Visible to the public Biblio

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2023-06-16
Xiao, Renjie, Yuan, Yong'an, Tan, Zijing, Ma, Shuai, Wang, Wei.  2022.  Dynamic Functional Dependency Discovery with Dynamic Hitting Set Enumeration. 2022 IEEE 38th International Conference on Data Engineering (ICDE). :286—298.
Functional dependencies (FDs) are widely applied in data management tasks. Since FDs on data are usually unknown, FD discovery techniques are studied for automatically finding hidden FDs from data. In this paper, we develop techniques to dynamically discover FDs in response to changes on data. Formally, given the complete set Σ of minimal and valid FDs on a relational instance r, we aim to find the complete set Σ$^\textrm\textbackslashprime$ of minimal and valid FDs on røplus\textbackslashDelta r, where \textbackslashDelta r is a set of tuple insertions and deletions. Different from the batch approaches that compute Σ$^\textrm\textbackslashprime$ on røplus\textbackslashDelta r from scratch, our dynamic method computes Σ$^\textrm\textbackslashprime$ in response to \textbackslashtriangle\textbackslashuparrow. by leveraging the known Σ on r, and avoids processing the whole of r for each update from \textbackslashDelta r. We tackle dynamic FD discovery on røplus\textbackslashDelta r by dynamic hitting set enumeration on the difference-set of røplus\textbackslashDelta r. Specifically, (1) leveraging auxiliary structures built on r, we first present an efficient algorithm to update the difference-set of r to that of røplus\textbackslashDelta r. (2) We then compute Σ$^\textrm\textbackslashprime$, by recasting dynamic FD discovery as dynamic hitting set enumeration on the difference-set of røplus\textbackslashDelta r and developing novel techniques for dynamic hitting set enumeration. (3) We finally experimentally verify the effectiveness and efficiency of our approaches, using real-life and synthetic data. The results show that our dynamic FD discovery method outperforms the batch counterparts on most tested data, even when \textbackslashDelta r is up to 30 % of r.
2023-02-17
Lu, Shaofeng, Lv, Chengzhe, Wang, Wei, Xu, Changqing, Fan, Huadan, Lu, Yuefeng, Hu, Yulong, Li, Wenxi.  2022.  Secret Numerical Interval Decision Protocol for Protecting Private Information and Its Application. 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). :726–731.
Cooperative secure computing based on the relationship between numerical value and numerical interval is not only the basic problems of secure multiparty computing but also the core problems of cooperative secure computing. It is of substantial theoretical and practical significance for information security in relation to scientific computing to continuously investigate and construct solutions to such problems. Based on the Goldwasser-Micali homomorphic encryption scheme, this paper propose the Morton rule, according to the characteristics of the interval, a double-length vector is constructed to participate in the exclusive-or operation, and an efficient cooperative decision-making solution for integer and integer interval security is designed. This solution can solve more basic problems in cooperative security computation after suitable transformations. A theoretical analysis shows that this solution is safe and efficient. Finally, applications that are based on these protocols are presented.
2023-01-20
Yu, Yue, Yao, Jiming, Wang, Wei, Qiu, Lanxin, Xu, Yangzhou.  2022.  A Lightweight Identity-Based Secondary Authentication Method in Smart Grid. 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 10:2190—2195.
5G network slicing plays a key role in the smart grid business. The existing authentication schemes for 5G slicing in smart grids require high computing costs, so they are time-consuming and do not fully consider the security of authentication. Aiming at the application scenario of 5G smart grid, this paper proposes an identity-based lightweight secondary authentication scheme. Compared with other well-known methods, in the protocol interaction of this paper, both the user Ui and the grid server can authenticate each other's identities, thereby preventing illegal users from pretending to be identities. The grid user Ui and the grid server can complete the authentication process without resorting to complex bilinear mapping calculations, so the computational overhead is small. The grid user and grid server can complete the authentication process without transmitting the original identification. Therefore, this scheme has the feature of anonymous authentication. In this solution, the authentication process does not require infrastructure such as PKI, so the deployment is simple. Experimental results show that the protocol is feasible in practical applications
Yao, Jiming, Wu, Peng, Chen, Duanyun, Wang, Wei, Fang, Youxu.  2022.  A security scheme for network slicing selection based on Pohlig-Hellman algorithm in smart grid. 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 10:906—910.
5G has significantly facilitated the development of attractive applications such as autonomous driving and telemedicine due to its lower latency, higher data rates, and enormous connectivity. However, there are still some security and privacy issues in 5G, such as network slicing privacy and flexibility and efficiency of network slicing selection. In the smart grid scenario, this paper proposes a 5G slice selection security scheme based on the Pohlig-Hellman algorithm, which realizes the protection of slice selection privacy data between User i(Ui) and Access and Mobility Management function (AMF), so that the data will not be exposed to third-party attackers. Compared with other schemes, the scheme proposed in this paper is simple in deployment, low in computational overhead, and simple in process, and does not require the help of PKI system. The security analysis also verifies that the scheme can accurately protect the slice selection privacy data between Ui and AMF.
Wang, Wei, Yao, Jiming, Shao, Weiping, Xu, Yangzhou, Peng, Shaowu.  2022.  Efficient 5G Network Slicing Selection with Privacy in Smart Grid. 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 10:916—922.
