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 this work we implement an automatic emitter identification system using three dimensional convolutional neural networks (3D-CNN). We show that it is possible to leverage the short term spatio-temporal properties of the raw I/Q signal data using 3D-CNN and apply this to the task of transmitter identification. The time series of raw I/Q signal data exhibits a “helical” structure which encodes the intrinsic properties of the emitter as short term variations. These features are exploited by our system to create unique “fingerprints” for the emitters, which are finally used for the task of identification. Our system is end-to-end, works with the raw I/Q signal data and is able to automatically discriminate between different transmitters without using a recurrent structure, thus reducing the complexity of the system. We experimented with four novel RF transmitter datasets that we curated in an uncalibrated and uncontrolled indoor environment. The first dataset consists of four identical transmitters, the second eight identical transmitters, the third four heterogeneous transmitters and the last eight heterogeneous transmitters. We use Ettus Research USRP Software Defined Radios (USRP B210, B200, X310), ADLM Pluto and BladeRF for transmission and collect the raw signal data using a RTL Software Defined Radio (RTL SDR) receiver dongle, from each of the four and eight emitters at a time. We use the data to train and test the 3D convolutional neural network for discerning intrinsic transmitter characteristics for the task of classification. Through our methods and implementation, we show that we can identify the transmitters with ~99 % accuracy.

Primary user emulation (PUE) attacks can pose a significant threat to the deployment of a robust cognitive radio network implementing dynamic spectrum access, for an intelligent allocation and usage of already crowded spectrum bands. In this paper, we present a solution towards the PUE attacks. We present two generative adversarial net (GAN) based models to successfully emulate the primary users (PUs) in two ways. We propose a (i) dumb generator model without any "prior" knowledge of PU's feature space, (ii) a smart generator model with some "prior" knowledge about PU's transmission. We also propose two deep neural network based discriminator models to discriminate between the PU and the emulated primary users (EPU) from the corresponding generators. Both the generator and discriminator of each GAN model gets smarter with iterative and sequential GAN training. Through a testbed evaluation, we show that discriminators are able to catch ~50% of PUE attackers without the GAN training during the deployment phase. We also observe 100% accuracy for both the GAN models during training phase. Ultimately, after the GAN training, the discriminators achieved 98% and 99.5% accuracies, for dumb and smart generator models respectively, to distinguish "yet to be seen" PUE attacker.

The never-before-seen proliferation of interconnected low-power computing devices, patently dubbed the Internet of Things (IoT), is revolutionizing how people, organizations, and malicious actors interact with one another and the Internet. Many of these devices collect data in different forms, be it audio, location data, or user commands. In civil or criminal nature investigations, the data collected can act as evidence for the prosecution or the defense. This data can also be used as a component of cybersecurity efforts. When data is extracted from these devices, investigators are expected to do so using proven methods. Still, unfortunately, given the heterogeneity in the types of devices that need to be examined, few widely agreed-upon standards exist. In this paper, we look at some of the architectures, current frameworks, and methods available to perform forensic analysis of IoT devices to provide a roadmap for investigators and researchers to form the basis of an investigation.

In this paper, we analyze the Reddit social network with the goal of identifying “highly interconnected” subreddits. Intuitively, a subreddit is highly interconnected if the users in the subreddit interact a lot with users from other subreddits in the Reddit ecosystem. To identify the highly interconnected subreddits, we used the communication patterns of the users on the Reddit platform. We definde an “interconnectedness score” that was obtained from user interactions across subreddits. This score was used to identify the highly interconnected subreddits. We also leveraged the interactions among users within the subreddits to identify implicit leader–follower relationships within them. Intuitively, an implicit leader in a subreddit is someone who receives a lot of attention from other users, who are the followers. We inferred the implicit leaders using only the responses they received on their posts from other users in the subreddit. Finally, we studied the role played by these implicit leaders within the interconnected subreddits using the idea of a “leaderness score”. For the analysis, we used data obtained from Reddit in 2022 with a custom-built crawler. We analyzed a total of 125,000 subreddits for this work and identified the group of highly interconnected subreddits using the idea of the interconnectedness score. We manually evaluated the content of the posts on the identified interconnected subreddits in order to understand the nature of these subreddits. Our analysis showed that the highly interconnected subreddits discuss content considered to be “not safe/suitable for work” (NSFW). We also observed that though these subreddits were highly interconnected among themselves, they were sparsely connected with other non-NSFW subreddits. Furthermore, we found that the implicit leaders in these subreddits drove majority of the conversations in these groups. These results are socially significant as they can be used to make online social networks safe for the underage population. Thus, our results can be used for enforcing age-based restrictions on access to these NSFW subreddits. Finally, our results also open up the possibility of moderating the content on these subreddits by enforcing content moderation rules on the implicit leaders who drive the conversation in these groups. Finally, though these results are specific to Reddit, the insights obtained from this analysis can be used for analyzing other large-scale online social networks with similar goals to this study.