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X. Feng, Z. Zheng, P. Hu, D. Cansever, P. Mohapatra.  2015.  "Stealthy attacks meets insider threats: A three-player game model". MILCOM 2015 - 2015 IEEE Military Communications Conference. :25-30.

Advanced persistent threat (APT) is becoming a major threat to cyber security. As APT attacks are often launched by well funded entities that are persistent and stealthy in achieving their goals, they are highly challenging to combat in a cost-effective way. The situation becomes even worse when a sophisticated attacker is further assisted by an insider with privileged access to the inside information. Although stealthy attacks and insider threats have been considered separately in previous works, the coupling of the two is not well understood. As both types of threats are incentive driven, game theory provides a proper tool to understand the fundamental tradeoffs involved. In this paper, we propose the first three-player attacker-defender-insider game to model the strategic interactions among the three parties. Our game extends the two-player FlipIt game model for stealthy takeover by introducing an insider that can trade information to the attacker for a profit. We characterize the subgame perfect equilibria of the game with the defender as the leader and the attacker and the insider as the followers, under two different information trading processes. We make various observations and discuss approaches for achieving more efficient defense in the face of both APT and insider threats.

X. Li, J. D. Haupt.  2015.  "Outlier identification via randomized adaptive compressive sampling". 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :3302-3306.

This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establish theoretical guarantees for its performance. Our results show that accurate outlier identification is achievable using very few linear summaries of the original data matrix - as few as the squared rank of the low-rank component plus the number of outliers, times constant and logarithmic factors. We demonstrate the performance of our approach experimentally in two stylized applications, one motivated by robust collaborative filtering tasks, and the other by saliency map estimation tasks arising in computer vision and automated surveillance.

Xenya, Michael Christopher, Kwayie, Crentsil, Quist-Aphesti, Kester.  2019.  Intruder Detection with Alert Using Cloud Based Convolutional Neural Network and Raspberry Pi. 2019 International Conference on Computing, Computational Modelling and Applications (ICCMA). :46–464.
In this paper, an intruder detection system has been built with an implementation of convolutional neural network (CNN) using raspberry pi, Microsoft's Azure and Twilio cloud systems. The CNN algorithm which is stored in the cloud is implemented to basically classify input data as either intruder or user. By using the raspberry pi as the middleware and raspberry pi camera for image acquisition, efficient execution of the learning and classification operations are performed using higher resources that cloud computing offers. The cloud system is also programmed to alert designated users via multimedia messaging services (MMS) when intruders or users are detected. Furthermore, our work has demonstrated that, though convolutional neural network could impose high computing demands on a processor, the input data could be obtained with low-cost modules and middleware which are of low processing power while subjecting the actual learning algorithm execution to the cloud system.
Xi Xiong, Haining Fan.  2014.  GF(2n) bit-parallel squarer using generalised polynomial basis for new class of irreducible pentanomials. Electronics Letters. 50:655-657.

Explicit formulae and complexities of bit-parallel GF(2n) squarers for a new class of irreducible pentanomials xn + xn-1 + xk + x + 1, where n is odd and 1 <; k <; (n - 1)/2 are presented. The squarer is based on the generalised polynomial basis of GF(2n). Its gate delay matches the best results, whereas its XOR gate complexity is n + 1, which is only about two thirds of the current best results.

Xi, W., Suo, S., Cai, T., Jian, G., Yao, H., Fan, L..  2019.  A Design and Implementation Method of IPSec Security Chip for Power Distribution Network System Based on National Cryptographic Algorithms. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :2307–2310.

