IU Indianapolis ScholarWorks :: Browsing by Author "Chen, Yingying"

Browsing by Author "Chen, Yingying"

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Authenticating Users Through Fine-Grained Channel Information
(IEEE, 2018-02) Liu, Hongbo; Wang, Yan; Liu, Jian; Yang, Jie; Chen, Yingying; Poor, H. Vincent; Engineering Technology, School of Engineering and Technology
User authentication is the critical first step in detecting identity-based attacks and preventing subsequent malicious attacks. However, the increasingly dynamic mobile environments make it harder to always apply cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic based techniques grounded on physical layer properties to perform user authentication appears promising. In this work, the use of channel state information (CSI), which is available from off-the-shelf WiFi devices, to perform fine-grained user authentication is explored. Particularly, a user-authentication framework that can work with both stationary and mobile users is proposed. When the user is stationary, the proposed framework builds a user profile for user authentication that is resilient to the presence of a spoofer. The proposed machine learning based user-authentication techniques can distinguish between two users even when they possess similar signal fingerprints and detect the existence of a spoofer. When the user is mobile, it is proposed to detect the presence of a spoofer by examining the temporal correlation of CSI measurements. Both office building and apartment environments show that the proposed framework can filter out signal outliers and achieve higher authentication accuracy compared with existing approaches using received signal strength (RSS).
CalTrig: A GUI-Based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents
(bioRxiv, 2024-10-01) Lange, Michal A.; Chen, Yingying; Fu, Haoying; Korada, Amith; Guo, Changyong; Ma, Yao-Ying; Pharmacology and Toxicology, School of Medicine
Advances in in vivo C⁢a2+ imaging using miniatured microscopes have enabled researchers to study single-neuron activity in freely moving animals. Tools such as MiniAN and CalmAn have been developed to convert C⁢a2+ visual signals to numerical information, collectively referred to as CalV2N. However, substantial challenges remain in analyzing the large datasets generated by CalV2N, particularly in integrating data streams, evaluating CalV2N output quality, and reliably and efficiently identifying Ca2+ transients. In this study, we introduce CalTrig, an open-source graphical user interface (GUI) tool designed to address these challenges at the post-CalV2N stage of data processing. CalTrig integrates multiple data streams, including C⁢a2+ imaging, neuronal footprints, C⁢a2+ traces, and behavioral tracking, and offers capabilities for evaluating the quality of CalV2N outputs. It enables synchronized visualization and efficient C⁢a2+ transient identification. We evaluated four machine learning models (i.e., GRU, LSTM, Transformer, and Local Transformer) for C⁢a2+ transient detection. Our results indicate that the GRU model offers the highest predictability and computational efficiency, achieving stable performance across training sessions, different animals and even among different brain regions. The integration of manual, parameter-based, and machine learning-based detection methods in CalTrig provides flexibility and accuracy for various research applications. The user-friendly interface and low computing demands of CalTrig make it accessible to neuroscientists without programming expertise. We further conclude that CalTrig enables deeper exploration of brain function, supports hypothesis generation about neuronal mechanisms, and opens new avenues for understanding neurological disorders and developing treatments.
CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping
(ACM, 2019-05) Liu, Jian; Shi, Cong; Chen, Yingying; Liu, Hongbo; Gruteser, Marco; Computer and Information Science, School of Science
With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%.
Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing
(IEEE, 2015-01) Zhang, Wenlin; Guo, Yi; Liu, Hongbo; Chen, Yingying; Wang, Zheng; Mitola, Joseph III; Department of Computer Information and Graphics Technology, School of Engineering and Technology
In this paper, we study the distributed spectrum sensing in cognitive radio networks. Existing distributed consensus-based fusion algorithms only ensure equal gain combining of local measurements, whose performance may be incomparable to various centralized soft combining schemes. Motivated by this fact, we consider practical channel conditions and link failures, and develop new weighted soft measurement combining without a centralized fusion center. Following the measurement by its energy detector, each secondary user exchanges its own measurement statistics with its local one-hop neighbors, and chooses the information exchanging rate according to the measurement channel condition, e.g., the signal-to-noise ratio (SNR). We rigorously prove the convergence of the new consensus algorithm, and show all secondary users hold the same global decision statistics from the weighted soft measurement combining throughout the network. We also provide distributed optimal weight design under uncorrelated measurement channels. The convergence rate of the consensus iteration is given under the assumption that each communication link has an independent probability to fail, and the upper bound of the iteration number of the $ \epsilon$ -convergence is explicitly given as a function of system parameters. Simulation results show significant improvement of the sensing performance compared to existing consensus-based approaches, and the performance of the distributed weighted design is comparable to the centralized weighted combining scheme.
Enabling Self-healing Smart Grid Through Jamming Resilient Local Controller Switching
(IEEE, 2015-09) Liu, Hongbo; Chen, Yingying; Chuah, Mooi Choo; Yang, Jie; Poor, H. Vincent; Department of Computer and Information Science, School of Science
A key component of a smart grid is its ability to collect useful information from a power grid for enabling control centers to estimate the current states of the power grid. Such information can be delivered to the control centers via wireless or wired networks. It is envisioned that wireless technology will be widely used for local-area communication subsystems in the smart grid (e.g., in distribution networks). However, various attacks with serious impact can be launched in wireless networks such as channel jamming attacks and denial-of-service attacks. In particular, jamming attacks can cause significant damages to power grids, e.g., delayed delivery of time-critical messages can prevent control centers from properly controlling the outputs of generators to match load demands. In this paper, a communication subsystem with enhanced self-healing capability in the presence of jamming is designed via intelligent local controller switching while integrating a retransmission mechanism. The proposed framework allows sufficient readings from smart meters to be continuously collected by various local controllers to estimate the states of a power grid under various attack scenarios. The jamming probability is also analyzed considering the impact of jammer power and shadowing effects. In addition, guidelines on optimal placement of local controllers to ensure effective switching of smart meters under jamming are provided. Via theoretical, experimental and simulation studies, it is demonstrated that our proposed system is effective in maintaining communications between smart meters and local controllers even when multiple jammers are present in the network.
Environment-independent In-baggage Object Identification Using WiFi Signals
(IEEE Xplore, 2021-10) Shi, Cong; Zhao, Tianming; Xie, Yucheng; Zhang, Tianfang; Wang, Yan; Guo, Xiaonan; Chen, Yingying; Engineering Technology, School of Engineering and Technology
Low-cost in-baggage object identification is highly demanded in enhancing public safety and smart manufacturing. Existing approaches usually require specialized equipment and heavy deployment overhead, making them hard to scale for wide deployment. The recent WiFi-based approach is unsuitable for practical deployment as it did not address dynamic environmental impacts. In this work, we propose an environment-independent in-baggage object identification system by leveraging low-cost WiFi. We exploit the channel state information (CSI) to capture material and shape characteristics to facilitate fine-grained inbaggage object identification. A major challenge of building such a system is that CSI measurements are sensitive to real-world dynamics, such as different types of baggage, time-varying ambient noises and interferences, and different deployment environments. To tackle these problems, we develop WiFi features based on polarized directional antennas that can capture objects’ material and shape characteristics. A convolutional neural network-based model is developed to constructively integrate the WiFi features and perform accurate in-baggage object identification. We also develop a material-based domain adaptation using adversarial learning to facilitate fast deployments in different environments. We conduct extensive experiments involving 14 representation objects, 4 types of bags in 3 different room environments. The results show that our system can achieve over 97% in the same environment, and our domain adaptation method can improve the object identification accuracy by 42% when the system is deployed in a new environment with little training.
