IU Indianapolis ScholarWorks :: Browsing by Author "Liu, Hongbo"

Browsing by Author "Liu, Hongbo"

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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).
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%.
Determining Driver Phone Use by Exploiting Smartphone Integrated Sensors
(IEEE, 2016-08) Wang, Yan; Chen, Yingying (Jennifer); Yang, Jie; Gruteser, Marco; Martin, Richard P.; Liu, Hongbo; Liu, Luyang; Karatas, Cagdas; Department of Engineering Technology, School of Engineering and Technology
This paper utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dynamics. These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Our low infrastructure approach is flexible with different turn sizes and driving speeds. Extensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving environments. Despite noisy sensor readings from smartphones, our approach can achieve a classification accuracy of over 90 percent with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., 95 percent) with a lower false positive rate. In addition, we seek to exploit the electromagnetic field measurement inside a vehicle to complement vehicle dynamics for driver phone sensing under the scenarios when little vehicle dynamics is present, for example, driving straight on highways or standing at roadsides.
Developing a dynamic recommendation system for personalizing educational content within an E-learning network
(2018) Mirzaeibonehkhater, Marzieh; King, Brian; Jafari, Ali; Liu, Hongbo
This research proposed a dynamic recommendation system for a social learning environment entitled CourseNetworking (CN). The CN provides an opportunity for the users to satisfy their academic requirement in which they receive the most relevant and updated content. In our research, we extracted some implicit and explicit features from the system, which are the most relevant user feature and posts features. The selected features are used to make a rating scale between users and posts so that represent the link between user and post in this learning management system (LMS). We developed an algorithm which measures the link between each user and post for the individual. To achieve our goal in our system design, we applied natural language processing technique (NLP) for text analysis and applied various classi cation technique with the aim of feature selection. We believe that considering the content of the posts in learning environments as an impactful feature will greatly affect to the performance of our system. Our experimental results demonstrated that our recommender system predicts the most informative and relevant posts to the users. Our system design addressed the sparsity and cold-start problems, which are the two main challenging issues in recommender systems.
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.
Hotel crisis communication on social media: Effects of message appeal
(Taylor & Francis, 2022-08-05) Liu-Lastres, Bingjie; Guo, Yueying; Liu, Hongbo; Tourism, Event & Sport Management, School of Health and Human Sciences
The hotel industry is vulnerable to various external crises, such as the recent COVID-19 pandemic. Social media is one of the primary platforms for hotel crisis communication. Accordingly, this study adopted the perspective of message appeal and tried to develop effective hotel crisis communication messages. An online experiment was conducted where 260 Chinese customers were included. The results showed that emotional-appeal messages are more effective in attracting customers for luxury hotels, while functional-appeal messages are more suitable for economic hotels. The results also showed that perceived safety mediated the relationship between the message appeal and booking intentions and that this mediating relationship is moderated by the hotel type. This study further discussed theoretical and practical implications.
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.
Secret key distribution leveraging color shift over visible light channel
(IEEE, 2017-10) Liu, Hongbo; Liu, Bo; Shi, Cong; Chen, Yingying; Computer and Information Science, School of Science
Given the widely adoption of screen and camera in many electronic devices, the visible light communication (VLC) over screen-to-camera channel emerges as a novel short range communication technique in recent years. Active research explores various ways to convey messages over screen-camera channel, such as barcode and unobtrusive optical pattern. However, with the prevalence of LED screens of wide viewing angles and mobile devices equipped with high standard cameras, the threat of information leakage over screen-to-camera channel becomes in-negligible. Few studies have discussed how to ensure the security of data transmission over screen-to-camera channel. In this paper, we propose a secret key distribution system leveraging the unique color shift property over visible light channel. To facilitate such design, we develop a practical secret key matching based method to map the secret key into gridded optical patterns on screen, which can only be correctly recognized by the legitimate user through an accessible region and allow regular data stream transmission through valid grids. The proposed system is prototyped with off-the-shelf devices and validated under various experimental scenarios. The results show that our system can achieve high bit-decoding accuracy for the legitimate users while maintaining comparable data throughput as regular unobtrusive VLC systems with very low recovery accuracy of the encrypted data for the attackers.
Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT
(ACM, 2017) Shi, Cong; Liu, Jian; Liu, Hongbo; Chen, Yingying; Engineering Technology, School of Engineering and Technology
User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This paper supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV and thermostat, etc. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep learning based user authentication scheme to accurately identify each individual user. Extensive experiments in two typical indoor environments, a university office and an apartment, are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% and 91% authentication accuracy with 11 subjects through walking and stationary activities, respectively.

Browsing by Author "Liu, Hongbo"

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