To fulfill different requirements from various services, the smart grid typically uses 5G network slicing technique for splitting the physical network into multiple virtual logical networks. By doing so, end users in smart grid can select appropriate slice that is suitable for their services. Privacy has vital significance in network slicing selection, since both the end user and the network entities are afraid that their sensitive slicing features are leaked to an adversary. At the same time, in the smart grid, there are many low-power users who are not suitable for complex security schemes. Therefore, both security and efficiency are basic requirements for 5G slicing selection schemes. Considering both security and efficiency, we propose a 5G slicing selection security scheme based on matching degree estimation, called SS-MDE. In SS-MDE, a set of random numbers is used to hide the feature information of the end user and the AMF which can provide privacy protection for exchanged slicing features. Moreover, the best matching slice is selected by calculating the Euclid distance between two slices. Since the algorithms used in SS-MDE include only several simple mathematical operations, which are quite lightweight, SS-MDE can achieve high efficiency. At the same time, since third-party attackers cannot extract the slicing information, SS-MDE can fulfill security requirements. Experimental results show that the proposed scheme is feasible in real world applications.
2021-12-20
Liu, Jiawei, Liu, Quanli, Wang, Wei, Wang, Xiao- Lei.  2021.  An Improved MLMS Algorithm with Prediction Error Method for Adaptive Feedback Cancellation. 2021 International Conference on Security, Pattern Analysis, and Cybernetics(SPAC). :397–401.
Adaptive feedback cancellation (AFC) method is widely adopted for the purpose of reducing the adverse effects of acoustic feedback on the sound reinforcement systems. However, since the existence of forward path results in the correlation between the source signal and the feedback signal, the source signal is mistakenly considered as the feedback signal to be eliminated by adaptive filter when it is colored, which leads to a inaccurate prediction of the acoustic feedback signal. In order to solve this problem, prediction error method is introduced in this paper to remove the correlation between the source signal and the feedback signal. Aiming at the dilemma of Modified Least Mean Square (MLMS) algorithm in choosing between prediction speed and prediction accuracy, an improved MLMS algorithm with a variable step-size scheme is proposed. Simulation examples are applied to show that the proposed algorithm can obtain more accurate prediction of acoustic feedback signal in a shorter time than the MLMS algorithm.
2021-11-29
Li, Taojin, Lei, Songgui, Wang, Wei, Wang, Qingli.  2020.  Research on MR virtual scene location method based on image recognition. 2020 International Conference on Information Science, Parallel and Distributed Systems (ISPDS). :109–113.
In order to solve the problem of accurate positioning of mixed reality virtual scene in physical space, this paper, firstly, analyzes the positioning principle of mixed reality virtual scene. Secondly, based on the comparison among the three developer kits: ARToolKit, ARTag, and Vuforia and two image optimization algorithms: AHE and ACE, it makes sure to use Vuforia development tool to complete the signature-based tracking and registration task, and use ACE algorithm to optimize the signature-based image. It improves the efficiency, stability and accuracy of image recognition registration. Then the multi-target recognition and registration technology is used to realize the multi-location of virtual scene. Finally, Hololens glasses are used as the hardware carrier to verify the above method. The experimental results show that the above method not only realizes the precise location of MR virtual scene based on image recognition, but also ensures the absolute position of the virtual model in the real space, bringing users a more real virtual experience. Keywords-mixed reality, multi-person collaboration, virtual positioning, gesture interaction.
2021-11-08
Li, Gao, Xu, Jianliang, Shen, Weiguo, Wang, Wei, Liu, Zitong, Ding, Guoru.  2020.  LSTM-based Frequency Hopping Sequence Prediction. 2020 International Conference on Wireless Communications and Signal Processing (WCSP). :472–477.
The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication. The core of this communication process is the frequency-hopping (FH) sequence with pseudo-random characteristics, which controls carrier frequency hopping. However, FH sequence is always generated by a certain model and is a kind of time sequence with certain regularity. Long Short-Term Memory (LSTM) neural network in deep learning has been proved to have strong ability to solve time series problems. Therefore, in this paper, we establish LSTM model to implement FH sequence prediction. The simulation results show that LSTM-based scheme can effectively predict frequency point by point based on historical HF frequency data. Further, we achieve frequency interval prediction based on frequency point prediction.
2021-09-30
Wang, Wei, Liu, Tieyuan, Chang, Liang, Gu, Tianlong, Zhao, Xuemei.  2020.  Convolutional Recurrent Neural Networks for Knowledge Tracing. 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :287–290.
Knowledge Tracing (KT) is a task that aims to assess students' mastery level of knowledge and predict their performance over questions, which has attracted widespread attention over the years. Recently, an increasing number of researches have applied deep learning techniques to knowledge tracing and have made a huge success over traditional Bayesian Knowledge Tracing methods. Most existing deep learning-based methods utilized either Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs). However, it is worth noticing that these two sorts of models are complementary in modeling abilities. Thus, in this paper, we propose a novel knowledge tracing model by taking advantage of both two models via combining them into a single integrated model, named Convolutional Recurrent Knowledge Tracing (CRKT). Extensive experiments show that our model outperforms the state-of-the-art models in multiple KT datasets.
2021-09-16
Asci, Cihan, Wang, Wei, Sonkusale, Sameer.  2020.  Security Monitoring System Using Magnetically-Activated RFID Tags. 2020 IEEE SENSORS. :1–4.