Genetic and pharmacologic alterations of claudin9 levels suffice to induce functional and mature inner hair cells
(bioRxiv, 2023-10-10) Chen, Yingying; Lee, Jeong Han; Li, Jin; Park, Seojin; Perez Flores, Maria C.; Peguero, Braulio; Kersigo, Jennifer; Kang, Mincheol; Choi, Jinsil; Levine, Lauren; Gratton, Michael Anne; Fritzsch, Bernd; Yamoah, Ebenezer N.; Pharmacology and Toxicology, School of Medicine
Hearing loss is the most common form of sensory deficit. It occurs predominantly due to hair cell (HC) loss. Mammalian HCs are terminally differentiated by birth, making HC loss incurable. Here, we show the pharmacogenetic downregulation of Cldn9, a tight junction protein, generates robust supernumerary inner HCs (IHCs) in mice. The putative ectopic IHCs have functional and synaptic features akin to typical IHCs and were surprisingly and remarkably preserved for at least fifteen months >50% of the mouse's life cycle. In vivo, Cldn9 knockdown using shRNA on postnatal days (P) P1-7 yielded analogous functional putative ectopic IHCs that were equally durably conserved. The findings suggest that Cldn9 levels coordinate embryonic and postnatal HC differentiation, making it a viable target for altering IHC development pre- and post-terminal differentiation.
Implications of smartphone user privacy leakage from the advertiser’s perspective
(Elsevier, 2019-02) Wang, Yan; Chen, Yingying; Ye, Fan; Liu, Hongbo; Yang, Jie; Computer Information and Graphics Technology, School of Engineering and Technology
Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps’ behavior, the understanding of the consequences of such privacy losses remains limited. In this paper, we investigate how much an advertiser can infer about users’ social and community relationships. After one month’s user study involving about 190 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks’ user data. Finally, we present a real-time privacy leakage visualization tool that captures and displays the spatial–temporal characteristics of the leakages. The discoveries underscore the importance of early adoption of privacy protection mechanisms.
Inferring Mobile Payment Passcodes Leveraging Wearable Devices
(ACM, 2018-10) Wang, Chen; Liu, Jian; Guo, Xiaonan; Wang, Yan; Chen, Yingying; Computer and Information Science, School of Science
Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs) are the first choice of most consumers to authorize the payment. This work demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, which examines to what extent the user's PIN during mobile payment could be revealed from a single wrist-worn wearable device under different input scenarios involving either two hands or a single hand. Extensive experiments with 15 volunteers demonstrate that an adversary is able to recover a user's PIN with high success rate within 5 tries under various input scenarios.
mmEat: Millimeter wave-enabled environment-invariant eating behavior monitoring
(Elsevier, 2022-03) Xie, Yucheng; Jiang, Ruizhe; Guo , Xiaonan; Wang , Yan; Cheng , Jerry; Chen, Yingying; Computer Science, Luddy School of Informatics, Computing, and Engineering
Dietary habits are closely related to people’s health condition. Unhealthy diet can cause obesity, diabetes, heart diseases, as well as increase the risk of cancers. It is necessary to have a monitoring system that helps people keep tracking his/her eating behaviors. Traditional sensor-based and camera-based dietary monitoring systems either require users to wear dedicated devices or may potentially incur privacy concerns. WiFi-based methods, though yielding reasonably robust performance in certain cases, have major limitations. The wireless signals usually carry substantial information that is specific to the environment where eating activities are performed. To overcome these limitations, we propose mmEat, a millimeter wave-enabled environment-invariant eating behavior monitoring system. In particular, we propose an environment impact mitigation method by analyzing mmWave signals in Dopper-Range domain. To differentiate dietary activities with various utensils (i.e., eating with fork, fork and knife, spoon, chopsticks, bare hand) for fine-grained eating behavior monitoring, we construct Spatial–Temporal Heatmap by integrating multiple dimensional measurements. We further utilize an unsupervised learning-based 2D segmentation method and an eating period derivation algorithm to estimate time duration of each eating activity. Our system has the potential to infer the food categories and eating speed. Extensive experiments with over 1000 eating activities show that our system can achieve dietary activity recognition with an average accuracy of 97.5% and a false detection rate of 5%.

Browsing by Author "Chen, Yingying"

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