The target of security protection of the power distribution automation system (the distribution system for short) is to ensure the security of communication between the distribution terminal (terminal for short) and the distribution master station (master system for short). The encryption and authentication gateway (VPN gateway for short) for distribution system enhances the network layer communication security between the terminal and the VPN gateway. The distribution application layer encryption authentication device (master cipher machine for short) ensures the confidentiality and integrity of data transmission in application layer, and realizes the identity authentication between the master station and the terminal. All these measures are used to prevent malicious damage and attack to the master system by forging terminal identity, replay attack and other illegal operations, in order to prevent the resulting distribution network system accidents. Based on the security protection scheme of the power distribution automation system, this paper carries out the development of multi-chip encapsulation, develops IPSec Protocols software within the security chip, and realizes dual encryption and authentication function in IP layer and application layer supporting the national cryptographic algorithm.

Xi, X., Zhang, F., Lian, Z..  2017.  Implicit Trust Relation Extraction Based on Hellinger Distance. 2017 13th International Conference on Semantics, Knowledge and Grids (SKG). :223–227.

Recent studies have shown that adding explicit social trust information to social recommendation significantly improves the prediction accuracy of ratings, but it is difficult to obtain a clear trust data among users in real life. Scholars have studied and proposed some trust measure methods to calculate and predict the interaction and trust between users. In this article, a method of social trust relationship extraction based on hellinger distance is proposed, and user similarity is calculated by describing the f-divergence of one side node in user-item bipartite networks. Then, a new matrix factorization model based on implicit social relationship is proposed by adding the extracted implicit social relations into the improved matrix factorization. The experimental results support that the effect of using implicit social trust to recommend is almost the same as that of using actual explicit user trust ratings, and when the explicit trust data cannot be extracted, our method has a better effect than the other traditional algorithms.

Xi, Z., Chen, L., Chen, M., Dai, Z., Li, Y..  2018.  Power Mobile Terminal Security Assessment Based on Weights Self-Learning. 2018 10th International Conference on Communication Software and Networks (ICCSN). :502–505.

At present, mobile terminals are widely used in power system and easy to be the target or springboard to attack the power system. It is necessary to have security assessment of power mobile terminal system to enable early warning of potential risks. In the context, this paper builds the security assessment system against to power mobile terminals, with features from security assessment system of general mobile terminals and power application scenarios. Compared with the existing methods, this paper introduces machine learning to the Rank Correlation Analysis method, which relies on expert experience, and uses objective experimental data to optimize the weight parameters of the indicators. From experiments, this paper proves that weights self-learning method can be used to evaluate the security of power mobile terminal system and improve credibility of the result.

Xia Zeng, Tencent, Inc., Dengfend Li, University of Illinois at Urbana-Champaign, Wujie Zheng, Tencent, Inc., Yuetang Deng, Tencent, Inc., Wing Lam, University of Illinois at Urbana-Champaign, Wei Yang, University of Illinois at Urbana-Champaign, Tao Xie, University of Illinois at Urbana-Champaign.  2016.  Automated Test Input Generation for Android: Are We Really There Yet in an Industrial Case? 24th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2016).

Given the ever increasing number of research tools to automatically generate inputs to test Android applications (or simply apps), researchers recently asked the question "Are we there yet?" (in terms of the practicality of the tools). By conducting an empirical study of the various tools, the researchers found that Monkey (the most widely used tool of this category in industrial settings) outperformed all of the research tools in the study. In this paper, we present two signi cant extensions of that study. First, we conduct the rst industrial case study of applying Monkey against WeChat, a popular  messenger app with over 762 million monthly active users, and report the empirical ndings on Monkey's limitations in an industrial setting. Second, we develop a new approach to address major limitations of Monkey and accomplish substantial code-coverage improvements over Monkey. We conclude the paper with empirical insights for future enhancements to both Monkey and our approach.

Xia, D., Zhang, Y..  2017.  The fuzzy control of trust establishment. 2017 4th International Conference on Systems and Informatics (ICSAI). :655–659.

In the open network environment, the strange entities can establish the mutual trust through Automated Trust Negotiation (ATN) that is based on exchanging digital credentials. In traditional ATN, the attribute certificate required to either satisfied or not, and in the strategy, the importance of the certificate is same, it may cause some unnecessary negotiation failure. And in the actual situation, the properties is not just 0 or 1, it is likely to between 0 and 1, so the satisfaction degree is different, and the negotiation strategy need to be quantified. This paper analyzes the fuzzy negotiation process, in order to improve the trust establishment in high efficiency and accuracy further.

