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Ding, Q., Peng, X., Zhang, X., Hu, X., Zhong, X..  2017.  Adaptive observer-based fault diagnosis for sensor in a class of MIMO nonlinear system. 2017 36th Chinese Control Conference (CCC). :7051–7058.

This paper presents a novel sensor parameter fault diagnosis method for generally multiple-input multiple-output (MIMO) affine nonlinear systems based on adaptive observer. Firstly, the affine nonlinear systems are transformed into the particular systems via diffeomorphic transformation using Lie derivative. Then, based on the techniques of high-gain observer and adaptive estimation, an adaptive observer structure is designed with simple method for jointly estimating the states and the unknown parameters in the output equation of the nonlinear systems. And an algorithm of the fault estimation is derived. The global exponential convergence of the proposed observer is proved succinctly. Also the proposed method can be applied to the fault diagnosis of generally affine nonlinear systems directly by the reversibility of aforementioned coordinate transformation. Finally, a numerical example is presented to illustrate the efficiency of the proposed fault diagnosis scheme.

Zhang, P., Zhang, X., Sun, X., Liu, J. K., Yu, J., Jiang, Z. L..  2017.  Anonymous Anti-Sybil Attack Protocol for Mobile Healthcare Networks Analytics. 2017 IEEE Trustcom/BigDataSE/ICESS. :668–674.

Mobile Healthcare Networks (MHN) continuouslycollect the patients' health data sensed by wearable devices, andanalyze the collected data pre-processed by servers combinedwith medical histories, such that disease diagnosis and treatmentare improved, and the heavy burden on the existing healthservices is released. However, the network is vulnerable to Sybilattacks, which would degrade network performance, disruptproceedings, manipulate data or cheat others maliciously. What'smore, the user is reluctant to leak identity privacy, so the identityprivacy preserving makes Sybil defenses more difficult. One ofthe best choices is mutually authenticating each other with noidentity information involved. Thus, we propose a fine-grainedauthentication scheme based on Attribute-Based Signature (ABS)using lattice assumption, where a signer is authorized by an at-tribute set instead of single identity string. This ABS scheme usesFiat-Shamir framework and supports flexible threshold signaturepredicates. Moreover, to anonymously guarantee integrity andavailability of health data in MHN, we design an anonymousanti-Sybil attack protocol based on our ABS scheme, so thatSybil attacks are prevented. As there is no linkability betweenidentities and services, the users' identity privacy is protected. Finally, we have analyzed the security and simulated the runningtime for our proposed ABS scheme.

Zhang, X., Gong, L., Xun, Y., Piao, X., Leit, K..  2016.  Centaur: A evolutionary design of hybrid NDN/IP transport architecture for streaming application. 2016 IEEE 7th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :1–7.

Named Data Networking (NDN), a clean-slate data oriented Internet architecture targeting on replacing IP, brings many potential benefits for content distribution. Real deployment of NDN is crucial to verify this new architecture and promote academic research, but work in this field is at an early stage. Due to the fundamental design paradigm difference between NDN and IP, Deploying NDN as IP overlay causes high overhead and inefficient transmission, typically in streaming applications. Aiming at achieving efficient NDN streaming distribution, this paper proposes a transitional architecture of NDN/IP hybrid network dubbed Centaur, which embodies both NDN's smartness, scalability and IP's transmission efficiency and deployment feasibility. In Centaur, the upper NDN module acts as the smart head while the lower IP module functions as the powerful feet. The head is intelligent in content retrieval and self-control, while the IP feet are able to transport large amount of media data faster than that if NDN directly overlaying on IP. To evaluate the performance of our proposal, we implement a real streaming prototype in ndnSIM and compare it with both NDN-Hippo and P2P under various experiment scenarios. The result shows that Centaur can achieve better load balance with lower overhead, which is close to the performance that ideal NDN can achieve. All of these validate that our proposal is a promising choice for the incremental and compatible deployment of NDN.

Wen, M., Zhang, X., Li, H., Li, J..  2017.  A Data Aggregation Scheme with Fine-Grained Access Control for the Smart Grid. 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). :1–5.