Existing methods for home security monitoring depend on expensive custom battery-powered solutions. In this article, we present a battery-free solution that leverages any off-the-shelf passive radio frequency identification (RFID) tag for real-time entry detection. Sensor consists of a printed RFID antenna on paper, coupled to a magnetic reed switch and is affixed on the door. Opening of the door triggers the reed switch causing RFID signal transmission detected by any off-the-shelf passive RFID reader. This paper shows simulation and experimental results for such magnetically-actuated RFID (or magRFID) opening sensor.
2021-05-13
Sheng, Mingren, Liu, Hongri, Yang, Xu, Wang, Wei, Huang, Junheng, Wang, Bailing.  2020.  Network Security Situation Prediction in Software Defined Networking Data Plane. 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA). :475–479.
Software-Defined Networking (SDN) simplifies network management by separating the control plane from the data forwarding plane. However, the plane separation technology introduces many new loopholes in the SDN data plane. In order to facilitate taking proactive measures to reduce the damage degree of network security events, this paper proposes a security situation prediction method based on particle swarm optimization algorithm and long-short-term memory neural network for network security events on the SDN data plane. According to the statistical information of the security incident, the analytic hierarchy process is used to calculate the SDN data plane security situation risk value. Then use the historical data of the security situation risk value to build an artificial neural network prediction model. Finally, a prediction model is used to predict the future security situation risk value. Experiments show that this method has good prediction accuracy and stability.
2020-08-03
Dai, Haipeng, Liu, Alex X., Li, Zeshui, Wang, Wei, Zhang, Fengmin, Dong, Chao.  2019.  Recognizing Driver Talking Direction in Running Vehicles with a Smartphone. 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). :10–18.
This paper addresses the fundamental problem of identifying driver talking directions using a single smartphone, which can help drivers by warning distraction of having conversations with passengers in a vehicle and enable safety enhancement. The basic idea of our system is to perform talking status and direction identification using two microphones on a smartphone. We first use the sound recorded by the two microphones to identify whether the driver is talking or not. If yes, we then extract the so-called channel fingerprint from the speech signal and classify it into one of three typical driver talking directions, namely, front, right and back, using a trained model obtained in advance. The key novelty of our scheme is the proposition of channel fingerprint which leverages the heavy multipath effects in the harsh in-vehicle environment and cancels the variability of human voice, both of which combine to invalidate traditional TDoA, DoA and fingerprint based sound source localization approaches. We conducted extensive experiments using two kinds of phones and two vehicles for four phone placements in three representative scenarios, and collected 23 hours voice data from 20 participants. The results show that our system can achieve 95.0% classification accuracy on average.
2020-07-24
CUI, A-jun, Fu, Jia-yu, Wang, Wei, Zhang, Hua-feng.  2019.  Construction of Network Active Security Threat Model Based on Offensive and Defensive Differential Game. 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA). :289—294.
Aiming at the shortcomings of the traditional network active security threat model that cannot continuously control the threat process, a network active security threat model based on offensive and defensive differential game is constructed. The attack and defense differential game theory is used to define the parameters of the network active security threat model, on this basis, the network security target is determined, the network active security threat is identified by the attack defense differential equation, and finally the network active security threat is quantitatively evaluated, thus construction of network active security threat model based on offensive and defensive differential game is completed. The experimental results show that compared with the traditional network active security threat model, the proposed model is more feasible in the attack and defense control of the network active security threat process, and can achieve the ideal application effect.
Wang, Wei, Zhang, Guidong, Shen, Yongjun.  2018.  A CP-ABE Scheme Supporting Attribute Revocation and Policy Hiding in Outsourced Environment. 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). :96—99.
Aiming at the increasing popularity of mobile terminals, a CP-ABE scheme adapted to lightweight decryption at the mobile end is proposed. The scheme has the function of supporting timely attributes revocation and policy hiding. Firstly, we will introduce the related knowledge of attribute base encryption. After that, we will give a specific CP-ABE solution. Finally, in the part of the algorithm analysis, we will give analysis performance and related security, and compare this algorithm with other algorithms.
2020-05-08
Shen, Weiguo, Wang, Wei.  2018.  Node Identification in Wireless Network Based on Convolutional Neural Network. 2018 14th International Conference on Computational Intelligence and Security (CIS). :238—241.
Aiming at the problem of node identification in wireless networks, a method of node identification based on deep learning is proposed, which starts with the tiny features of nodes in radiofrequency layer. Firstly, in order to cut down the computational complexity, Principal Component Analysis is used to reduce the dimension of node sample data. Secondly, a convolution neural network containing two hidden layers is designed to extract local features of the preprocessed data. Stochastic gradient descent method is used to optimize the parameters, and the Softmax Model is used to determine the output label. Finally, the effectiveness of the method is verified by experiments on practical wireless ad-hoc network.
2020-01-28
Xuan, Shichang, Wang, Huanhong, Gao, Duo, Chung, Ilyong, Wang, Wei, Yang, Wu.  2019.  Network Penetration Identification Method Based on Interactive Behavior Analysis. 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD). :210–215.