Xia, H., Xiao, F., Zhang, S., Hu, C., Cheng, X..  2019.  Trustworthiness Inference Framework in the Social Internet of Things: A Context-Aware Approach. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :838–846.
The concept of social networking is integrated into Internet of things (IoT) to socialize smart objects by mimicking human behaviors, leading to a new paradigm of Social Internet of Things (SIoT). A crucial problem that needs to be solved is how to establish reliable relationships autonomously among objects, i.e., building trust. This paper focuses on exploring an efficient context-aware trustworthiness inference framework to address this issue. Based on the sociological and psychological principles of trust generation between human beings, the proposed framework divides trust into two types: familiarity trust and similarity trust. The familiarity trust can be calculated by direct trust and recommendation trust, while the similarity trust can be calculated based on external similarity trust and internal similarity trust. We subsequently present concrete methods for the calculation of different trust elements. In particular, we design a kernel-based nonlinear multivariate grey prediction model to predict the direct trust of a specific object, which acts as the core module of the entire framework. Besides, considering the fuzziness and uncertainty in the concept of trust, we introduce the fuzzy logic method to synthesize these trust elements. The experimental results verify the validity of the core module and the resistance to attacks of this framework.
Xia, H., Gao, N., Peng, J., Mo, J., Wang, J..  2020.  Binarized Attributed Network Embedding via Neural Networks. 2020 International Joint Conference on Neural Networks (IJCNN). :1—8.
Traditional attributed network embedding methods are designed to map structural and attribute information of networks jointly into a continuous Euclidean space, while recently a novel branch of them named binarized attributed network embedding has emerged to learn binary codes in Hamming space, aiming to save time and memory costs and to naturally fit node retrieval task. However, current binarized attributed network embedding methods are scarce and mostly ignore the local attribute similarity between each pair of nodes. Besides, none of them attempt to control the independency of each dimension(bit) of the learned binary representation vectors. As existing methods still need improving, we propose an unsupervised Neural-based Binarized Attributed Network Embedding (NBANE) approach. Firstly, we inherit the Weisfeiler-Lehman proximity matrix from predecessors to aggregate high-order features for each node. Secondly, we feed the aggregated features into an autoencoder with the attribute similarity penalizing term and the orthogonality term to make further dimension reduction. To solve the problem of integer optimization we adopt the relaxation-quantization method during the process of training neural networks. Empirically, we evaluate the performance of NBANE through node classification and clustering tasks on three real-world datasets and study a case on fast retrieval in academic networks. Our method achieves better performance over state- of-the-art baselines methods of various types.
Xia, Haijun.  2016.  Object-Oriented Interaction: Enabling Direct Physical Manipulation of Abstract Content via Objectification. Proceedings of the 29th Annual Symposium on User Interface Software and Technology. :13–16.

Touch input promises intuitive interactions with digital content as it employs our experience of manipulating physical objects: digital content can be rotated, scaled, and translated using direct manipulation gestures. However, the reliance on analog also confines the scope of direct physical manipulation: the physical world provides no mechanism to interact with digital abstract content. As such, applications on touchscreen devices either only include limited functionalities or fallback on the traditional form-filling paradigm, which is tedious, slow, and error prone for touch input. My research focuses on designing a new UI framework to enable complex functionalities on touch screen devices by expanding direct physical manipulation to abstract content via objectification. I present two research projects, objectification of attributes and selection, which demonstrate considerable promises.

