Multi-motion behavioral biometrics based implicit authentication leverages individual unique behavioral patterns for user authentication. However, traditional methods using fixed-sized time window segmentation often disrupt local temporal structures and overlook behavioral semantics. This paper investigates a semantic-aware segmentation-based implicit authentication approach, yet challenges persist in achieving semantically consistent segmentation, fixed-dimension representations for variable-length data, and robust modeling under large intra-class variance. Towards this end, we propose SegAuth, a novel semantic-aware multi-motion behavioral biometrics based implicit authentication system. Specifically, given the input raw multi-motion data, SegAuth first adopts a data-driven semantic-aware segmentation method to adaptively generate variable-sized segments, capturing fine-grained behavioral patterns for authentication. Next, SegAuth proposes a causal temporal convolutional network, which allows to learn the effective embedding of varied-sized multi-motion data segments. Finally, a multi-center deep one-class classifier–based authentication model is developed to capture behavioral representations from genuine user data characterized by high intra-class variance, allowing it to identify and authenticate behaviors that differ from normal patterns. Extensive experiments are conducted on a large-scale uncontrolled evaluation dataset. The experimental results demonstrate the state-of-the-art authentication performance of SegAuth.

As central hubs of the Internet of Everything, smart phones integrate essential functions such as payments, navigation, and IoT connectivity. However, this expanded functionality also heightens security risks. Motion dynamics biometrics, which utilizes motion patterns from user-phone interactions captured via multi-sensor data, has emerged as a promising solution for smartphone security. Offering continuous and unobtrusive protection by analyzing natural user interactions, it still faces challenges in effectively modeling the complex spatio-temporal dynamics between the user and the phone within multi-sensor data. This paper focuses on leveraging graph neural networks (GNNs) to enhance user behavior modeling for smartphone security protection by capturing the relationships within motion sensor data, but it is non-trivial due to the characteristics of complexity, asynchrony, and temporal dependencies of multi motion sensor data. Towards this end, we propose MotionGNN, a multi-motion spatial-temporal graph based behavior modeling framework for user identification and authentication. Specifically, MotionGNN first divides the input multi-motion sensor data into a sequence of segments adaptively by developing a context-aware data segmentation method. Then, MotionGNN constructs fully connected spatio-temporal graphs to model sensor dependencies and temporal dynamics. Finally, windowing graph convolutions are adopted to learn user behavior representations. To evaluate the performance of MotionGNN, we collect a large-scale dataset from real-world scenarios. Extensive experiments demonstrate the state-of-the-art performance of MotionGNN in user identi fication and authentication tasks. We also test MotionGNN for 7 days on smartphones, showing high authentication accuracy with minimal battery and memory usage, making it a reliable solution for smartphone security protection.

Accurately predicting user trading behavior in decentralized exchanges is essential for investors to mitigate risks and optimize their trading strategies. While existing research primarily focuses on predicting trading behavior in stock markets, these methods often struggle to adapt to the distinct nature of cryptocurrency trading. Specifically, they face issues such as limited adaptivity to high-frequency and algorithmic trading, as well as an insufficient consideration of fine-grained real-time market participants' behavior.Thanks to the pending mechanism of blockchain, it becomes possible to capture traders' interactions before transactions are finalized, providing valuable insights into market state. However, accurately modeling and predicting trading behavior in decentralized exchanges presents challenges, including limited adaptability to high-frequency trading, a lack of fine-grained transaction data, and high computational costs. This work proposes CryptoMixer, a lightweight fine-grained market information-aware multilayer perceptron (MLP)-based model for high-frequency cryptocurrency trading behavior prediction. Specifically, to overcome the sparsity and asynchrony of user behavior data, CryptoMixer develops a Market Information Augmenter that aggregates historical transaction data of users. Furthermore, CryptoMixer designs a Market Information Mixer as well as a Two-stream MLP Fusion Mixer to capture fine-grained user trading behavior patterns. We evaluate CryptoMixer on real-world user trading datasets from the Uniswap decentralized finance platform. Experimental results demonstrate that CryptoMixer outperforms traditional prediction models while maintaining low computational overheads, providing a practical solution for real-time cryptocurrency trading behavior prediction. The code is available at https://github.com/aqua111000/CryptoMixer.

