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Lin, B., Chen, X., Wang, L..  2017.  A Cloud-Based Trust Evaluation Scheme Using a Vehicular Social Network Environment. 2017 24th Asia-Pacific Software Engineering Conference (APSEC). :120–129.

New generation communication technologies (e.g., 5G) enhance interactions in mobile and wireless communication networks between devices by supporting a large-scale data sharing. The vehicle is such kind of device that benefits from these technologies, so vehicles become a significant component of vehicular networks. Thus, as a classic application of Internet of Things (IoT), the vehicular network can provide more information services for its human users, which makes the vehicular network more socialized. A new concept is then formed, namely "Vehicular Social Networks (VSNs)", which bring both benefits of data sharing and challenges of security. Traditional public key infrastructures (PKI) can guarantee user identity authentication in the network; however, PKI cannot distinguish untrustworthy information from authorized users. For this reason, a trust evaluation mechanism is required to guarantee the trustworthiness of information by distinguishing malicious users from networks. Hence, this paper explores a trust evaluation algorithm for VSNs and proposes a cloud-based VSN architecture to implement the trust algorithm. Experiments are conducted to investigate the performance of trust algorithm in a vehicular network environment through building a three-layer VSN model. Simulation results reveal that the trust algorithm can be efficiently implemented by the proposed three-layer model.

Ullah, A., Chen, X., Yang, J..  2020.  Design and Implementation of MobilityFirst Future Internet Testbed. 2020 3rd International Conference on Hot Information-Centric Networking (HotICN). :170—174.

Recently, Future Internet research has attracted enormous attentions towards the design of clean slate Future Internet Architecture. A large number of research projects has been established by National Science Foundation's (NSF), Future Internet Architecture (FIA) program in this area. One of these projects is MobilityFirst, which recognizes the predominance of mobile networking and aims to address the challenges of this paradigm shift. Future Internet Architecture Projects, are usually deploying on large scale experimental networks for testing and evaluating the properties of new architecture and protocols. Currently only some specific experiments, like routing and name resolution scalability in MobilityFirst architecture has been performed over the ORBIT and GENI platforms. However, to move from this experimental networking to technology trials with real-world users and applications deployment of alternative testbeds are necessary. In this paper, MobilityFirst Future Internet testbed is designed and deployed on Future Networks Laboratory, University of Science and Technology of China, China. Which provides a realistic environment for MobilityFirst experiments. Next, in this paper, for MF traffic transmission between MobilityFirst networks through current networking protocols (TCP), MobilityFirst Proxies are designed and implemented. Furthermore, the results and experience obtained from experiments over proposed testbed are presented.

Guo, C., Chen, X., Jie, Y., Zhangjie, F., Li, M., Feng, B..  2017.  Dynamic Multi-phrase Ranked Search over Encrypted Data with Symmetric Searchable Encryption. IEEE Transactions on Services Computing. PP:1–1.

As cloud computing becomes prevalent, more and more data owners are likely to outsource their data to a cloud server. However, to ensure privacy, the data should be encrypted before outsourcing. Symmetric searchable encryption allows users to retrieve keyword over encrypted data without decrypting the data. Many existing schemes that are based on symmetric searchable encryption only support single keyword search, conjunctive keywords search, multiple keywords search, or single phrase search. However, some schemes, i.e., static schemes, only search one phrase in a query request. In this paper, we propose a multi-phrase ranked search over encrypted cloud data, which also supports dynamic update operations, such as adding or deleting files. We used an inverted index to record the locations of keywords and to judge whether the phrase appears. This index can search for keywords efficiently. In order to rank the results and protect the privacy of relevance score, the relevance score evaluation model is used in searching process on client-side. Also, the special construction of the index makes the scheme dynamic. The data owner can update the cloud data at very little cost. Security analyses and extensive experiments were conducted to demonstrate the safety and efficiency of the proposed scheme.

Xu, P., Miao, Q., Liu, T., Chen, X..  2015.  Multi-direction Edge Detection Operator. 2015 11th International Conference on Computational Intelligence and Security (CIS). :187—190.

Due to the noise in the images, the edges extracted from these noisy images are always discontinuous and inaccurate by traditional operators. In order to solve these problems, this paper proposes multi-direction edge detection operator to detect edges from noisy images. The new operator is designed by introducing the shear transformation into the traditional operator. On the one hand, the shear transformation can provide a more favorable treatment for directions, which can make the new operator detect edges in different directions and overcome the directional limitation in the traditional operator. On the other hand, all the single pixel edge images in different directions can be fused. In this case, the edge information can complement each other. The experimental results indicate that the new operator is superior to the traditional ones in terms of the effectiveness of edge detection and the ability of noise rejection.