With the rapid development of smart grid, smart meters are deployed at energy consumers' premises to collect real-time usage data. Although such a communication model can help the control center of the energy producer to improve the efficiency and reliability of electricity delivery, it also leads to some security issues. For example, this real-time data involves the customers' privacy. Attackers may violate the privacy for house breaking, or they may tamper with the transmitted data for their own benefits. For this purpose, many data aggregation schemes are proposed for privacy preservation. However, rare of them cares about both the data aggregation and fine-grained access control to improve the data utility. In this paper, we proposes a data aggregation scheme based on attribute decision tree. Security analysis illustrates that our scheme can achieve the data integrity, data privacy preservation and fine- grained data access control. Experiment results show that our scheme are more efficient than existing schemes.

Wang, J., Zhang, X., Zhang, H., Lin, H., Tode, H., Pan, M., Han, Z..  2018.  Data-Driven Optimization for Utility Providers with Differential Privacy of Users' Energy Profile. 2018 IEEE Global Communications Conference (GLOBECOM). :1–6.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

Yu, R., Xue, G., Kilari, V. T., Zhang, X..  2018.  Deploying Robust Security in Internet of Things. 2018 IEEE Conference on Communications and Network Security (CNS). :1-9.

Popularization of the Internet-of-Things (IoT) has brought widespread concerns on IoT security, especially in face of several recent security incidents related to IoT devices. Due to the resource-constrained nature of many IoT devices, security offloading has been proposed to provide good-enough security for IoT with minimum overhead on the devices. In this paper, we investigate the inevitable risk associated with security offloading: the unprotected and unmonitored transmission from IoT devices to the offloaded security mechanisms. An important challenge in modeling the security risk is the dynamic nature of IoT due to demand fluctuations and infrastructure instability. We propose a stochastic model to capture both the expected and worst-case security risks of an IoT system. We then propose a framework to efficiently address the optimal robust deployment of security mechanisms in IoT. We use results from extensive simulations to demonstrate the superb performance and efficiency of our approach compared to several other algorithms.

Ma, T., Zhang, H., Qian, J., Liu, S., Zhang, X., Ma, X..  2015.  The Design of Brand Cosmetics Anti-counterfeiting System Based on RFID Technology. 2015 International Conference on Network and Information Systems for Computers. :184–189.

The digital authentication security technology is widely used in the current brand cosmetics as key anti-counterfeiting technology, yet this technology is prone to "false security", "hard security" and "non-security" phenomena. This paper researches the current cosmetics brand distribution channels and sales methods also analyses the cosmetics brands' demand for RFID technology anti-counterfeiting security system, then proposes a security system based on RFID technology for brand cosmetics. The system is based on a typical distributed RFID tracking and tracing system which is the most widely used system-EPC system. This security system based on RFID technology for brand cosmetics in the paper is a visual information management system for luxury cosmetics brand. It can determine the source of the product timely and effectively, track and trace products' logistics information and prevent fake goods and gray goods getting into the normal supply chain channels.

Zhang, X., Li, R., Zhao, W., Wu, R..  2017.  Detection of malicious nodes in NDN VANET for Interest Packet Popple Broadcast Diffusion Attack. 2017 11th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID). :114–118.

As one of the next generation network architectures, Named Data Networking(NDN) which features location-independent addressing and content caching makes it more suitable to be deployed into Vehicular Ad-hoc Network(VANET). However, a new attack pattern is found when NDN and VANET combine. This new attack is Interest Packet Popple Broadcast Diffusion Attack (PBDA). There is no mitigation strategies to mitigate PBDA. In this paper a mitigation strategies called RVMS based on node reputation value (RV) is proposed to detect malicious nodes. The node calculates the neighbor node RV by direct and indirect RV evaluation and uses Markov chain predict the current RV state of the neighbor node according to its historical RV. The RV state is used to decide whether to discard the interest packet. Finally, the effectiveness of the RVMS is verified through modeling and experiment. The experimental results show that the RVMS can mitigate PBDA.

Zhang, X., Cao, Y., Yang, M., Wu, J., Luo, T., Liu, Y..  2017.  Droidrevealer: Automatically detecting Mysterious Codes in Android applications. 2017 IEEE Conference on Dependable and Secure Computing. :535–536.

The state-of-the-art Android malware often encrypts or encodes malicious code snippets to evade malware detection. In this paper, such undetectable codes are called Mysterious Codes. To make such codes detectable, we design a system called Droidrevealer to automatically identify Mysterious Codes and then decode or decrypt them. The prototype of Droidrevealer is implemented and evaluated with 5,600 malwares. The results show that 257 samples contain the Mysterious Codes and 11,367 items are exposed. Furthermore, several sensitive behaviors hidden in the Mysterious Codes are disclosed by Droidrevealer.