Online reviews play a crucial role in comprehending user rating behavior and improving personalized recommendations in e-commerce. However, existing review-based recommendation systems ignore the influence of theory-driven and context information. This paper proposes the Deep Learning Recommendation Model with Textual Review and Context (DeepRM-TC), which is built upon a cognitive process-driven approach to improve the quality and interpretability of recommendations. The DeepRM-TC framework mimics the human brain's cognitive process for predicting user rating behavior. Essentially, the simulation of human cognitive processing is manifested in several aspects of the designed neural network , including treating rating prediction to an attitude question, mapping raw data in latent space as the user's belief, injecting attention mechanisms for rendering judgment and predicting rating as an answer. Furthermore, we design a three-layer coattention mechanism to adaptively match product information based on users' preferences in various contexts. This mechanism extracts finer-grained interaction information from user–product–context pairs. Extensive experiments on real datasets demonstrate that our proposed model outperforms existing state-of-the-art models. We demonstrate the importance of context information and the three-layer coattention mechanism in enhancing recommendation accuracy through ablation and hierarchic analysis, respectively. Additionally, we further validate the performance of our model through data sparseness analysis, scalability analysis, other datasets, classification analysis , and user study.

Touch behavior biometric has been widely studied for continuous authentication on mobile devices, which provides a more secure authentication in an implicit process. However, the existing touch behavior biometric-based authentication systems suffer from two issues. First, the existing touch behavior representation methods are hard to characterize touch operations under complex usage context. Second, the authentication accuracy of existing authentication models is inclined to degrade over time in a long-term real-life usage scenario due to change in data distribution caused by varying touch behavior. Toward this end, in this article, we develop IncreAuth, an incremental learning-based continuous authentication framework, which allows to provide effective stable authentication performance in the long-term smartphone usage scenario. Specifically, we first propose a novel context-aware feature set to characterize touch behavior patterns in complex usage context. Then, we develop an authentication model GBDTNN, which integrates the advantages of a gradient boosting decision tree model for processing our high-dimensional feature set and neural network model for efficient online updating. A behavior drift-based online updating mechanism is also designed to learn both long-term and short-term touch behavior patterns. To evaluate our framework, we construct a large-scale smartphone usage data set over two months collected from the unconstrained environment. Extensive experiments demonstrate that IncreAuth achieves the state-of-the-art and stable authentication accuracy over time and low system overheads.

Smartphones have become the most important devices for users to communicate and interact with different forms of media, and at the same time stored a large amount of sensitive and private data. The security and protection of such data has become increasingly critical. As the sensor technology rapid developed, the diversity of sensors on smartphones has greatly increased (e.g., motion sensor and touchscreen sensor), empowering the smartphones to provide continuous and implicit user authentication by capturing behavioral biometrics. Unfortunately, it remains a challenge to make full use of the data from the multimodality sensors to provide accurate authentication. Toward this end, in this article, we develop an implicit authentication (IA) framework AttAuth which explores organic integration of such multimodality data to authenticate smartphone users through the usage session. Specifically, AttAuth first develops a series of data processing techniques to process the multichannel motion sensor data and the discrete touchscreen data. Then, a temporal and channelwise attention-based temporal and channelwise attention recurrent neural network (TCA-RNN) is developed to build authentication model, which allows to jointly model continuously monitored motion sensor data and irregularly recorded discrete touchscreen data effectively. By generating a guidance vector based on touch events, TCA-RNN guides the temporalwise attention mechanism on the processed multichannel motion sensor data and outputs authentication results. We evaluate AttAuth on a real-world multimodality smartphone usage data set of 100 users. Extensive experiments demonstrate that AttAuth achieves the state-of-the-art authentication accuracy. Additional experiments are provided to examine the applicability of AttAuth in terms of running overheads and sensitivity to various application scenarios.