Xia, Huiyun, Han, Shuai, Li, Cheng, Meng, Weixiao.  2019.  Joint PHY/MAC Layer AN-Assisted Security Scheme in SVD-Based MIMO HARQ system. 2019 IEEE/CIC International Conference on Communications in China (ICCC). :328–333.
With the explosive data growth arise from internet of things, how to ensure information security is facing unprecedented challenges. In this paper, a joint PHY/MAC layer security scheme with artificial noise design in singular value decomposition (SVD) based multiple input multiple output hybrid automatic retransmission request (MIMO HARQ) system is proposed to resolve the problem of low data rates in existing cross-layer security design and further adapt to the high data rate requirement of 5G. First, the SVD was applied to simplify MIMO systems into several parallel sub-channels employing HARQ protocol. Then, different from traditional null space based artificial noise design, the artificial noise design, which is dependent on the characteristics of channel states and transmission rounds, is detailed presented. Finally, the analytical and simulation results proved that with the help of the proposed artificial noise, both the information security and data rate performance can be significantly improved compared with that in single input single output (SISO) system.
Xia, Lixue, Tang, Tianqi, Huangfu, Wenqin, Cheng, Ming, Yin, Xiling, Li, Boxun, Wang, Yu, Yang, Huazhong.  2016.  Switched by Input: Power Efficient Structure for RRAM-based Convolutional Neural Network. Proceedings of the 53rd Annual Design Automation Conference. :125:1–125:6.

Convolutional Neural Network (CNN) is a powerful technique widely used in computer vision area, which also demands much more computations and memory resources than traditional solutions. The emerging metal-oxide resistive random-access memory (RRAM) and RRAM crossbar have shown great potential on neuromorphic applications with high energy efficiency. However, the interfaces between analog RRAM crossbars and digital peripheral functions, namely Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs), consume most of the area and energy of RRAM-based CNN design due to the large amount of intermediate data in CNN. In this paper, we propose an energy efficient structure for RRAM-based CNN. Based on the analysis of data distribution, a quantization method is proposed to transfer the intermediate data into 1 bit and eliminate DACs. An energy efficient structure using input data as selection signals is proposed to reduce the ADC cost for merging results of multiple crossbars. The experimental results show that the proposed method and structure can save 80% area and more than 95% energy while maintaining the same or comparable classification accuracy of CNN on MNIST.

Xia, Qi, Sifah, Emmanuel Boateng, Obour Agyekum, Kwame Opuni-Boachie, Xia, Hu, Acheampong, Kingsley Nketia, Smahi, Abla, Gao, Jianbin, Du, Xiaojiang, Guizani, Mohsen.  2019.  Secured Fine-Grained Selective Access to Outsourced Cloud Data in IoT Environments. IEEE Internet of Things Journal. 6:10749–10762.
With the vast increase in data transmission due to a large number of information collected by devices, data management, and security has been a challenge for organizations. Many data owners (DOs) outsource their data to cloud repositories due to several economic advantages cloud service providers present. However, DOs, after their data are outsourced, do not have complete control of the data, and therefore, external systems are incorporated to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data but prove to be deficient in providing suitable solutions to the problem. In this article, we propose a secure fine-grain access control system for outsourced data, which supports read and write operations to the data. We make use of an attribute-based encryption (ABE) scheme, which is regarded as a suitable scheme to achieve access control for security and privacy (confidentiality) of outsourced data. This article considers different categories of data users, and make provisions for distinct access roles and permissible actions on the outsourced data with dynamic and efficient policy updates to the corresponding ciphertext in cloud repositories. We adopt blockchain technologies to enhance traceability and visibility to enable control over outsourced data by a DO. The security analysis presented demonstrates that the security properties of the system are not compromised. Results based on extensive experiments illustrate the efficiency and scalability of our system.
Xia, S., Li, N., Xiaofeng, T., Fang, C..  2018.  Multiple Attributes Based Spoofing Detection Using an Improved Clustering Algorithm in Mobile Edge Network. 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN). :242–243.

Information centric network (ICN) based Mobile Edge Computing (MEC) network has drawn growing attentions in recent years. The distributed network architecture brings new security problems, especially the identity security problem. Because of the cloud platform deployed on the edge of the MEC network, multiple channel attributes can be easily obtained and processed. Thus this paper proposes a multiple channel attributes based spoofing detection mechanism. To further reduce the complexity, we also propose an improved clustering algorithm. The simulation results indicate that the proposed spoofing detection method can provide near-optimal performance with extremely low complexity.

