The WebXR API enables immersive AR/VR experiences directly through web browsers on head-mounted displays (HMDs). However, prior research shows that security-sensitive UI properties and the lack of an like element that separates different origins can be exploited to manipulate user actions, particularly within the advertising ecosystem. In our prior work, we proposed five novel UI-based attacks in WebXR, targeting the ad ecosystem. This demo presents these attacks in a unified gaming application, embedding each into distinct interactive scenarios. Our work highlights the need to address design challenges and requirements for improving immersive web-based experiences. We provide our demo video at: https://youtu.be/lTBQbxnNq34.

We show a new type of side-channel leakage in which the built-in magnetometer sensor in Apple's mobile devices captures touch events of users. When a conductive material such as the human body touches the mobile device screen, the electric current passes through the screen capacitors generating an electromagnetic field around the touch point. This electromagnetic field leads to a sharp fluctuation in the magnetometer signals when a touch occurs, both when the mobile device is stationary and held in hand naturally. These signals can be accessed by mobile applications running in the background without requiring any permissions. We develop iSTELAN, a three-stage attack, which exploits this side-channel to infer users' application and touch data. iSTELAN translates the magnetometer signals to a binary sequence to reveal users' touch events, exploits touch event patterns to fingerprint the type of application a user is using, and models touch events to identify users' touch event types performed on different applications. We demonstrate the iSTELAN attack on 22 users while using 7 popular app types and show that it achieves an average accuracy of 90% for disclosing touch events, 74% for classifying application type used, and 73% for detecting touch event types.

Facial authentication mechanisms are gaining traction on smartphones because of their convenience and increasingly good performance of face recognition systems. However, mainstream systems use traditional 2D face recognition technologies, which are vulnerable to various spoofing attacks. Existing systems perform liveness detection via specialized hardware, such as infrared dot projectors and dedicated cameras. Although effective, such methods do not align well with the smartphone industry's desire to maximize screen space.

We present the SnapNet system, which provides accurate real-time map matching for cellular-based trajectory traces. Such traces are characterized by input locations that are far from the actual road segment, errors on the order of kilometers, back-and-forth transitions, and highly sparse input data. SnapNet applies a series of filters to handle the noisy locations and an interpolation stage to address the data sparseness. At the core of SnapNet is a novel incremental HMM algorithm that combines digital map hints in the estimation process and a number of heuristics to reduce the noise and provide real-time estimations. Evaluation of SnapNet using actual traces from different cities covering more than 400 km shows that it can achieve a precision and recall of more than 90% under noisy coarse-grained input location estimates. This maps to over 97% and 34% enhancement in precision and recall, respectively, when compared to the traditional HMM map-matching algorithms. Moreover, SnapNet has a latency of 0.58 ms per location estimate.

Indoor localization using mobile sensors has gained momentum lately. Most of the current systems rely on an extensive calibration step to achieve high accuracy. We propose SemanticSLAM, a novel unsupervised indoor localization scheme that bypasses the need for war-driving. SemanticSLAM leverages the idea that certain locations in an indoor environment have a unique signature on one or more phone sensors. Climbing stairs, for example, has a distinct pattern on the phone's accelerometer; a specific spot may experience an unusual magnetic interference while another may have a unique set of Wi-Fi access points covering it. SemanticSLAM uses these unique points in the environment as landmarks and combines them with dead-reckoning in a new Simultaneous Localization And Mapping (SLAM) framework to reduce both the localization error and convergence time. In particular, the phone inertial sensors are used to keep track of the user's path, while the observed landmarks are used to compensate for the accumulation of error in a unified probabilistic framework. Evaluation in two testbeds on Android phones shows that the system can achieve 0.53 meters human median localization errors. In addition, the system can detect the location of landmarks with 0.83 meters median error. This is 62 percent better than a system that does not use SLAM. Moreover, SemanticSLAM has a 33 percent lower convergence time compared to the same systems. This highlights the promise of SemanticSLAM as an unconventional approach for indoor localization.

Relation Extraction is an important preprocessing task for a number of text mining applications, including: Information Retrieval, Question Answering, Ontology building, among others. In this paper, we propose a novel Arabic relation extraction method that leverages linguistic features of the Arabic language in Web data to infer relations between entities. Due to the lack of labeled Arabic corpora, we adopt the idea of distant supervision, where DBpedia, a large database of semantic relations extracted from Wikipedia, is used along with a large unlabeled text corpus to build the training data. We extract the sentences from the unlabeled text corpus, and tag them using the corresponding DBpedia relations. Finally, we build a relation classifier using this data which predicts the relation type of new instances. Our experimental results show that the system reaches 70% for the F-measure in detecting relations.

We present the SnapNet, a system that provides accurate real-time map matching for cellular-based trajectories. Such coarse-grained trajectories introduce new challenges to map matching including (1) input locations that are far from the actual road segment (errors in the orders of kilometers), (2) back-and-forth transitions, and (3) highly sparse input data. SnapNet addresses these challenges by applying extensive preprocessing steps to remove the noisy locations and to handle the data sparseness. At the core of SnapNet is a novel incremental HMM algorithm that combines digital map hints and a number of heuristics to reduce the noise and provide real-time estimation. Evaluation of SnapNet in different cities covering more than 100km distance shows that it can achieve more than 90% accuracy under noisy coarse-grained input location estimates. This maps to over 97% and 34% enhancement in precision and recall respectively when compared to traditional HMM map matching algorithms. Moreover, SnapNet has a low latency of 1.2ms per location estimate.

Device-free (DF) indoor localization has grasped great attention recently as a value-added service to the already installed WiFi infrastructure as it allows the tracking of entities that do not carry any devices nor participate actively in the localization process. Current approaches, however, require a relatively large number of wireless streams, i.e. transmitter-receiver pairs, which is not available in many typical scenarios, such as home monitoring. In this paper, we introduce MonoPHY as an accurate mono-stream device-free WLAN localization system. MonoPHY leverages the physical layer information of WiFi networks supported by the IEEE 802.11n standard to provide accurate DF localization with only one stream. In particular, MonoPHY leverages both the low-level Channel State Information and the MIMO information to capture the human effect on signal strength. Experimental evaluation in a typical apartment, with a side-by-side comparison with the state-of-the-art, shows that MonoPHY can achieve an accuracy of 1.36m. This corresponds to at least 48% enhancement in median distance error over the state-of-the-art DF localization systems using a single stream only.