Continuous authentication, which provides identity verification using behavioral biometrics in an implicit and transparent manner, has shown potentials for protecting privacy. As the most common way of human-computer interaction, touch behavior pattern of each user has been proven distinctive and widely adopted for continuous authentication. However, most touch based solutions rely on the touchscreen signals obtained from high-level application programming interfaces, which are hard to characterize fine-grained appearance and contour profile of contact fingertips as well as dynamic sliding information in a touch gesture. In this paper, we propose a continuous authentication framework called CT-Auth, which leverages raw capacitive value collected from capacitive touchscreen on smartphone as a descriptor of touch behavior for authentication. Specifically, we first develop a three-dimensional convolution neural network model for capturing intra-gesture spatial-temporal feature and a structure extraction model for capturing structural information between moving fingertips of a touch gesture and touchscreen. A recurrent neural network based model is also applied for capturing temporal patterns among a sequence of touch gestures. To evaluate the effectiveness of our framework, we recruit 100 volunteers over 2 months and collect a large-scale dataset in the unconstrained conditions. Extensive experiments reveal that CT-Auth provides the state-of-the-art authentication accuracy.

This paper presents a novel map matching framework that adopts deep learning techniques to map a sequence of cell tower locations to a trajectory on a road network. Map matching is an essential pre-processing step for many applications, such as traffic optimization and human mobility analysis. However, most recent approaches are based on hidden Markov models (HMMs) or neural networks that are hard to consider high-order location information or heuristics observed from real driving scenarios. In this paper, we develop a deep reinforcement learning based map matching framework for cellular data, named as DMM, which adopts a recurrent neural network (RNN) coupled with a reinforcement learning scheme to identify the most-likely trajectory of roads given a sequence of cell towers. To transform DMM into a practical system, several challenges are addressed by developing a set of techniques, including spatial-aware representation of input cell tower sequences, an encoder-decoder based RNN network for map matching model with variable-length input and output, and a global heuristics-driven reinforcement learning based scheme for optimizing the parameters of the encoder-decoder map matching model. Extensive experiments on a large-scale anonymized cellular dataset reveal that DMM provides high map matching accuracy and fast inference time.

Retrieving similar trajectories aims to search for the trajectories that are close to a query trajectory in spatio-temporal domain from a large trajectory dataset. This is critical for a variety of applications, like transportation planning and mobility analysis. Unlike previous studies that perform similar trajectory retrieval on fine-grained GPS data or single cellular carrier, we investigate the feasibility of finding similar trajectories from cellular data of multiple carriers, which provide more comprehensive coverage of population and space. To handle the issues of spatial bias of cellular data from multiple carriers, coarse spatial granularity, and irregular sparse temporal sampling, we develop a holistic system cellSim . Specifically, to avoid the issue of spatial bias, we first propose a novel map matching approach, which transforms the cell tower sequences from multiple carriers to routes on a unified road map. Then, to address the issue of temporal sparse sampling, we generate multiple routes with different confidences to increase the probability of finding truly similar trajectories. Finally, a new trajectory similarity measure is developed for similar trajectory search by calculating the similarities between the irregularly-sampled trajectories. Extensive experiments on a large-scale cellular dataset from two carriers and real-world 1,701 km query trajectories reveal that cellSim provides state-of-the-art performance for similar trajectory retrieval.