The Internet has gradually penetrated into the national economy, politics, culture, military, education and other fields. Due to its openness, interconnectivity and other characteristics, the Internet is vulnerable to all kinds of malicious attacks. The research uses a honeynet to collect attacker information, and proposes a network penetration recognition technology based on interactive behavior analysis. Using Sebek technology to capture the attacker's keystroke record, time series modeling of the keystroke sequences of the interaction behavior is proposed, using a Recurrent Neural Network. The attack recognition method is constructed by using Long Short-Term Memory that solves the problem of gradient disappearance, gradient explosion and long-term memory shortage in ordinary Recurrent Neural Network. Finally, the experiment verifies that the short-short time memory network has a high accuracy rate for the recognition of penetration attacks.

2019-11-27
Gao, Yang, Li, Borui, Wang, Wei, Xu, Wenyao, Zhou, Chi, Jin, Zhanpeng.  2018.  Watching and Safeguarding Your 3D Printer: Online Process Monitoring Against Cyber-Physical Attacks. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.. 2:108:1–108:27.

The increasing adoption of 3D printing in many safety and mission critical applications exposes 3D printers to a variety of cyber attacks that may result in catastrophic consequences if the printing process is compromised. For example, the mechanical properties (e.g., physical strength, thermal resistance, dimensional stability) of 3D printed objects could be significantly affected and degraded if a simple printing setting is maliciously changed. To address this challenge, this study proposes a model-free real-time online process monitoring approach that is capable of detecting and defending against the cyber-physical attacks on the firmwares of 3D printers. Specifically, we explore the potential attacks and consequences of four key printing attributes (including infill path, printing speed, layer thickness, and fan speed) and then formulate the attack models. Based on the intrinsic relation between the printing attributes and the physical observations, our defense model is established by systematically analyzing the multi-faceted, real-time measurement collected from the accelerometer, magnetometer and camera. The Kalman filter and Canny filter are used to map and estimate three aforementioned critical toolpath information that might affect the printing quality. Mel-frequency Cepstrum Coefficients are used to extract features for fan speed estimation. Experimental results show that, for a complex 3D printed design, our method can achieve 4% Hausdorff distance compared with the model dimension for infill path estimate, 6.07% Mean Absolute Percentage Error (MAPE) for speed estimate, 9.57% MAPE for layer thickness estimate, and 96.8% accuracy for fan speed identification. Our study demonstrates that, this new approach can effectively defend against the cyber-physical attacks on 3D printers and 3D printing process.