Gu, R., Zhang, X., Yu, L., Zhang, J..  2018.  Enhancing Security and Scalability in Software Defined LTE Core Networks. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :837–842.

The rapid development of mobile networks has revolutionized the way of accessing the Internet. The exponential growth of mobile subscribers, devices and various applications frequently brings about excessive traffic in mobile networks. The demand for higher data rates, lower latency and seamless handover further drive the demand for the improved mobile network design. However, traditional methods can no longer offer cost-efficient solutions for better user quality of experience with fast time-to-market. Recent work adopts SDN in LTE core networks to meet the requirement. In these software defined LTE core networks, scalability and security become important design issues that must be considered seriously. In this paper, we propose a scalable channel security scheme for the software defined LTE core network. It applies the VxLAN for scalable tunnel establishment and MACsec for security enhancement. According to our evaluation, the proposed scheme not only enhances the security of the channel communication between different network components, but also improves the flexibility and scalability of the core network with little performance penalty. Moreover, it can also shed light on the design of the next generation cellular network.

Zhou, G., Feng, Y., Bo, R., Chien, L., Zhang, X., Lang, Y., Jia, Y., Chen, Z..  2017.  GPU-Accelerated Batch-ACPF Solution for N-1 Static Security Analysis. IEEE Transactions on Smart Grid. 8:1406–1416.

Graphics processing unit (GPU) has been applied successfully in many scientific computing realms due to its superior performances on float-pointing calculation and memory bandwidth, and has great potential in power system applications. The N-1 static security analysis (SSA) appears to be a candidate application in which massive alternating current power flow (ACPF) problems need to be solved. However, when applying existing GPU-accelerated algorithms to solve N-1 SSA problem, the degree of parallelism is limited because existing researches have been devoted to accelerating the solution of a single ACPF. This paper therefore proposes a GPU-accelerated solution that creates an additional layer of parallelism among batch ACPFs and consequently achieves a much higher level of overall parallelism. First, this paper establishes two basic principles for determining well-designed GPU algorithms, through which the limitation of GPU-accelerated sequential-ACPF solution is demonstrated. Next, being the first of its kind, this paper proposes a novel GPU-accelerated batch-QR solver, which packages massive number of QR tasks to formulate a new larger-scale problem and then achieves higher level of parallelism and better coalesced memory accesses. To further improve the efficiency of solving SSA, a GPU-accelerated batch-Jacobian-Matrix generating and contingency screening is developed and carefully optimized. Lastly, the complete process of the proposed GPU-accelerated batch-ACPF solution for SSA is presented. Case studies on an 8503-bus system show dramatic computation time reduction is achieved compared with all reported existing GPU-accelerated methods. In comparison to UMFPACK-library-based single-CPU counterpart using Intel Xeon E5-2620, the proposed GPU-accelerated SSA framework using NVIDIA K20C achieves up to 57.6 times speedup. It can even achieve four times speedup when compared to one of the fastest multi-core CPU parallel computing solution using KLU library. The prop- sed batch-solving method is practically very promising and lays a critical foundation for many other power system applications that need to deal with massive subtasks, such as Monte-Carlo simulation and probabilistic power flow.

Song, W., Li, X., Lou, L., Hua, Y., Zhang, Q., Huang, G., Hou, F., Zhang, X..  2018.  High-Temperature Magnetic Properties of Anisotropic SmCo7/Fe(Co) Bulk Nanocomposite Magnets. IEEE Transactions on Magnetics. 54:1–5.
High-temperature magnetic properties of the anisotropic bulk SmCo7/Fe(Co) nanocomposite magnets prepared by multistep deformation have been investigated and compared with the corresponding isotropic nanocomposites. The anisotropic SmCo7/Fe(Co) nanocomposites with a Fe(Co) fraction of 28% exhibit much higher energy products than the corresponding isotropic nanocomposites at both room and high temperatures. These magnets show a small remanence (α = -0.022%/K) and a coercivity (β = -0.25%/K) temperature coefficient which can be comparable to those of the conventional SmCo5 and Sm2Co17 high-temperature magnets. The magnetic properties of these nanocomposites at high temperatures are sensitive to the weight fractions of the Fe(Co) phase. This paper demonstrates that the anisotropic bulk SmCo7/Fe(Co) nanocomposites have better high-temperature magnetic properties than the corresponding isotropic ones.
Wu, Y., Li, X., Zou, D., Yang, W., Zhang, X., Jin, H..  2019.  MalScan: Fast Market-Wide Mobile Malware Scanning by Social-Network Centrality Analysis. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). :139—150.

