Security diagnostics expose vulnerabilities and privacy threats that exist in commercial Intelligent Virtual Assistants (IVA) – diagnostics offer the possibility of securer IVA ecosystems.

The demand for cloud computing is increasing because of the popularity of digital devices and the wide use of the Internet. Among cloud computing services, most consumers use cloud storage services that provide mass storage. This is because these services give them various additional functions as well as storage. It is easy to access cloud storage services using smartphones. With increasing utilization, it is possible for malicious users to abuse cloud storage services. Therefore, a study on digital forensic investigation of cloud storage services is necessary. This paper proposes new procedure for investigating and analyzing the artifacts of all accessible devices, such as Windows, Mac, iPhone, and Android smartphone.

Internet of Things (IoT) devices such as the Amazon Echo -a smart speaker developed by Amazon -are undoubtedly great sources of potential digital evidence due to their ubiquitous use and their always-on mode of operation, constituting a human-life's black box. The Amazon Echo in particular plays a centric role for the cloud-based intelligent virtual assistant (IVA) Alexa developed by Amazon Lab126. The Alexa-enabled wireless smart speaker is the gateway for all voice commands submitted to Alexa. Moreover, the IVA interacts with a plethora of compatible IoT devices and third-party applications that leverage cloud resources. Understanding the complex cloud ecosystem that allows ubiquitous use of Alexa is paramount on supporting digital investigations when need raises. This paper discusses methods for digital forensics pertaining to the IVA Alexa's ecosystem. The primary contribution of this paper consists of a new efficient approach of combining cloud-native forensics with client-side forensics (forensics for companion devices), to support practical digital investigations. Based on a deep understanding of the targeted ecosystem, we propose a proof-of-concept tool, CIFT, that supports identification, acquisition and analysis of both native artifacts from the cloud and client-centric artifacts from local devices (mobile applications and web browsers).

No abstract

No abstract