ORCID Profile
0000-0001-9741-5912
Current Organisations
Institute of Cancer Research Sutton
,
The Institute of Cancer Research
,
City University of Hong Kong
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Association for Computing Machinery (ACM)
Date: 26-01-2017
DOI: 10.1145/3023954
Abstract: Recent years have witnessed a remarkable growth in the number of smart wearable devices. For many of these devices, an important security issue is to establish an authenticated communication channel between legitimate devices to protect the subsequent communications. Due to the wireless nature of the communication and the extreme resource constraints of sensor devices, providing secure, efficient, and user-friendly device pairing is a challenging task. Traditional solutions for device pairing mostly depend on key predistribution, which is unsuitable for wearable devices in many ways. In this article, we design Gait-Key, a shared secret key generation scheme that allows two legitimate devices to establish a common cryptographic key by exploiting users’ walking characteristics (gait). The intuition is that the sensors on different locations on the same body experience similar accelerometer signals when the user is walking. However, one main challenge is that the accelerometer also captures motion signals produced by other body parts (e.g., swinging arms). We address this issue by using the blind source separation technique to extract the informative signal produced by the unique gait patterns. Our experimental results show that Gait-Key can generate a common 128-bit key for two legitimate devices with 98.3% probability. To demonstrate the feasibility, the proposed key generation scheme is implemented on modern smartphones. The evaluation results show that the proposed scheme can run in real time on modern mobile devices and incurs low system overhead.
Publisher: IEEE
Date: 04-2018
Publisher: IEEE
Date: 13-03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: ACM
Date: 04-11-2018
Publisher: Association for Computing Machinery (ACM)
Date: 06-09-2022
DOI: 10.1145/3550292
Abstract: Pattern lock-based authentication has been widely adopted in modern smartphones. However, this scheme relies essentially on passwords, making it vulnerable to various side-channel attacks such as the smudge attack and the shoulder-surfing attack. In this paper, we propose a second-factor authentication system named SwipePass, which authenticates a smartphone user by examining the distinct physiological and behavioral characteristics embedded in the user's pattern lock process. By emitting and receiving modulated audio using the built-in modules of the smartphone, SwipePass can sense the entire unlocking process and extract discriminative features to authenticate the user from the signal variations associated with hand dynamics. Moreover, to alleviate the burden of data collection in the user enrollment phase, we conduct an in-depth analysis of users' behaviors under different conditions and propose two augmentation techniques to significantly improve identification accuracy even when only a few training s les are available. Finally, we design a robust authentication model based on CNN-LSTM and One-Class SVM for user identification and spoofer detection. We implement SwipePass on three off-the-shelf smartphones and conduct extensive evaluations in different real-world scenarios. Experiments involving 36 participants show that SwipePass achieves an average identification accuracy of 96.8% while maintaining a false accept rate below 0.45% against various attacks.
Publisher: American Society of Clinical Oncology (ASCO)
Date: 10-2022
DOI: 10.1200/PO.21.00534
Abstract: Rhabdomyosarcomas (RMS) are rare neoplasms affecting children and young adults. Efforts to improve patient survival have been undermined by a lack of suitable disease markers. Plasma circulating tumor DNA (ctDNA) has shown promise as a potential minimally invasive biomarker and monitoring tool in other cancers however, it remains underexplored in RMS. We aimed to determine the feasibility of identifying and quantifying ctDNA in plasma as a marker of disease burden and/or treatment response using blood s les from RMS mouse models and patients. We established mouse models of RMS and applied quantitative polymerase chain reaction (PCR) and droplet digital PCR (ddPCR) to detect ctDNA within the mouse plasma. Potential driver mutations, copy-number alterations, and DNA breakpoints associated with PAX3/ 7-FOXO1 gene fusions were identified in the RMS s les collected at diagnosis. Patient-matched plasma s les