The increasing integration of capacitive fingerprint recognition sensors in IoT devices presents new challenges in digital forensics, particularly in the context of advanced fingerprint spoofing. Previous research has highlighted the effectiveness of materials such as latex and silicone in deceiving biometric systems. In this study, we introduce Alginate, a biopolymer derived from brown seaweed, as a novel material with the potential for spoofing IoT-specific capacitive fingerprint sensors. Our research uses Alginate and cutting-edge image recognition techniques to unveil a nuanced IoT vulnerability that raises significant security and privacy concerns. Our proof-of-concept experiments employed authentic fingerprint molds to create Alginate replicas, which exhibited remarkable visual and tactile similarities to real fingerprints. The conductivity and resistivity properties of Alginate, closely resembling human skin, make it a subject of interest in the digital forensics field, especially regarding its ability to spoof IoT device sensors. This study calls upon the digital forensics community to develop advanced anti-spoofing strategies to protect the evolving IoT infrastructure against such sophisticated threats.

In this modern world, mobile banking has become an essential tool for the daily transaction. This study explores the world of digital forensics in the context of investigating the Mint application, a popular tool for managing personal finances. We aim to uncover valuable digital clues left behind by Mint on Android and iOS devices. Our approach involves creating a step-by-step method to capture digital data of devices using Mint. Our goal is to provide investigators with insights into potential digital evidence for financial fraud cases or disputes. We also examine Mint's security features, assessing how well it protects user data through encryption and authentication methods. We employed Magnet AXIOM, Oxygen Forensics, and GrayKey to extract, analyze, and interpret these artifacts; focusing on data storage practices, potential vulnerabilities, and privacy risks associated with the application's handling of sensitive user information. Our findings underscore the potential utility of Mint artifacts for forensic investigations and law enforcement, while also highlighting the robust security measures employed by the application to protect user data.

The widespread activity of hash-tagging, especially among the Gen-Z population, and the impact of social commerce on average consumers raise questions about privacy implications and dangers of anonymous cyberstalking. In this work, we examined the privacy implications observed in hash-tag-based social-media posts (of average users and influencers) by following the trails of online shopping platform(s) product listings, consumer reviews, social-commerce policies, and influencer posts. We have conducted a preliminary analysis considering cyberstalking as one of the avenues that an anonymous stalker may use to impact the social-media user negatively. Further, we have conceptualized the trails behind hash-tagging activities in terms of a privacy threat model, the need for practical data analysis tools, and the lack of mitigation strategies at various layers. Mainly, this paper throws light on the need for more robust user privacy policies and the impact on socio-economic-privacy aspects. This paper also demonstrates the need for expanding the scope of digital investigations and DFIR tools beyond just the devices of individuals (including victims, suspects, perpetrators, and cyber-criminals) and to thoroughly prepare the forensic professionals to consider the online presence of individuals in its entirety including anonymous cyberstalking avenues and to raise awareness about the abuse of social networks.

With the increasing demand for digital privacy, content-hiding (or vault) apps are becoming popular among mobile phone users. Content-hiding apps affiliate to decoy apps. They are used for hiding photos, text, or videos and appear to have an interface very similar to commonly-used utility/productivity/gaming applications (for example, a calculator user interface). While these kinds of applications are convenient for people and let them hide private data, it raises concerns among app security researchers about their presence in legit and illicit app markets. It can also set a barrier for digital investigators, practitioners, victim service agencies, and the intelligence community since these apps are known to encrypt/delete data and make it unrecoverable. Such data could be anything ranging from contraband to classified data. Our research focuses on developing a fully automated Android Vault app Identification and Extraction system, primarily from the Google Play store. Through the feature extractions from description and images of applications followed by various machine learning and deep learning models, the system successfully identifies the content-hiding applications. The system can also automatically extract the user data from vault applications running on Android phones. To facilitate the advancement of research, we also keep an inventory of vault apps found in the Google Play store and offer to trace such apps even if they get removed from the Google Play store for security/other reasons. Our methodology and findings can be further extended to detect and classify content-hiding and anti-forensic apps in any Android app market and not limited to the Google Play store.

Wearable devices are emerging as effective modalities for the collection of individuals’ data. While this data can be leveraged for use in several areas ranging from health-care to crime investigation, storing and securely accessing such information while preserving privacy and detecting any tampering attempts are significant challenges. This paper describes a decentralized system that ensures an individual’s privacy, maintains an immutable log of any data access, and provides decentralized access control management. Our proposed framework uses a custom permissioned blockchain protocol to securely log data transactions from wearable devices in the blockchain ledger. We have implemented a proof-of-concept for our framework, and our preliminary evaluation is summarized to demonstrate our proposed framework’s capabilities. We have also discussed various application scenarios of our privacy-preserving model using blockchain and proof-of-authority. Our research aims to detect data tampering attempts in data sharing scenarios using a thorough transaction log model.

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.