Xia, Weiyi, Kantarcioglu, Murat, Wan, Zhiyu, Heatherly, Raymond, Vorobeychik, Yevgeniy, Malin, Bradley.  2015.  Process-Driven Data Privacy. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. :1021–1030.

The quantity of personal data gathered by service providers via our daily activities continues to grow at a rapid pace. The sharing, and the subsequent analysis of, such data can support a wide range of activities, but concerns around privacy often prompt an organization to transform the data to meet certain protection models (e.g., k-anonymity or E-differential privacy). These models, however, are based on simplistic adversarial frameworks, which can lead to both under- and over-protection. For instance, such models often assume that an adversary attacks a protected record exactly once. We introduce a principled approach to explicitly model the attack process as a series of steps. Specically, we engineer a factored Markov decision process (FMDP) to optimally plan an attack from the adversary's perspective and assess the privacy risk accordingly. The FMDP captures the uncertainty in the adversary's belief (e.g., the number of identied individuals that match the de-identified data) and enables the analysis of various real world deterrence mechanisms beyond a traditional protection model, such as a penalty for committing an attack. We present an algorithm to solve the FMDP and illustrate its efficiency by simulating an attack on publicly accessible U.S. census records against a real identied resource of over 500,000 individuals in a voter registry. Our results demonstrate that while traditional privacy models commonly expect an adversary to attack exactly once per record, an optimal attack in our model may involve exploiting none, one, or more indiviuals in the pool of candidates, depending on context.

Xia, Xiaoxu, Song, Wei, Chen, Fangfei, Li, Xuansong, Zhang, Pengcheng.  2016.  Effa: A proM Plugin for Recovering Event Logs. Proceedings of the 8th Asia-Pacific Symposium on Internetware. :108–111.

While event logs generated by business processes play an increasingly significant role in business analysis, the quality of data remains a serious problem. Automatic recovery of dirty event logs is desirable and thus receives more attention. However, existing methods only focus on missing event recovery, or fall short of efficiency. To this end, we present Effa, a ProM plugin, to automatically recover event logs in the light of process specifications. Based on advanced heuristics including process decomposition and trace replaying to search the minimum recovery, Effa achieves a balance between repairing accuracy and efficiency.

Xiang Zhou.  2014.  Efficient Clock and Carrier Recovery Algorithms for Single-Carrier Coherent Optical Systems: A systematic review on challenges and recent progress. Signal Processing Magazine, IEEE. 31:35-45.

This article presents a systematic review on the challenges and recent progress of timing and carrier synchronization techniques for high-speed optical transmission systems using single-carrier-based coherent optical modulation formats.
 

Xiang-ning, M., Kai-jia, L., Hao, L..  2017.  A physical layer security algorithm based on constellation. 2017 IEEE 17th International Conference on Communication Technology (ICCT). :50–53.
The cyclostationary characteristics of signals has some important applications in such as blind channel equalization, blind adaptive beamforming, and system identification. However, the cyclostationary characteristics also can be a weak link in physical layer security. With high-order cyclostationary theory, some system information can be obtained easily. In this paper, we proposed a new algorithm based on constellation phase rotation and amplitude randomization, during which the cyclostationary feature of signals can be suppressed.
Xiang, Guangli, Li, Beilei, Fu, Xiannong, Xia, Mengsen, Ke, Weiyi.  2019.  An Attribute Revocable CP-ABE Scheme. 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD). :198—203.