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use either syntax-based features which can be evaded by transformation attacks or semantic-based features which are usually extracted by performing expensive program analysis. Therefor, in this paper, we propose a lightweight graph-based approach to perform Android malware detection. Instead of traditional heavyweight static analysis, we treat function call graphs of apps as social networks and perform social-network-based centrality analysis to represent the semantic features of the graphs. Our key insight is that centrality provides a succinct and fault-tolerant representation of graph semantics, especially for graphs with certain amount of inaccurate information (e.g., inaccurate call graphs). We implement a prototype system, MalScan, and evaluate it on datasets of 15,285 benign samples and 15,430 malicious samples. Experimental results show that MalScan is capable of detecting Android malware with up to 98% accuracy under one second which is more than 100 times faster than two state-of-the-art approaches, namely MaMaDroid and Drebin. We also demonstrate the feasibility of MalScan on market-wide malware scanning by performing a statistical study on over 3 million apps. Finally, in a corpus of dataset collected from Google-Play app market, MalScan is able to identify 18 zero-day malware including malware samples that can evade detection of existing tools.

Zhang, X., Li, R., Zhao, H..  2017.  Neighbor-aware based forwarding strategy in NDN-MANET. 2017 11th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID). :125–129.

Named Data Networking (NDN) is a future Internet architecture, NDN forwarding strategy is a hot research topic in MANET. At present, there are two categories of forwarding strategies in NDN. One is the blind forwarding(BF), the other is the aware forwarding(AF). Data packet return by the way that one came forwarding strategy(DRF) as one of the BF strategy may fail for the interruptions of the path that are caused by the mobility of nodes. Consumer need to wait until the interest packet times out to request the data packet again. To solve the insufficient of DRF, in this paper a Forwarding Strategy, called FN based on Neighbor-aware is proposed for NDN MANET. The node maintains the neighbor information and the request information of neighbor nodes. In the phase of data packet response, in order to improve request satisfaction rate, node specifies the next hop node; Meanwhile, in order to reduce packet loss rate, node assists the last hop node to forward packet to the specific node. The simulation results show that compared with DRF and greedy forwarding(GF) strategy, FN can improve request satisfaction rate when node density is high.

Xu, Y., Chen, H., Zhao, Y., Zhang, W., Shen, Q., Zhang, X., Ma, Z..  2019.  Neural Adaptive Transport Framework for Internet-scale Interactive Media Streaming Services. 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). :1–6.
Network dynamics, such as bandwidth fluctuation and unexpected latency, hurt users' quality of experience (QoE) greatly for media services over the Internet. In this work, we propose a neural adaptive transport (NAT) framework to tackle the network dynamics for Internet-scale interactive media services. The entire NAT system has three major components: a learning based cloud overlay routing (COR) scheme for the best delivery path to bypass the network bottlenecks while offering the minimal end-to-end latency simultaneously; a residual neural network based collaborative video processing (CVP) system to trade the computational capability at client-end for QoE improvement via learned resolution scaling; and a deep reinforcement learning (DRL) based adaptive real-time streaming (ARS) strategy to select the appropriate video bitrate for maximal QoE. We have demonstrated that COR could improve the user satisfaction from 5% to 43%, CVP could reduce the bandwidth consumption more than 30% at the same quality, and DRL-based ARS can maintain the smooth streaming with \textbackslashtextless; 50% QoE improvement, respectively.
Yang, Y., Wu, L., Zhang, X., He, J..  2017.  A Novel Hardware Trojan Detection with Chip ID Based on Relative Time Delays. 2017 11th IEEE International Conference on Anti-Counterfeiting, Security, and Identification (ASID). :163–167.