2019-05-01
Yu, Wenchao, Cheng, Wei, Aggarwal, Charu C., Zhang, Kai, Chen, Haifeng, Wang, Wei.  2018.  NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :2672-2681.

Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose "behaviors'' deviate from underlying majority of the network, in a real-time fashion. Recently, network embedding has proven a powerful tool in learning the low-dimensional representations of vertices in networks that can capture and preserve the network structure. However, most existing network embedding approaches are designed for static networks, and thus may not be perfectly suited for a dynamic environment in which the network representation has to be constantly updated. In this paper, we propose a novel approach, NetWalk, for anomaly detection in dynamic networks by learning network representations which can be updated dynamically as the network evolves. We first encode the vertices of the dynamic network to vector representations by clique embedding, which jointly minimizes the pairwise distance of vertex representations of each walk derived from the dynamic networks, and the deep autoencoder reconstruction error serving as a global regularization. The vector representations can be computed with constant space requirements using reservoir sampling. On the basis of the learned low-dimensional vertex representations, a clustering-based technique is employed to incrementally and dynamically detect network anomalies. Compared with existing approaches, NetWalk has several advantages: 1) the network embedding can be updated dynamically, 2) streaming network nodes and edges can be encoded efficiently with constant memory space usage, 3) flexible to be applied on different types of networks, and 4) network anomalies can be detected in real-time. Extensive experiments on four real datasets demonstrate the effectiveness of NetWalk.

Yu, Wenchao, Zheng, Cheng, Cheng, Wei, Aggarwal, Charu C., Song, Dongjin, Zong, Bo, Chen, Haifeng, Wang, Wei.  2018.  Learning Deep Network Representations with Adversarially Regularized Autoencoders. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :2663-2671.

The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure. Most existing network embedding models, with shallow or deep architectures, learn vertex representations from the sampled vertex sequences such that the low-dimensional embeddings preserve the locality property and/or global reconstruction capability. The resultant representations, however, are difficult for model generalization due to the intrinsic sparsity of sampled sequences from the input network. As such, an ideal approach to address the problem is to generate vertex representations by learning a probability density function over the sampled sequences. However, in many cases, such a distribution in a low-dimensional manifold may not always have an analytic form. In this study, we propose to learn the network representations with adversarially regularized autoencoders (NetRA). NetRA learns smoothly regularized vertex representations that well capture the network structure through jointly considering both locality-preserving and global reconstruction constraints. The joint inference is encapsulated in a generative adversarial training process to circumvent the requirement of an explicit prior distribution, and thus obtains better generalization performance. We demonstrate empirically how well key properties of the network structure are captured and the effectiveness of NetRA on a variety of tasks, including network reconstruction, link prediction, and multi-label classification.

2019-02-18
Wu, Siyan, Tong, Xiaojun, Wang, Wei, Xin, Guodong, Wang, Bailing, Zhou, Qi.  2018.  Website Defacements Detection Based on Support Vector Machine Classification Method. Proceedings of the 2018 International Conference on Computing and Data Engineering. :62–66.
Website defacements can inflict significant harm on the website owner through the loss of reputation, the loss of money, or the leakage of information. Due to the complexity and diversity of all kinds of web application systems, especially a lack of necessary security maintenance, website defacements increased year by year. In this paper, we focus on detecting whether the website has been defaced by extracting website features and website embedded trojan features. We use three kinds of classification learning algorithms which include Gradient Boosting Decision Tree (GBDT), Random Forest (RF) and Support Vector Machine (SVM) to do the classification experiments, and experimental results show that Support Vector Machine classifier performed better than two other classifiers. It can achieve an overall accuracy of 95%-96% in detecting website defacements.
2017-10-27
Xu, Peng, Li, Jingnan, Wang, Wei, Jin, Hai.  2016.  Anonymous Identity-Based Broadcast Encryption with Constant Decryption Complexity and Strong Security. Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. :223–233.
Anonymous Identity-Based Broadcast Encryption (AIBBE) allows a sender to broadcast a ciphertext to multi-receivers, and keeps receivers' anonymity. The existing AIBBE schemes fail to achieve efficient decryption or strong security, like the constant decryption complexity, the security under the adaptive attack, or the security in the standard model. Hence, we propose two new AIBBE schemes to overcome the drawbacks of previous schemes in the state-of-art. The biggest contribution in our work is the proposed AIBBE scheme with constant decryption complexity and the provable security under the adaptive attack in the standard model. This scheme should be the first one to obtain advantages in all above mentioned aspects, and has sufficient contribution in theory due to its strong security. We also propose another AIBBE scheme in the Random Oracle (RO) model, which is of sufficient interest in practice due to our experiment.
Xu, Peng, Xu, Jun, Wang, Wei, Jin, Hai, Susilo, Willy, Zou, Deqing.  2016.  Generally Hybrid Proxy Re-Encryption: A Secure Data Sharing Among Cryptographic Clouds. Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. :913–918.

