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

Browsing by Author "Liu, Jian"

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Accelerating ab initio QM/MM Molecular Dynamics Simulations with Multiple Time Step Integration and a Recalibrated Semi-empirical QM/MM Hamiltonian
(American Chemical Society, 2022-06-02) Pan, Xiaoliang; Van, Richard; Epifanovsky, Evgeny; Liu, Jian; Pu, Jingzhi; Nam, Kwangho; Shao, Yihan; Chemistry and Chemical Biology, School of Science
Molecular dynamics (MD) simulations employing ab initio quantum mechanical and molecular mechanical (ai-QM/MM) potentials are considered to be the state of the art, but the high computational cost associated with the ai-QM calculations remains a theoretical challenge for their routine application. Here, we present a modified protocol of the multiple time step (MTS) method for accelerating ai-QM/MM MD simulations of condensed-phase reactions. Within a previous MTS protocol [Nam J. Chem. Theory Comput. 2014, 10, 4175], reference forces are evaluated using a low-level (semiempirical QM/MM) Hamiltonian and employed at inner time steps to propagate the nuclear motions. Correction forces, which arise from the force differences between high-level (ai-QM/MM) and low-level Hamiltonians, are applied at outer time steps, where the MTS algorithm allows the time-reversible integration of the correction forces. To increase the outer step size, which is bound by the highest-frequency component in the correction forces, the semiempirical QM Hamiltonian is recalibrated in this work to minimize the magnitude of the correction forces. The remaining high-frequency modes, which are mainly bond stretches involving hydrogen atoms, are then removed from the correction forces. When combined with a Langevin or SIN(R) thermostat, the modified MTS-QM/MM scheme remains robust with an up to 8 (with Langevin) or 10 fs (with SIN(R)) outer time step (with 1 fs inner time steps) for the chorismate mutase system. This leads to an over 5-fold speedup over standard ai-QM/MM simulations, without sacrificing the accuracy in the predicted free energy profile of the reaction.
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%.
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.
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.
SubTrack: Enabling Real-Time Tracking of Subway Riding on Mobile Devices
(IEEE, 2017-11) Liu, Guo; Liu, Jian; Li, Fangmin; Ma, Xiaolin; Chen, Yingying; Liu, Hongbo; Computer and Information Science, School of Science
Real-time tracking of subway riding will provide great convenience to millions of commuters in metropolitan areas. Traditional approaches using timetables need continuous attentions from the subway riders and are limited to the poor accuracy of estimating the travel time. Recent approaches using mobile devices rely on GSM and WiFi, which are not always available underground. In this work, we present SubTrack, utilizing sensors on mobile devices to provide automatic tracking of subway riding in real time. The real-time automatic tracking covers three major aspects of a passenger: detection of entering a station, tracking the passenger's position, and estimating the arrival time of subway stops. In particular, SubTrack employs the cell ID to first detect a passenger entering a station and exploits inertial sensors on the passenger's mobile device to track the train ride. Our algorithm takes the advantages of the unique vibrations in acceleration and typical moving patterns of the train to estimate the train's velocity and the corresponding position, and further predict the arrival time in real time. Our extensive experiments in two cities in China and USA respectively demonstrate that our system can accurately track the position of subway riders, predict the arrival time and push the arrival notification in a timely manner.
Towards In-baggage Suspicious Object Detection Using Commodity WiFi
(IEEE, 2018) Wang, Chen; Liu, Jian; Chen, Yingying; Liu, Hongbo; Wang, Yan; Computer Information and Graphics Technology, School of Engineering and Technology
The growing needs of public safety urgently require scalable and low-cost techniques on detecting dangerous objects (e.g., lethal weapons, homemade-bombs, explosive chemicals) hidden in baggage. Traditional baggage check involves either high manpower for manual examinations or expensive and specialized instruments, such as X-ray and CT. As such, many public places (i.e., museums and schools) that lack of strict security check are exposed to high risk. In this work, we propose to utilize the fine-grained channel state information (CSI) from off-the-shelf WiFi to detect suspicious objects that are suspected to be dangerous (i.e., defined as any metal and liquid object) without penetrating into the user's privacy through physically opening the baggage. Our suspicious object detection system significantly reduces the deployment cost and is easy to set up in public venues. Towards this end, our system is realized by two major components: it first detects the existence of suspicious objects and identifies the dangerous material type based on the reconstructed CSI complex value (including both amplitude and phase information); it then determines the risk level of the object by examining the object's dimension (i.e., liquid volume and metal object's shape) based on the reconstructed CSI complex of the signals reflected by the object. Extensive experiments are conducted with 15 metal and liquid objects and 6 types of bags in a 6-month period. The results show that our system can detect over 95% suspicious objects in different types of bags and successfully identify 90% dangerous material types. In addition, our system can achieve the average errors of 16ml and 0.5cm when estimating the volume of liquid and shape (i.e., width and height) of metal objects, respectively.
When Your Wearables Become Your Fitness Mate
(Elsevier, 2020-05) Guo, Xiaonan; Liu, Jian; Chen, Yingying; Computer Information and Graphics Technology, School of Engineering and Technology
Acknowledging the powerful sensors on wearables and smartphones enabling various applications to improve users' life styles and qualities (e.g., sleep monitoring and running rhythm tracking), this paper takes one step forward developing FitCoach, a virtual fitness coach leveraging users' wearable mobile devices (including wrist-worn wearables and arm-mounted smartphones) to assess dynamic postures (movement patterns & positions) in workouts. FitCoach aims to help the user to achieve effective workout and prevent injury by dynamically depicting the short-term and long-term picture of a user's workout based on various sensors in wearable mobile devices. In particular, FitCoach recognizes different types of exercises and interprets fine-grained fitness data (i.e., motion strength and speed) to an easy-to-understand exercise review score, which provides a comprehensive workout performance evaluation and recommendation. Our system further enables contactless device control during workouts (e.g., gesture to pick up an incoming call) through distinguishing customized gestures from regular exercise movement. In addition, FitCoach has the ability to align the sensor readings from wearable devices to the human coordinate system, ensuring the accuracy and robustness of the system. Extensive experiments with over 5000 repetitions of 12 types of exercises involve 12 participants doing both anaerobic and aerobic exercises in indoors as well as outdoors. Our results demonstrate that FitCoach can provide meaningful review and recommendations to users by accurately measure their workout performance and achieve and accuracy for workout analysis and customized control gesture recognition, respectively.
WristSpy: Snooping Passcodes in Mobile Payment Using Wrist-worn Wearables
(IEEE, 2019-04) Wang, Chen; Liu, Jian; Guo, Xiaonan; Wang, Yan; Chen, Yingying; Computer Information and Graphics Technology, School of Engineering and Technology
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 or patterns) are the first choice of most consumers to authorize the payment. This paper 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, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.

Browsing by Author "Liu, Jian"

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