This paper introduces a hardware Trojan detection method using Chip ID which is generated by Relative Time-Delays (RTD) of sensor chains and the effectiveness of RTD is verified by post-layout simulations. The rank of time-delays of the sensor chains would be changed in Trojan-inserted chip. RTD is an accurate approach targeting to all kinds of Trojans, since it is based on the RELATIVE relationship between the time-delays rather than the absolute values, which are hard to be measured and will change with the fabricate process. RTD needs no golden chip, because the RELATIVE values would not change in most situations. Thus the genuine ID can be generated by simulator. The sensor chains can be inserted into a layout utilizing unused spaces, so RTD is a low-cost solution. A Trojan with 4x minimum NMOS is placed in different places of the chip. The behavior of the chip is obtained by using transient based post-layout simulation. All the Trojans are detected AND located, thus the effectiveness of RTD is verified.

Li, T., Wu, L., Zhang, X., Wu, X., Zhou, J., Wang, X..  2017.  A novel transition effect ring oscillator based true random number generator for a security SoC. 2017 International Conference on Electron Devices and Solid-State Circuits (EDSSC). :1–2.

The transition effect ring oscillator (TERO) based true random number generator (TRNG) was proposed by Varchola and Drutarovsky in 2010. There were several stochastic models for this advanced TRNG based on ring oscillator. This paper proposed an improved TERO based TRNG and implements both on Altera Cyclone series FPGA platform and on a 0.13um CMOS ASIC process. FPGA experimental results show that this balanced TERO TRNG is in good performance as the experimental data results past the national institute of standards and technology (NIST) test in 1M bit/s. The TRNG is feasible for a security SoC.

Li, W., Li, S., Zhang, X., Pan, Q..  2018.  Optimization Algorithm Research of Logistics Distribution Path Based on the Deep Belief Network. 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). :60-63.

Aiming at the phenomenon that the urban traffic is complex at present, the optimization algorithm of the traditional logistic distribution path isn't sensitive to the change of road condition without strong application in the actual logistics distribution, the optimization algorithm research of logistics distribution path based on the deep belief network is raised. Firstly, build the traffic forecast model based on the deep belief network, complete the model training and conduct the verification by learning lots of traffic data. On such basis, combine the predicated road condition with the traffic network to build the time-share traffic network, amend the access set and the pheromone variable of ant algorithm in accordance with the time-share traffic network, and raise the optimization algorithm of logistics distribution path based on the traffic forecasting. Finally, verify the superiority and application value of the algorithm in the actual distribution through the optimization algorithm contrast test with other logistics distribution paths.

Zheng, H., Zhang, X..  2017.  Optimizing Task Assignment with Minimum Cost on Heterogeneous Embedded Multicore Systems Considering Time Constraint. 2017 ieee 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids). :225–230.
Time and cost are the most critical performance metrics for computer systems including embedded system, especially for the battery-based embedded systems, such as PC, mainframe computer, and smart phone. Most of the previous work focuses on saving energy in a deterministic way by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments. Through studying the relationship between energy consumption, execution time and completion probability of tasks on heterogeneous multi-core architectures this paper proposes an optimal energy efficiency and system performance model and the OTHAP (Optimizing Task Heterogeneous Assignment with Probability) algorithm to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with areal-time embedded systems guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task and The experimental results demonstrate our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 24.6%).
Zhang, X., Chandramouli, K., Gabrijelcic, D., Zahariadis, T., Giunta, G..  2020.  Physical Security Detectors for Critical Infrastructures Against New-Age Threat of Drones and Human Intrusion. 2020 IEEE International Conference on Multimedia Expo Workshops (ICMEW). :1—4.

Modern critical infrastructures are increasingly turning into distributed, complex Cyber-Physical systems that need proactive protection and fast restoration to mitigate physical or cyber incidents or attacks. Addressing the need for early stage threat detection against physical intrusion, the paper presents two physical security sensors developed within the DEFENDER project for detecting the intrusion of drones and humans using video analytics. The continuous stream of media data obtained from the region of vulnerability and proximity is processed using Region based Fully Connected Neural Network deep-learning model. The novelty of the pro-posed system relies in the processing of multi-threaded media input streams for achieving real-time threat identification. The video analytics solution has been validated using NVIDIA GeForce GTX 1080 for drone detection and NVIDIA GeForce RTX 2070 Max-Q Design for detecting human intruders. The experimental test bed for the validation of the proposed system has been constructed to include environments and situations that are commonly faced by critical infrastructure operators such as the area of protection, tradeoff between angle of coverage against distance of coverage.