collected from 28 patients with RMS before, during, and after treatment were analyzed for the presence of ctDNA via ddPCR, panel sequencing, and/or whole-exome sequencing. Human tumor-derived DNA was detectable in plasma s les from mouse models of RMS and correlated with tumor burden. In patients, ctDNA was detected in 14/18 pretreatment plasma s les with ddPCR and 7/7 cases assessed by sequencing. Levels of ctDNA at diagnosis were significantly higher in patients with unfavorable tumor sites, positive nodal status, and metastasis. In patients with serial plasma s les (n = 18), fluctuations in ctDNA levels corresponded to treatment response. Comprehensive ctDNA analysis combining high sensitivity and throughput can identify key molecular drivers in RMS models and patients, suggesting potential as a minimally invasive biomarker. Preclinical assessment of treatments using mouse models and further patient testing through prospective clinical trials are now warranted.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 11-04-2020
DOI: 10.1007/S00432-020-03211-Z
Abstract: Desmoplastic small round cell tumors (DSRCTs) are highly malignant and very rare soft tissue sarcomas with a high unmet need for new therapeutic options. Therefore, we examined poly(ADP-ribose) polymerase 1 (PARP1) and Schlafen-11 (SLFN11) expression in DSRCT tumor tissue and the combination of PARP inhibitor olaparib with the alkylating agent temozolomide (TMZ) in a preclinical DSRCT model. PARP1 and SLFN11 have been described as predictive biomarkers for response to PARP inhibition. Expression of PARP1 and SLFN11 was assessed in 16 and 12 DSRCT tumor tissue s les, respectively. Effects of single-agent olaparib, and olaparib and TMZ combination treatment were examined using the preclinical JN-DSRCT-1 model. In vitro, single-agent and combination treatment effects on cell viability, the cell cycle, DNA damage and apoptosis were examined. Olaparib and TMZ combination treatment was also assessed in vivo. PARP1 and SLFN11 expression was observed in 100% and 92% of DSRCT tumor tissues, respectively. Olaparib treatment reduced cell viability and cell migration in a dose-dependent manner in vitro. Drug synergy between olaparib and TMZ was observed in vitro and in vivo. Combination treatment led to a cell-cycle arrest and induction of DNA damage and apoptosis, even when combined at low dosages. We show high PARP1 and SLFN11 expression in DSRCT tumor material and antitumor effects following olaparib and TMZ combination treatment in a preclinical DSRCT model. This suggests that olaparib and TMZ combination treatment could be a potential treatment option for DSRCTs.
Publisher: ACM
Date: 09-05-2023
Publisher: ACM
Date: 28-07-2023
Publisher: IEEE
Date: 03-2018
Publisher: ACM
Date: 07-11-2017
Publisher: WIT Press
Date: 11-2014
DOI: 10.2495/ICCTS140041
Publisher: IEEE
Date: 04-2016
Publisher: IEEE
Date: 04-2016
Publisher: ACM
Date: 11-2015
Publisher: IEEE
Date: 24-10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: ACM
Date: 16-10-2023
Publisher: ACM
Date: 21-09-2021
Publisher: Internet Society
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 02-05-2022
Publisher: Elsevier BV
Date: 10-2020
Publisher: ACM
Date: 02-10-2023
Publisher: IEEE
Date: 10-2022
Publisher: ACM
Date: 21-09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Association for Computing Machinery (ACM)
Date: 29-02-2020
DOI: 10.1145/3375799
Abstract: Recently, Internet of Things (IoT) has raised as an important research area that combines the environmental sensing and machine learning capabilities to flourish the concept of smart spaces, in which intelligent and customized services can be provided to users in a smart manner. In smart spaces, one fundamental service that needs to be provided is accurate and unobtrusive user identification. In this work, to address this challenge, we propose a Gait Recognition as a Service (GRaaS) model, which is an instantiation of the traditional Sensing as a Service ( S 2 aaS ) model, and is specially deigned for user identification using gait in smart spaces. To illustrate the idea, a Radio Frequency Identification (RFID)-based gait recognition service is designed and implemented following the GRaaS concept. Novel tag selection algorithms and attention-based Long Short-term Memory (At-LSTM) models are designed to realize the device layer and edge layer, achieving a robust recognition with 96.3% accuracy. Extensive evaluations are provided, which show that the proposed service has accurate and robust performance and has great potential to support future smart space applications.