This paper aims to predict a set of apps a user will open on her mobile device in the next time slot. Such an information is essential for many smartphone operations, e.g., app pre-loading and content pre-caching, to improve user experience. However, it is hard to build an explicit model that accurately captures the complex environment context and predicts a set of apps at one time. This paper presents a deep reinforcement learning framework, named as DeepAPP, which learns a model-free predictive neural network from historical app usage data. Meanwhile, an online updating strategy is designed to adapt the predictive network to the time-varying app usage behavior. To transform DeepAPP into a practical deep reinforcement learning system, several challenges are addressed by developing a context representation method for complex contextual environment, a general agent for overcoming data sparsity and a lightweight personalized agent for minimizing the prediction time. Extensive experiments on a large-scale anonymized app usage dataset reveal that DeepAPP provides high accuracy (precision 70.6 percent and recall of 62.4 percent) and reduces the prediction time of the state-of-the-art by 6.58×. A field experiment of 29 participants demonstrates DeepAPP can effectively reduce launch time of apps.

With the wide use of smartphones, more private data are collected and saved in the smartphones. This raises higher requirements for secure and effective user authentication scheme. Continuous authentication leverages behavioral biometrics as identity information and shows promising characteristics for user verification in a continuous and passive means. However, most studies require users to operate the smartphones in a specific mobile application or perform user-defined touch operations. This paper studies the continuous authentication on smartphones in the wild, where it is hard to characterize touching behavior accurately due to the complexity of usage context and cross-use of various types of touch gestures. Towards this end, in this paper, we propose a continuous authentication framework using multiple modalities, named as MMAuth, which integrates the heterogeneous information of user identity from multiple modalities (e.g., motion movement pattern, touch dynamics, usage context). A time-extended behavioral feature set (TEB) and a deep learning based one-class classifier (DeSVDD) are developed for performing more accurate authentication. Evaluations are conducted using a novel unconstrained smartphone usage dataset collected from 100 volunteers in real world as well as a public laboratory dataset. Extensive experimental results demonstrate that the state-of-the-art authentication performance of MMAuth in both unconstrained and laboratory environment, and the effectiveness of its two proposed modules (the TEB feature set and the DeSVDD classifier). Additional experiments on system robustness, in terms of usability to different touch gestures, sensitivity to various mobile applications, and scalability to user space, are also provided to examine the applicability of MMAuth.

Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are based on Hidden Markov Models (HMMs) that have heavy computation overhead to consider high-order cell tower information. This paper presents a fast map matching framework for cellular data, named as DMM, which adopts a recurrent neural network (RNN) to identify the most-likely trajectory of roads given a sequence of cell towers. Once the RNN model is trained, it can process cell tower sequences as making RNN inference, resulting in fast map matching speed. To transform DMM into a practical system, several challenges are addressed by developing a set of techniques, including spatial-aware representation of input cell tower sequences, an encoder-decoder framework for map matching model with variable-length input and output, and a reinforcement learning based model for optimizing the matched outputs. Extensive experiments on a large-scale anonymized cellular dataset reveal that DMM provides high map matching accuracy (precision 80.43% and recall 85.42%) and reduces the average inference time of HMM-based approaches by 46.58×.

This paper aims to predict the apps a user will open on her mobile device next. Such an information is essential for many smartphone operations, e.g., app pre-loading and content pre-caching, to save mobile energy. However, it is hard to build an explicit model that accurately depicts the affecting factors and their affecting mechanism of time-varying app usage behavior. This paper presents a deep reinforcement learning framework, named as DeepAPP, which learns a model-free predictive neural network from historical app usage data. Meanwhile, an online updating strategy is designed to adapt the predictive network to the time-varying app usage behavior. To transform DeepAPP into a practical deep reinforcement learning system, several challenges are addressed by developing a context representation method for complex contextual environment, a general agent for overcoming data sparsity and a lightweight personalized agent for minimizing the prediction time. Extensive experiments on a large-scale anonymized app usage dataset reveal that DeepAPP provides high accuracy (precision 70.6% and recall of 62.4%) and reduces the prediction time of the state-of-the-art by 6.58×. A field experiment of 29 participants also demonstrates DeepAPP can effectively reduce time of loading apps.