Ni, J., Cheng, W., Zhang, K., Song, D., Yan, T., Chen, H., Zhang, X..  2017.  Ranking Causal Anomalies by Modeling Local Propagations on Networked Systems. 2017 IEEE International Conference on Data Mining (ICDM). :1003–1008.

Complex systems are prevalent in many fields such as finance, security and industry. A fundamental problem in system management is to perform diagnosis in case of system failure such that the causal anomalies, i.e., root causes, can be identified for system debugging and repair. Recently, invariant network has proven a powerful tool in characterizing complex system behaviors. In an invariant network, a node represents a system component, and an edge indicates a stable interaction between two components. Recent approaches have shown that by modeling fault propagation in the invariant network, causal anomalies can be effectively discovered. Despite their success, the existing methods have a major limitation: they typically assume there is only a single and global fault propagation in the entire network. However, in real-world large-scale complex systems, it's more common for multiple fault propagations to grow simultaneously and locally within different node clusters and jointly define the system failure status. Inspired by this key observation, we propose a two-phase framework to identify and rank causal anomalies. In the first phase, a probabilistic clustering is performed to uncover impaired node clusters in the invariant network. Then, in the second phase, a low-rank network diffusion model is designed to backtrack causal anomalies in different impaired clusters. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.

Luo, C., Fan, X., Xin, G., Ni, J., Shi, P., Zhang, X..  2017.  Real-time localization of mobile targets using abnormal wireless signals. 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). :303–304.

Real-time localization of mobile target has been attracted much attention in recent years. With the limitation of unavailable GPS signals in the complex environments, wireless sensor networks can be applied to real-time locate and track the mobile targets in this paper. The multi wireless signals are used to weaken the effect of abnormal wireless signals in some areas. To verify the real-time localization performance for mobile targets, experiments and analyses are implemented. The results of the experiments reflect that the proposed location method can provide experimental basis for the applications, such as the garage, shopping center, underwater, etc.

Zhu, L., Zhou, X., Zhang, X..  2020.  A Reversible Meaningful Image Encryption Scheme Based on Block Compressive Sensing. 2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP). :326–330.
An efficient and reversible meaningful image encryption scheme is proposed in this paper. The plain image is first compressed and encrypted simultaneously by Adaptive Block Compressive Sensing (ABCS) framework to create a noise-like secret image. Next, Least Significant Bit (LSB) embedding is employed to embed the secret image into a carrier image to generate the final meaningful cipher image. In this scheme, ABCS improves the compression and efficiency performance, and the embedding and extraction operations are absolutely reversible. The simulation results and security analyses are presented to demonstrate the effectiveness, compression, secrecy of the proposed scheme.
Zhang, X., Li, R., Cui, B..  2018.  A security architecture of VANET based on blockchain and mobile edge computing. 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN). :258–259.

The development of Vehicular Ad-hoc NETwork (VANET) has brought many conveniences to human beings, but also brings a very prominent security problem. The traditional solution to the security problem is based on centralized approach which requires a trusted central entity which exists a single point of failure problem. Moreover, there is no approach of technical level to ensure security of data. Therefore, this paper proposes a security architecture of VANET based on blockchain and mobile edge computing. The architecture includes three layers, namely perception layer, edge computing layer and service layer. The perception layer ensures the security of VANET data in the transmission process through the blockchain technology. The edge computing layer provides computing resources and edge cloud services to the perception layer. The service layer uses the combination of traditional cloud storage and blockchain to ensure the security of data.

Yuan, Y., Wu, L., Zhang, X., Yang, Y..  2017.  Side-channel collision attack based on multiple-bits. 2017 11th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID). :1–5.

Side-channel collision attacks have been one of the most powerful attack techniques, combining advantages of traditional side-channel attack and mathematical cryptanalysis. In this paper, we propose a novel multiple-bits side-channel collision attack based on double distance voting detection, which can find all 120 relations among 16 key bytes with only 32 averaged power traces when applied to AES (Advanced Encryption Standard) algorithm. Practical attack experiments are performed successfully on a hardware implementation of AES on FPGA board. Results show that the necessary number of traces for our method is about 50% less than correlation-enhanced collision attack and 76% less than binary voting test with 90% success rate.