Ciphertext storage can effectively solve the security problems in cloud storage, among which the ciphertext policy attribute-based encryption (CP-ABE) is more suitable for ciphertext access control in cloud storage environment for it can achieve one-to-many ciphertext sharing. The existing attribute encryption scheme CP-ABE has problems with revocation such as coarse granularity, untimeliness, and low efficiency, which cannot meet the demands of cloud storage. This paper proposes an RCP-ABE scheme that supports real-time revocable fine-grained attributes for the existing attribute revocable scheme, the scheme of this paper adopts the version control technology to realize the instant revocation of the attributes. In the key update mechanism, the subset coverage technology is used to update the key, which reduces the workload of the authority. The experimental analysis shows that RCP-ABE is more efficient than other schemes.

Xiang, Jie, Chen, Long.  2018.  A Method of Docker Container Forensics Based on API. Proceedings of the 2Nd International Conference on Cryptography, Security and Privacy. :159–164.
As one of the main technologies supporting cloud computing virtualization, Docker is featured in its fast and lightweight virtualization which has been adopted by numerous platform-as-a-service (PaaS) systems, but forensics research for Docker has not been paid the corresponding attention yet. Docker exists to store and distribute illegal information as a carrier for initiating attacks like traditional cloud services. The paper explains Docker service principles and structural features, and analyzing the model and method of forensics in related cloud environment, then proposes a Docker container forensics solution based on the Docker API. In this paper, Docker APIs realize the derivation of the Docker container instances, copying and back-up of the container data volume, extraction of the key evidence data, such as container log information, configuration information and image information, thus conducts localized fixed forensics to volatile evidence and data in the Docker service container. Combined with digital signatures and digital encryption technology to achieve the integrity of the original evidence data protection.
Xiang, Wei.  2019.  An Efficient Location Privacy Preserving Model based on Geohash. 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC). :1–5.
With the rapid development of location-aware mobile devices, location-based services have been widely used. When LBS (Location Based Services) bringing great convenience and profits, it also brings great hidden trouble, among which user privacy security is one of them. The paper builds a LBS privacy protection model and develops algorithm depend on the technology of one dimensional coding of Geohash geographic information. The results of experiments and data measurements show that the model the model has reached k-anonymity effect and has good performance in avoiding attacking from the leaked information in a continuous query with the user's background knowledge. It also has a preferable performance in time cost of system process.
Xiang, Yingmeng, Zhang, Yichi, Wang, Lingfeng, Sun, Weiqing.  2014.  Impact of UPFC on power system reliability considering its cyber vulnerability. T D Conference and Exposition, 2014 IEEE PES. :1-5.

The unified power flow controller (UPFC) has attracted much attention recently because of its capability in controlling the active and reactive power flows. The normal operation of UPFC is dependent on both its physical part and the associated cyber system. Thus malicious cyber attacks may impact the reliability of UPFC. As more information and communication technologies are being integrated into the current power grid, more frequent occurrences of cyber attacks are possible. In this paper, the cyber architecture of UPFC is analyzed, and the possible attack scenarios are considered and discussed. Based on the interdependency of the physical part and the cyber part, an integrated reliability model for UPFC is proposed and analyzed. The impact of UPFC on the overall system reliability is examined, and it is shown that cyber attacks against UPFC may yield an adverse influence.

Xiang, Z., Cai, Y., Yang, W., Sun, X., Hu, Y..  2017.  Physical layer security of non-orthogonal multiple access in cognitive radio networks. 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP). :1–6.

This paper investigates physical layer security of non-orthogonal multiple access (NOMA) in cognitive radio (CR) networks. The techniques of NOMA and CR have improved the spectrum efficiency greatly in the traditional networks. Because of the difference in principles of spectrum improving, NOMA and CR can be combined together, i.e. CR NOMA network, and have great potential to improving the spectrum efficiency. However the physical layer security in CR NOMA network is different from any single network of NOMA or CR. We will study the physical layer security in underlay CR NOMA network. Firstly, the wiretap network model is constructed according to the technical characteristics of NOMA and CR. In addition, new exact and asymptotic expressions of the security outage probability are derived and been confirmed by simulation. Ultimately, we have studied the effect of some critical factors on security outage probability after simulation.