Wu, L., Chen, X., Meng, L., Meng, X..  2020.  Multitask Adversarial Learning for Chinese Font Style Transfer. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
Style transfer between Chinese fonts is challenging due to both the complexity of Chinese characters and the significant difference between fonts. Existing algorithms for this task typically learn a mapping between the reference and target fonts for each character. Subsequently, this mapping is used to generate the characters that do not exist in the target font. However, the characters available for training are unlikely to cover all fine-grained parts of the missing characters, leading to the overfitting problem. As a result, the generated characters of the target font may suffer problems of incomplete or even radicals and dirty dots. To address this problem, this paper presents a multi-task adversarial learning approach, termed MTfontGAN, to generate more vivid Chinese characters. MTfontGAN learns to transfer a reference font to multiple target ones simultaneously. An alignment is imposed on the encoders of different tasks to make them focus on the important parts of the characters in general style transfer. Such cross-task interactions at the feature level effectively improve the generalization capability of MTfontGAN. The performance of MTfontGAN is evaluated on three Chinese font datasets. Experimental results show that MTfontGAN outperforms the state-of-the-art algorithms in a single-task setting. More importantly, increasing the number of tasks leads to better performance in all of them.
Chen, X., Cao, C., Mai, J..  2020.  Network Anomaly Detection Based on Deep Support Vector Data Description. 2020 5th IEEE International Conference on Big Data Analytics (ICBDA). :251–255.
Intrusion detection system based on representation learning is the main research direction in the field of anomaly detection. Malicious traffic detection system can distinguish normal and malicious traffic by learning representations between normal and malicious traffic. However, under the context of big data, there are many types of malicious traffic, and the features are also changing constantly. It is still a urgent problem to design a detection model that can effectively learn and summarize the feature of normal traffic and accurately identify the features of new kinds of malicious this paper, a malicious traffic detection method based on Deep Support Vector Data Description is proposed, which is called Deep - SVDD. We combine convolutional neural network (CNN) with support vector data description, and train the model with normal traffic. The normal traffic features are mapped to high-dimensional space through neural networks, and a compact hypersphere is trained by unsupervised learning, which includes the normal features of the highdimensional space. Malicious traffic fall outside the hypersphere, thus distinguishing between normal and malicious traffic. Experiments show that the model has a high detection rate and a low false alarm rate, and it can effectively identify new malicious traffic.
Zong, Y., Guo, Y., Chen, X..  2019.  Policy-Based Access Control for Robotic Applications. 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE). :368—3685.

With the wide application of modern robots, more concerns have been raised on security and privacy of robotic systems and applications. Although the Robot Operating System (ROS) is commonly used on different robots, there have been few work considering the security aspects of ROS. As ROS does not employ even the basic permission control mechanism, applications can access any resources without limitation, which could result in equipment damage, harm to human, as well as privacy leakage. In this paper we propose an access control mechanism for ROS based on an extended policy-based access control (PBAC) model. Specifically, we extend ROS to add an additional node dedicated for access control so that it can provide user identity and permission management services. The proposed mechanism also allows the administrator to revoke a permission dynamically. We implemented the proposed method in ROS and demonstrated its applicability and performance through several case studies.

YE, X., JI, B., Chen, X., QIAN, D., Zhao, Z..  2020.  Probability Boltzmann Machine Network for Face Detection on Video. 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). :138—147.

By the multi-layer nonlinear mapping and the semantic feature extraction of the deep learning, a deep learning network is proposed for video face detection to overcome the challenge of detecting faces rapidly and accurately in video with changeable background. Particularly, a pre-training procedure is used to initialize the network parameters to avoid falling into the local optimum, and the greedy layer-wise learning is introduced in the pre-training to avoid the training error transfer in layers. Key to the network is that the probability of neurons models the status of human brain neurons which is a continuous distribution from the most active to the least active and the hidden layer’s neuron number decreases layer-by-layer to reduce the redundant information of the input data. Moreover, the skin color detection is used to accelerate the detection speed by generating candidate regions. Experimental results show that, besides the faster detection speed and robustness against face rotation, the proposed method possesses lower false detection rate and lower missing detection rate than traditional algorithms.

