smart wearables) as an example, we discuss where such a black-box can reside and what sort of artifacts can be collected. This black-box can also complement other existing digital forensic readiness strategies, such as those described in ISO/IEC 27043:2015. We also explore the associated design requirements such as data provenance. There are changes required to the organization's current computing architecture in order to deploy our proposed black-box, as explained in this paper. In addition, we will explore the potential privacy implications and potential research opportunities (e.g., blockchain-based digital forensic black-box).

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Section: Vehiclesmentioning

confidence: 99%

Section: Potential Forensic Artifactsmentioning

confidence: 99%

The need for Internet of Things digital forensic black‐boxes

Chung

Choo

2020

WIREs Forensic Science

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“…In recent years, IoT forensic has attracted attention from the forensic community [13], [14], [15], [16], for example in wearable devices [17], smart vehicles [18], smart home devices [19], and so on. Approaches may vary between the nature and type of digital forensic investigation.…”

Section: Related Literaturementioning

confidence: 99%

IoT Forensics: Amazon Echo as a Use Case

Choo

Sun

et al. 2019

IEEE Internet Things J.

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109

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Internet of Things (IoT) are increasingly common in our society, and can be found in applications such as battlefields and national security. These devices can also be targeted by attackers and hence, they are a valuable source in digital forensic investigations. In addition, incriminating evidence may be stored on an IoT device (e.g. Amazon Echo in a home environment and Fitbit worn by the victim or an accused person). In comparison to the IoT security and privacy literature, however, IoT forensics is relatively under-studied. IoT forensics is also challenging in practice, particularly due to the complexity, diversity, and heterogeneity of IoT systems. In this paper, we present an IoT based forensic model that supports the identification, acquisition, analysis, and presentation of potential artifacts of forensic interest from IoT devices and the underpinning infrastructure. Specifically, we use the popular Amazon Echo as a use case to demonstrate how our proposed model can be used to guide forensics analysis of IoT devices.

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“…The growing influence of DFs among researchers and practitioners in recent years have already provided new developments, innovative ideas and practices for the advancement of digital investigation in diverse areas, for example, smart TV forensics, smart home forensics, drone forensics, mobile application forensics, alexa ecosystem forensics, Internet of things device forensics, smart cities forensics, scada forensics, and so on. Le‐Khac et al discusses the recent challenges and case studies related to smart vehicle forensics. The lack of total network states, and high mobility of smart vehicles makes it more vulnerable to the potential attacks when content dissemination is achieved via vehicular social networks .…”

Section: Background and Related Workmentioning

confidence: 99%

An intelligent approach for examining and detecting target data fragments in suspected large storage drives

Bharadwaj

Singh

2019

Security and Privacy

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The ever increasing capacity of storage devices is becoming a formidable obstruction to the digital forensic community due to its substantial investigation time and preservation space requirements. However, the investigation process becomes more complicated, whenever the fragmented or partially overwritten drives need forensic consideration. It is becoming extremely necessary to unfold novel methods the examination of storage drives involving a bulk volume of data. In this paper, the differential evolution (DE) technique is utilized with an unsupervised mean-shift clustering approach in the field of digital forensics for intelligently locating the traces of target file in suspected storage drives or raw images. The proposed methodology leverages the drives' geometrical information in DE for obtaining random sector samples followed by fitness value and sector hash computation. The clustering algorithm evaluates the obtained intelligence and assists in estimating the regions of forensic significance in the drive, instead of considering the entire drive contents. As an outcome, a proof-of-concept Python based command line tool has been developed and released to support the study and further enhancement. The experiments and case studies demonstrated using the drives of different capacities demonstrates the efficacy of the proposed method. K E Y W O R D Sdifferential evolution, digital forensics, huge data volumes, mean shift clustering, storage drive, target file 1 Security Privacy. 2019;2:e71.wileyonlinelibrary.com/journal/spy2

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Smart vehicle forensics: Challenges and case study

Cited by 68 publications

References 11 publications

The need for Internet of Things digital forensic black‐boxes

The need for Internet of Things digital forensic black‐boxes

IoT Forensics: Amazon Echo as a Use Case

An intelligent approach for examining and detecting target data fragments in suspected large storage drives

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