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Smart devices are becoming quite popular and many homes all around the world have started using such devices. Smart devices are used for various purposes: home security, entertainment, news, streaming, HVAC, medical healthcare, etc. The cost of these devices vary according to their functionality, brand and configuration. However, there is one thing that is common among these devices in a home setting, that is, all these devices are connected to the home’s Wi-Fi network. This could mean that if a security vulnerability exists in even one of the devices in a smart home setting especially in a smart medical home, it could lead to serious repercussions. Current state-of-the-art solution to this problem is to purchase vulnerability scanning software or to deploy packet sniffing network-based tools. The drawback is that those tools can be expensive and on the other hand, most people are unaware of the existence of security threats which could arise from vulnerabilities that exist in smart home devices. In this work, we aim to explore the existence of vulnerabilities in an experimental smart home setting using various connected devices and to propose a set of solutions to isolate vulnerable devices in order to secure the smart home.

Part 2: Mobile and Embedded Device Forensics; International audience; Smartphones contain large amounts of data that are of significant interest in forensic investigations. In many situations, a smartphone owner may be willing to provide a forensic investigator with access to data under a documented consent agreement. However, for privacy or personal reasons, not all the smartphone data may be extracted for analysis. Courts have also opined that only data relevant to the investigation at hand may be extracted.This chapter describes the design and implementation of a targeted data extraction system for mobile devices. It assumes user consent and implements state-of-the-art filtering using machine learning techniques. The system can be used to identify and extract selected data from smartphones in real time at crime scenes. Experiments conducted with iOS and Android devices demonstrate the utility of the targeted data extraction system.

Content hiding (or vault) apps are a class of applications that allow users to hide photos, videos, documents and other content securely. A subclass of these applications called decoy apps further supports secret hiding by having a mode which mimics standard apps such as calculators but can turn into a vault app through entering a specific input. In this work we focus on iOS devices and first describe how to identify content hiding applications from the App Store. We consider not only the US Store but also give results for App Stores in Russia, India and China. We show an effective and very fast identification of content hiding apps through a two-phase process: initial categorization using keywords followed by more precise binary classification. We next turn to understanding the behavior and features of these vault apps and how to extract the hidden information from artifacts of the app's stored data. Based on this work, we have designed and built a fully automated vault app identification and extraction system that first identifies and then extracts the hidden data from the apps on an iOS smartphone. Using our vault identification and data extraction system (VIDE), law enforcement investigators can more easily identify and extract data from such apps as needed. Although vault apps are removed regularly from the App Store, VIDE can still identify removed apps as our system continues to maintain information on such apps in our vault database.

The adoption of smart home Internet of Things (IoT) devices continues to grow. What if your devices can snitch on you and let us know where you are at any given point in time? In this work we examined the forensic artifacts produced by Nest devices, and in specific, we examined the logical backup structure of an iPhone used to control a Nest thermostat, Nest Indoor Camera and a Nest Outdoor Camera. We also integrated the Google Home Mini as another method of controlling the studied Smart Home devices. Our work is the primary account for the examination of Nest artifacts produced by an iPhone, and is also the first open source research to produce a usable forensics tool we name the Forensic Evidence Acquisition and Analysis System (FEAAS). FEAAS consolidates evidentiary data into a readable report that can infer user events (like entering or leaving a home) and what triggered an event (whether it was the Google Assistant through a voice command, or the use of an iPhone application). Our results are important for the advancement of digital forensics, as there are cases starting to emerge in which smart home IoT devices have already been used as culpatory evidence.