Proxy Re-Encryption (PRE) is a favorable primitive to realize a cryptographic cloud with secure and flexible data sharing mechanism. A number of PRE schemes with versatile capabilities have been proposed for different applications. The secure data sharing can be internally achieved in each PRE scheme. But no previous work can guarantee the secure data sharing among different PRE schemes in a general manner. Moreover, it is challenging to solve this problem due to huge differences among the existing PRE schemes in their algebraic systems and public-key types. To solve this problem more generally, this paper uniforms the definitions of the existing PRE and Public Key Encryption (PKE) schemes, and further uniforms their security definitions. Then taking any uniformly defined PRE scheme and any uniformly defined PKE scheme as two building blocks, this paper constructs a Generally Hybrid Proxy Re-Encryption (GHPRE) scheme with the idea of temporary public and private keys to achieve secure data sharing between these two underlying schemes. Since PKE is a more general definition than PRE, the proposed GHPRE scheme also is workable between any two PRE schemes. Moreover, the proposed GHPRE scheme can be transparently deployed even if the underlying PRE schemes are implementing.

2017-09-15
Cheng, Wei, Zhang, Kai, Chen, Haifeng, Jiang, Guofei, Chen, Zhengzhang, Wang, Wei.  2016.  Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :805–814.

Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies thus attracts significant amount of interest in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be a powerful way in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: 1) fault propagation in the network is ignored; 2) the root casual anomalies may not always be the nodes with a high-percentage of vanishing correlations; 3) temporal patterns of vanishing correlations are not exploited for robust detection. To address these limitations, in this paper we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network, and can perform joint inference on both the structural, and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations, and can compensate for unstructured measurement noise in the system. Extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets demonstrate the effectiveness of our approach.

Gu, Zhaoyu, Wang, Wei, Wang, Guoyu.  2016.  HRRP Reconstruction of Sub-Nyquist Sampled Chirp Signals with CS-based Dechirping. Proceedings of the 8th International Conference on Signal Processing Systems. :123–126.

Benefiting bythe large time-bandwidth product, chirp signals arefrequentlyadopted in modern radars. In this paper, the influence on thehigh-resolution range profile (HRRP) reconstruction of chirp waveform after sub-Nyquist sampling is investigated, where the (compressive sensing) CS-based dechirpingalgorithms are applied to achieve the range compression of the sub-Nyquist sampled chirp signals. The conditions that the HRRP can be recovered from the sub-Nyquist sampled chirp signals via CS-based dechirping are addressed. The simulated echoes, formed by the sub-Nyquist sampled chirp signals and scattered by moving targets, are collected by radars to yieldthe high-resolution range profile (HRRP) which validate the correctness of the analyses.

2017-09-05
Wang, Wei, Yang, Lin, Zhang, Qian.  2016.  Touch-and-guard: Secure Pairing Through Hand Resonance. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. :670–681.

Securely pairing wearables with another device is the key to many promising applications, such as mobile payment, sensitive data transfer and secure interactions with smart home devices. This paper presents Touch-And-Guard (TAG), a system that uses hand touch as an intuitive manner to establish a secure connection between a wristband wearable and the touched device. It generates secret bits from hand resonant properties, which are obtained using accelerometers and vibration motors. The extracted secret bits are used by both sides to authenticate each other and then communicate confidentially. The ubiquity of accelerometers and motors presents an immediate market for our system. We demonstrate the feasibility of our system using an experimental prototype and conduct experiments involving 12 participants with 1440 trials. The results indicate that we can generate secret bits at a rate of 7.84 bit/s, which is 58% faster than conventional text input PIN authentication. We also show that our system is resistant to acoustic eavesdroppers in proximity.