Zhang, J., Tang, Z., Li, R., Chen, X., Gong, X., Fang, D., Wang, Z..  2017.  Protect Sensitive Information against Channel State Information Based Attacks. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). 2:203–210.

Channel state information (CSI) has been recently shown to be useful in performing security attacks in public WiFi environments. By analyzing how CSI is affected by the finger motions, CSI-based attacks can effectively reconstruct text-based passwords and locking patterns. This paper presents WiGuard, a novel system to protect sensitive on-screen gestures in a public place. Our approach carefully exploits the WiFi channel interference to introduce noise into the attacker's CSI measurement to reduce the success rate of the attack. Our approach automatically detects when a CSI-based attack happens. We evaluate our approach by applying it to protect text-based passwords and pattern locks on mobile devices. Experimental results show that our approach is able to reduce the success rate of CSI attacks from 92% to 42% for text-based passwords and from 82% to 22% for pattern lock.

Chen, X., Shang, T., Kim, I., Liu, J..  2017.  A Remote Data Integrity Checking Scheme for Big Data Storage. 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC). :53–59.

In the existing remote data integrity checking schemes, dynamic update operates on block level, which usually restricts the location of the data inserted in a file due to the fixed size of a data block. In this paper, we propose a remote data integrity checking scheme with fine-grained update for big data storage. The proposed scheme achieves basic operations of insertion, modification, deletion on line level at any location in a file by designing a mapping relationship between line level update and block level update. Scheme analysis shows that the proposed scheme supports public verification and privacy preservation. Meanwhile, it performs data integrity checking with low computation and communication cost.

Chen, X., Qu, G., Cui, A., Dunbar, C..  2017.  Scan chain based IP fingerprint and identification. 2017 18th International Symposium on Quality Electronic Design (ISQED). :264–270.

Digital fingerprinting refers to as method that can assign each copy of an intellectual property (IP) a distinct fingerprint. It was introduced for the purpose of protecting legal and honest IP users. The unique fingerprint can be used to identify the IP or a chip that contains the IP. However, existing fingerprinting techniques are not practical due to expensive cost of creating fingerprints and the lack of effective methods to verify the fingerprints. In the paper, we study a practical scan chain based fingerprinting method, where the digital fingerprint is generated by selecting the Q-SD or Q'-SD connection during the design of scan chains. This method has two major advantages. First, fingerprints are created as a post-silicon procedure and therefore there will be little fabrication overhead. Second, altering the Q-SD or Q'-SD connection style requires the modification of test vectors for each fingerprinted IP in order to maintain the fault coverage. This enables us to verify the fingerprint by inspecting the test vectors without opening up the chip to check the Q-SD or Q'-SD connection styles. We perform experiment on standard benchmarks to demonstrate that our approach has low design overhead. We also conduct security analysis to show that such fingerprints are robust against various attacks.

Xie, J., She, H., Chen, X., Zhang, H., Niu, Y..  2020.  Test Method for Automatic Detection Capability of Civil Aviation Security Equipment Using Bayesian Estimation. 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT. :831–835.
There are a lot of emerging security equipment required to be tested on detection rate (DR) and false alarm rate (FAR) for prohibited items. This article imports Bayesian approach to accept or reject DR and FAR. The detailed quantitative predictions can be made through the posterior distribution obtained by Markov chain Monte Carlo method. Based on this, HDI + ROPE decision rule is established. For the tests that need to make early decision, HDI + ROPE stopping rule is presented with biased estimate value, and criterial precision rule is presented with unbiased estimate value. Choosing the stopping rule according to the test purpose can achieve the balance of efficiency and accuracy.
Lei, M., Jin, M., Huang, T., Guo, Z., Wang, Q., Wu, Z., Chen, Z., Chen, X., Zhang, J..  2020.  Ultra-wideband Fingerprinting Positioning Based on Convolutional Neural Network. 2020 International Conference on Computer, Information and Telecommunication Systems (CITS). :1—5.

The Global Positioning System (GPS) can determine the position of any person or object on earth based on satellite signals. But when inside the building, the GPS cannot receive signals, the indoor positioning system will determine the precise position. How to achieve more precise positioning is the difficulty of an indoor positioning system now. In this paper, we proposed an ultra-wideband fingerprinting positioning method based on a convolutional neural network (CNN), and we collect the dataset in a room to test the model, then compare our method with the existing method. In the experiment, our method can reach an accuracy of 98.36%. Compared with other fingerprint positioning methods our method has a great improvement in robustness. That results show that our method has good practicality while achieves higher accuracy.