Publisher: Association for Computing Machinery (ACM)
Date: 09-04-2020
DOI: 10.1145/3378669
Abstract: Since ancient Greece, handshaking has been commonly practiced between two people as a friendly gesture to express trust and respect, or form a mutual agreement. In this article, we show that such physical contact can be used to bootstrap secure cyber contact between the smart devices worn by users. The key observation is that during handshaking, although belonged to two different users, the two hands involved in the shaking events are often rigidly connected, and therefore exhibit very similar motion patterns. We propose a novel key generation system, which harvests motion data during user handshaking from the wrist-worn smart devices such as smartwatches or fitness bands, and exploits the matching motion patterns to generate symmetric keys on both parties. The generated keys can be then used to establish a secure communication channel for exchanging data between devices. This provides a much more natural and user-friendly alternative for many applications, e.g., exchanging/sharing contact details, friending on social networks, or even making payments, since it doesn’t involve extra bespoke hardware, nor require the users to perform pre-defined gestures. We implement the proposed key generation system on off-the-shelf smartwatches, and extensive evaluation shows that it can reliably generate 128-bit symmetric keys just after around 1s of handshaking (with success rate %), and is resilient to different types of attacks including impersonate mimicking attacks, impersonate passive attacks, or eavesdropping attacks. Specifically, for real-time impersonate mimicking attacks, in our experiments, the Equal Error Rate (EER) is only 1.6% on average. We also show that the proposed key generation system can be extremely lightweight and is able to run in-situ on the resource-constrained smartwatches without incurring excessive resource consumption.
Publisher: ACM
Date: 11-09-2022
Publisher: Association for Computing Machinery (ACM)
Date: 08-01-2018
DOI: 10.1145/3161410
Abstract: Smart head-worn or head-mounted devices, including smart glasses and Virtual Reality (VR) headsets, are gaining popularity. Online shopping and in-app purchase from such headsets are presenting new e-commerce opportunities to the app developers. For convenience, users of these headsets may store account login, bank account and credit card details in order to perform quick in-app purchases. If the device is unattended, then an attacker, which can include insiders, can make use of the stored account and banking details to perform their own in-app purchases at the expense of the legitimate owner. To better protect the legitimate users of VR headsets (or head mounted displays in general) from such threats, in this paper, we propose to use eye movement to continuously authenticate the current wearer of the VR headset. We built a prototype device which allows us to apply visual stimuli to the wearer and to video the eye movements of the wearer at the same time. We use implicit visual stimuli (the contents of existing apps) which evoke eye movements from the headset wearer but without distracting them from their normal activities. This is so that we can continuously authenticate the wearer without them being aware of the authentication running in the background. We evaluated our proposed system experimentally with 30 subjects. Our results showed that the achievable authentication accuracy for implicit visual stimuli is comparable to that of using explicit visual stimuli. We also tested the time stability of our proposed method by collecting eye movement data on two different days that are two weeks apart. Our authentication method achieved an Equal Error Rate of 6.9% (resp. 9.7%) if data collected from the same day (resp. two weeks apart) were used for testing. In addition, we considered active impersonation attacks where attackers trying to imitate legitimate users' eye movements. We found that for a simple (resp. complex) eye tracking scene, a successful attack could be realised after on average 5.67 (13.50) attempts and our proposed authentication algorithm gave a false acceptance rate of 14.17% (3.61%). These results show that active impersonating attacks can be prevented using complex scenes and an appropriate limit on the number of authentication attempts. Lastly, we carried out a survey to study the user acceptability to our proposed implicit stimuli. We found that on a 5-point Likert scale, at least 60% of the respondents either agreed or strongly agreed that our proposed implicit stimuli were non-intrusive.
Publisher: Association for Computing Machinery (ACM)
Date: 02-01-2021
DOI: 10.1145/3429740
Abstract: Key generation is a promising technique to bootstrap secure communications for the Internet of Things devices that have no prior knowledge between each other. In the past few years, a variety of key generation protocols and systems have been proposed. In this survey, we review and categorise recent key generation systems based on a novel taxonomy. Then, we provide both quantitative and qualitative comparisons of existing approaches. We also discuss the security vulnerabilities of key generation schemes and possible countermeasures. Finally, we discuss the current challenges and point out several potential research directions.
