Rapid technology advancements, especially in the past decade, have allowed off-the-shelf unmanned aerial vehicles (UAVs) that weigh less than 250 g to become available for recreational use by the general population. Many well-known manufacturers (e.g., DJI) are now focusing on this segment of UAVs, and the new DJI Mini 2 drone is one of many that falls under this category, which enables easy access to be purchased and used without any Part 107 certification and Remote ID registration. The versatility of drones and drone models is appealing for customers, but they pose many challenges to forensic tools and digital forensics investigators due to numerous hardware and software variations. In addition, different devices can be associated and used for controlling these drones (e.g., Android and iOS smartphones). Moreover, according to the Federal Aviation Administration (FAA), the adoption of Remote ID is not going to be required for people without the 107 certifications for this segment at least until 2023, which creates finding personally identifiable information a necessity in these types of investigations. In this research, we conducted a comprehensive investigation of DJI Mini 2 and its data stored across multiple devices (e.g., SD cards and mobile devices) that are associated with the drone. The aim of this paper is to (1) create several criminal-like scenarios, (2) acquire and analyze the created scenarios using leading forensics software (e.g., Cellebrite and Magnet Axiom) that are commonly used by law enforcement agencies, (3) and present findings associated with potential criminal activities.

Unmanned Aerial Vehicles (UAVs) also known as drones have created many challenges to the digital forensic field. These challenges are introduced in all processes of the digital forensic investigation (i.e., identification, preservation, examination, documentation, and reporting). From identification of evidence to reporting, there are several challenges caused by the data type, source of evidence, and multiple components that operate UAVs. In this paper, we comprehensively reviewed the current UAV forensic investigative techniques from several perspectives. Moreover, the contributions of this paper are as follows: (1) discovery of personal identifiable information, (2) test and evaluation of currently available forensic software tools, (3) discussion on data storage mechanism and evidence structure in two DJI UAV models (e.g., Phantom 4 and Matrice 210), and (4) exploration of flight trajectories recovered from UAVs using a three-dimensional (3D) visualization software. The aforementioned contributions aim to aid digital investigators to encounter challenges posed by UAVs. In addition, we apply our testing, evaluation, and analysis on the two selected models including DJI Matrice 210, which have not been presented in previous works.

Constant advancements in technology have a significant impact on our everyday lives and the ecosystem in which we live. The growing popularity of cryptocurrencies (e.g., Bitcoin and Ethereum), along with Non-Fungible Tokens (NFTs), which are founded on blockchain technology, has opened the way for these blockchain projects to be integrated into a wide range of other kinds of applications (apps). Today, cryptocurrencies are used as a popular method of payment online; however, their popularity on the dark Web is also increasing. For example, they can be used to buy and perform various illegal activities among criminals due to their anonymity. Web3 cryptocurrency wallets, used to store cryptocurrencies, have not been studied as thoroughly as many other apps from a digital forensic perspective on mobile devices, given the increasing number of these services and apps today for many platforms, including the leading mobile operating systems (i.e., iOS and Android). Therefore, the purpose of this research is to guide investigators to unlock the full potential of popular cryptocurrency Web3 wallets, Trust Wallet and Metamask, to understand what can be recovered, and to look at areas where there are knowledge gaps. We digitally analyzed and forensically examined two mobile wallets that do not require any personal identifiers to register and are widely used for Web3 cryptocurrencies on Android and iOS devices. We review the digital evidence we have collected and discuss the implications of the forensic tools we have used. Finally, we propose a proof of concept extension to the iOS Logs, Events, And Plists Parser (iLEAPP) tool to automatically recover artifacts.

Wearable devices are becoming more and more prevalent in our daily lives as people become more curious about how well they are doing in monitoring, improving, or maintaining their health and fitness. Fitness trackers and smartwatches have become almost ubiquitous, so these devices have begun to play a critical role in forensic investigations. In this paper, the authors conducted a forensic analysis of the controlling applications for three popular fitness bands and smartwatches (i.e., Amazon Halo, Garmin Connect, and Mobvoi) on an Android smartphone device to (1) provide forensic investigators with a road-map of forensically relevant data that are stored within these applications and (2) highlight any privacy concerns that the stored data within these applications may present to the applications’ users. Our findings indicate that the three fitness applications store a wealth of user data. In particular, the Amazon Halo app stores daily, weekly, and monthly activity-related data for at least the last 13 days. The user’s Tone Analysis results were also recovered. The Garmin Connect application also records detailed user activity information, as it was possible to recover the last 15 days worth of user activity data. The Garmin Connect user’s general location was also determined via the application’s weather notification feature. Lastly, the Mobvoi application records all data points from the time the device is first used until the last time the device is used. These data points may include heart rates taken every 5 min and step counts. Our findings highlight the possibility of collecting personally identifiable information about users of these devices and apps, including their profile information, habits, location, and state of mind. These findings would be pertinent to forensic investigators in the event that these or similar applications are part of an investigation.

In the digital forensics discipline, the lack of comprehensive research that addresses investigative challenges and opportunities for newer mobile Operating Systems (OSs) such as Android and iOS keeps continuing. These two OSs are currently widely operated by millions of smartphones and used by millions of users; therefore, forensic investigators need to be prepared to analyze these OSs during an investigation giving consideration to mobile app updates. The current research efforts focus on the forensic analysis of individual applications of certain OSs. In this study, we conducted a detailed forensic investigation of both Android and iOS OSs to (1) elucidate their structures for investigators, (2) identify pertinent forensic artifacts, (3) highlight any privacy and security concerns in popular applications present on both OSs, and (4) validate the forensic investigation on the selected tools for reproducibility and verification purposes. This work aims to analyze 27 Android and 33 iOS mobile applications comprehensively.

Reliability and security are the main concerns in modern industrial wireless sensing systems because of the nature property of the industrial wireless sensing environment. Therefore, how to enhance the secrecy and make the industrial wireless sensing systems smart attract a lot of attention. In order to address the secrecy problem in industrial wireless sensing systems, this article proposes a secrecy enhancement mechanism based on space shift keying (SSK) communication in industrial wireless sensing systems where a transmitting node, a receiving node, and an eavesdropper are considered. To prevent the valid information from being attacked by the eavesdropper, the technologies of antenna selection (AS) and the artificial noise (AN) are jointly used in the proposed scheme to enhance the physical layer security (PLS). Eventually, the designed AN is canceled at the receiving node and remains at the eavesdropper. In addition, two kinds of AS methods, namely, signal-to-leakage-noise ratio (SLNR)-based method and Euclidean distance AS (EDAS)-based method are proposed for the proposed secrecy enhancement scheme. Specifically, the SLNR and EDAS methods are designed for the known eavesdropper channel case and the unknown eavesdropper channel case, respectively. Moreover, to make the scenario practical, the channel estimation error is fully considered in the sensing data transmission stage since the perfect channel state information (CSI) is rarely available in the actual sensing environment. The results from simulations demonstrate that the proposed scheme can enhance the system performances of secrecy rate (SR) and bit error rate (BER) when compared with the traditional random selection method.