Publisher: IEEE
Date: 04-2020
Publisher: IEEE
Date: 07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Elsevier BV
Date: 07-2023
Publisher: ACM
Date: 02-10-2023
Publisher: Elsevier BV
Date: 2022
Publisher: IEEE
Date: 05-2022
Publisher: IEEE
Date: 03-2016
Publisher: IEEE
Date: 12-2019
Publisher: Association for Computing Machinery (ACM)
Date: 29-11-2022
DOI: 10.1145/3543856
Abstract: The vast demand for erse applications raises new networking challenges, which have encouraged the development of a new paradigm of Internet of Things (IoT), e.g., LoRa. LoRa is a proprietary spread spectrum modulation technique that provides a solution for long-range and ultra-low power-consumption transmission. Due to promising prospects of LoRa, significant effort has been made on this compelling technology since its emergence. In this article, we provide a comprehensive survey of LoRa from a systematic perspective: LoRa analysis, communication, security, and its enabled applications. First, we summarize works focusing on analyzing the performance of LoRa networks. Then, we review studies enhancing the performance of LoRa networks in communication. Afterward, we analyze the security vulnerabilities and countermeasures. Finally, we survey the various LoRa-enabled applications. We also present comparisons of existing methods, together with insightful observations and inspiring future research directions.
Publisher: Elsevier BV
Date: 05-2020
Publisher: IEEE
Date: 04-2019
Publisher: Association for Computing Machinery (ACM)
Date: 29-02-2020
DOI: 10.1145/3362124
Abstract: We propose the use of the conventional energy storage component, i.e., capacitor, in the kinetic-powered wearable IoTs as the sensor to detect human activities. Since activities accumulate energy in the capacitor at different rates, the charging rate of the capacitor can be used to detect the activities. The key advantage of the proposed capacitor-based activity sensing mechanism, called CapSense, is that it obviates the need for s ling the motion signal at a high rate, and thus, significantly reduces power consumption of the wearable device. The challenge we face is that capacitors are inherently non-linear energy accumulators, which leads to significant variations in the charging rates. We solve this problem by jointly configuring the parameters of the capacitor and the associated energy harvesting circuits, which allows us to operate in the charging cycles that are approximately linear. We design and implement a kinetic-powered shoe and conduct experiments with 10 subjects. Our results show that CapSense can classify five different daily activities with 95% accuracy while consuming 57% less system power compared to conventional motion-sensor-based approaches.
Publisher: Association for Computing Machinery (ACM)
Date: 16-08-2021
DOI: 10.1145/3467980
Abstract: Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.
Publisher: IEEE
Date: 03-2017
Publisher: Elsevier BV
Date: 06-2022
Publisher: IEEE
Date: 05-2023
Publisher: ACM
Date: 18-05-2021
Publisher: ACM
Date: 16-11-2020
Publisher: IEEE
Date: 17-05-2023
Publisher: Association for Computing Machinery (ACM)
Date: 19-10-2023
DOI: 10.1145/3605780
Abstract: Solar cells have been widely used for offering energy for Internet of Things(IoT) devices. Recently, solar cells have also been used as sensors for context awareness sensing due to their sensitivity to varying lighting conditions. In this paper, we are the first to use solar cells for symmetric key generation. To generate symmetric keys, we take advantage of photovoltage measurements generated from solar cells equipped with a pair of IoT devices. Symmetric keys are essential for pairing IoT devices and further securing wireless communication. Despite the sensitivity to varying lighting conditions, challenges still remain for the use of solar cells for key generation, such as time unsynchronisation and noisy measurements. To solve these challenges, we design a novel key generation framework, SolarKey, which includes the starting point detection and a compressed sensing based two-tier key reconciliation method. Extensive experiments have been conducted to evaluate the performance of our proposed key generation method in various environments, which shows the proposed method can improve the key matching rate by up to 25%. We also conduct security analysis and the randomness test, which shows that SolarKey is resilient to common attacks such as the eavesdropping attack and the imitating attack and sufficiently random.
Publisher: MDPI AG
Date: 29-08-2021
DOI: 10.3390/S21175818
Abstract: With the recent advances in deep learning, wearable sensors have increasingly been used in automated animal activity recognition. However, there are two major challenges in improving recognition performance—multi-modal feature fusion and imbalanced data modeling. In this study, to improve classification performance for equine activities while tackling these two challenges, we developed a cross-modality interaction network (CMI-Net) involving a dual convolution neural network architecture and a cross-modality interaction module (CMIM). The CMIM adaptively recalibrated the temporal- and axis-wise features in each modality by leveraging multi-modal information to achieve deep intermodality interaction. A class-balanced (CB) focal loss was adopted to supervise the training of CMI-Net to alleviate the class imbalance problem. Motion data was acquired from six neck-attached inertial measurement units from six horses. The CMI-Net was trained and verified with leave-one-out cross-validation. The results demonstrated that our CMI-Net outperformed the existing algorithms with high precision (79.74%), recall (79.57%), F1-score (79.02%), and accuracy (93.37%). The adoption of CB focal loss improved the performance of CMI-Net, with increases of 2.76%, 4.16%, and 3.92% in precision, recall, and F1-score, respectively. In conclusion, CMI-Net and CB focal loss effectively enhanced the equine activity classification performance using imbalanced multi-modal sensor data.
Publisher: IEEE
Date: 04-2018
Publisher: IEEE
Date: 12-2012
Publisher: ACM
Date: 11-2015
Publisher: IEEE
Date: 03-2016
Publisher: American Association for Cancer Research (AACR)
Date: 14-12-2017
DOI: 10.1158/0008-5472.CAN-17-2056
Abstract: Synovial sarcoma (SS) is an aggressive soft-tissue malignancy characterized by expression of SS18–SSX fusions, where treatment options are limited. To identify therapeutically actionable genetic dependencies in SS, we performed a series of parallel, high-throughput small interfering RNA (siRNA) screens and compared genetic dependencies in SS tumor cells with those in & non–SS tumor cell lines. This approach revealed a reliance of SS tumor cells upon the DNA damage response serine/threonine protein kinase ATR. Clinical ATR inhibitors (ATRi) elicited a synthetic lethal effect in SS tumor cells and impaired growth of SS patient-derived xenografts. Oncogenic SS18–SSX family fusion genes are known to alter the composition of the BAF chromatin–remodeling complex, causing ejection and degradation of wild-type SS18 and the tumor suppressor SMARCB1. Expression of oncogenic SS18–SSX fusion proteins caused profound ATRi sensitivity and a reduction in SS18 and SMARCB1 protein levels, but an SSX18–SSX1 Δ71–78 fusion containing a C-terminal deletion did not. ATRi sensitivity in SS was characterized by an increase in biomarkers of replication fork stress (increased γH2AX, decreased replication fork speed, and increased R-loops), an apoptotic response, and a dependence upon cyclin E expression. Combinations of cisplatin or PARP inhibitors enhanced the antitumor cell effect of ATRi, suggesting that either single-agent ATRi or combination therapy involving ATRi might be further assessed as candidate approaches for SS treatment. Cancer Res 77(24) 7014–26. ©2017 AACR.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: IEEE
Date: 08-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: Association for Computing Machinery (ACM)
Date: 18-03-2020
DOI: 10.1145/3381754
Abstract: For kinetic-powered body area networks, we explore the feasibility of converting energy harvesting patterns for device authentication and symmetric secret keys generation continuously. The intuition is that at any given time, multiple wearable devices harvest kinetic energy from the same user activity, such as walking, which allows them to independently observe a common secret energy harvesting pattern not accessible to outside devices. Such continuous KEH-based authentication and key generation is expected to be highly power efficient as it obviates the need to employ any extra sensors, such as accelerometer, to precisely track the walking patterns. Unfortunately, lack of precise activity tracking introduces bit mismatches between the independently generated keys, which makes KEH-based authentication and symmetric key generation a challenging problem. We propose KEHKey, a KEH-based authentication and key generation system that employs a compressive sensing-based information reconciliation protocol for wearable devices to effectively correct any mismatches in generated keys. We implement KEHKey using off-the-shelf piezoelectric energy harvesting products and evaluate its performance with data collected from 24 subjects wearing the devices on different body locations including head, torso and hands. Our results show that KEHKey is able to generate the same key for two KEH-embedded devices at a speed of 12.57 bps while reducing energy consumption by 59% compared to accelerometer-based methods, which makes it suitable for continuous operation. Finally, we demonstrate that KEHKey can successfully defend against typical adversarial attacks. In particular, KEHKey is found to be more resilient to video side channel attacks than its accelerometer-based counterparts.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Weitao Xu.