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Zhou, Zan, Xu, Changqiao, Ma, Tengchao, Kuang, Xiaohui.  2020.  Multi-vNIC Intelligent Mutation: A Moving Target Defense to thwart Client-side DNS Cache Attack. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—6.
As massive research efforts are poured into server-side DNS security enhancement in online cloud service platforms, sophisticated APTs tend to develop client-side DNS attacks, where defenders only have limited resources and abilities. The collaborative DNS attack is a representative newest client-side paradigm to stealthily undermine user cache by falsifying DNS responses. Different from existing static methods, in this paper, we propose a moving target defense solution named multi-vNIC intelligent mutation to free defenders from arduous work and thwart elusive client-side DNS attack in the meantime. Multiple virtual network interface cards are created and switched in a mutating manner. Thus attackers have to blindly guess the actual NIC with a high risk of exposure. Firstly, we construct a dynamic game-theoretic model to capture the main characteristics of both attacker and defender. Secondly, a reinforcement learning mechanism is developed to generate adaptive optimal defense strategy. Experiment results also highlight the security performance of our defense method compared to several state-of-the-art technologies.
Zhou, Z., Qian, L., Xu, H..  2019.  Intelligent Decentralized Dynamic Power Allocation in MANET at Tactical Edge based on Mean-Field Game Theory. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :604—609.

In this paper, decentralized dynamic power allocation problem has been investigated for mobile ad hoc network (MANET) at tactical edge. Due to the mobility and self-organizing features in MANET and environmental uncertainties in the battlefield, many existing optimal power allocation algorithms are neither efficient nor practical. Furthermore, the continuously increasing large scale of the wireless connection population in emerging Internet of Battlefield Things (IoBT) introduces additional challenges for optimal power allocation due to the “Curse of Dimensionality”. In order to address these challenges, a novel Actor-Critic-Mass algorithm is proposed by integrating the emerging Mean Field game theory with online reinforcement learning. The proposed approach is able to not only learn the optimal power allocation for IoBT in a decentralized manner, but also effectively handle uncertainties from harsh environment at tactical edge. In the developed scheme, each agent in IoBT has three neural networks (NN), i.e., 1) Critic NN learns the optimal cost function that minimizes the Signal-to-interference-plus-noise ratio (SINR), 2) Actor NN estimates the optimal transmitter power adjustment rate, and 3) Mass NN learns the probability density function of all agents' transmitting power in IoBT. The three NNs are tuned based on the Fokker-Planck-Kolmogorov (FPK) and Hamiltonian-Jacobian-Bellman (HJB) equation given in the Mean Field game theory. An IoBT wireless network has been simulated to evaluate the effectiveness of the proposed algorithm. The results demonstrate that the actor-critic-mass algorithm can effectively approximate the probability distribution of all agents' transmission power and converge to the target SINR. Moreover, the optimal decentralized power allocation is obtained through integrated mean-field game theory with reinforcement learning.

Zhou, Z., Yang, Y., Cai, Z., Yang, Y., Lin, L..  2019.  Combined Layer GAN for Image Style Transfer*. 2019 IEEE International Conference on Computational Electromagnetics (ICCEM). :1—3.

Image style transfer is an increasingly interesting topic in computer vision where the goal is to map images from one style to another. In this paper, we propose a new framework called Combined Layer GAN as a solution of dealing with image style transfer problem. Specifically, the edge-constraint and color-constraint are proposed and explored in the GAN based image translation method to improve the performance. The motivation of the work is that color and edge are fundamental vision factors for an image, while in the traditional deep network based approach, there is a lack of fine control of these factors in the process of translation and the performance is degraded consequently. Our experiments and evaluations show that our novel method with the edge and color constrains is more stable, and significantly improves the performance compared with the traditional methods.

Zhou, You, Zhou, Yian, Chen, Min, Chen, Shigang.  2017.  Persistent Spread Measurement for Big Network Data Based on Register Intersection. Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. :67–67.
Persistent spread measurement is to count the number of distinct elements that persist in each network flow for predefined time periods. It has many practical applications, including detecting long-term stealthy network activities in the background of normal-user activities, such as stealthy DDoS attack, stealthy network scan, or faked network trend, which cannot be detected by traditional flow cardinality measurement. With big network data, one challenge is to measure the persistent spreads of a massive number of flows without incurring too much memory overhead as such measurement may be performed at the line speed by network processors with fast but small on-chip memory. We propose a highly compact Virtual Intersection HyperLogLog (VI-HLL) architecture for this purpose. It achieves far better memory efficiency than the best prior work of V-Bitmap, and in the meantime drastically extends the measurement range. Theoretical analysis and extensive experiments demonstrate that VI-HLL provides good measurement accuracy even in very tight memory space of less than 1 bit per flow.
Zhou, Yiwen, Shen, Qili, Dong, Mianxiong, Ota, Kaoru, Wu, Jun.  2019.  Chaos-Based Delay-Constrained Green Security Communications for Fog-Enabled Information-Centric Multimedia Network. 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring). :1–6.
The Information-Centric Network possessing the content-centric features, is the innovative architecture of the next generation of network. Collaborating with fog computing characterized by its strong edge power, ICN will become the development trend of the future network. The emergence of Information-Centric Multimedia Network (ICMN) can meet the increasing demand for transmission of multimedia streams in the current Internet environment. The data transmission has become more delay-constrained and convenient because of the distributed storage, the separation between the location of information and terminals, and the strong cacheability of each node in ICN. However, at the same time, the security of the multimedia streams in the delivery process still requires further protection against wiretapping, interception or attacking. In this paper, we propose the delay-constrained green security communications for ICMN based on chaotic encryption and fog computing so as to transmit multimedia streams in a more secure and time-saving way. We adapt a chaotic cryptographic method to ICMN, implementing the encryption and decryption of multimedia streams. Meanwhile, the network edge capability to process the encryption and decryption is enhanced. Thanks to the fog computing, the strengthened transmission speed of the multimedia streams can fulfill the need for short latency. The work in the paper is of great significance to improve the green security communications of multimedia streams in ICMN.
Zhou, Yejun, Qiu, Lede, Yu, Hang, Sun, Chunhui.  2018.  Study on Security Technology of Internet of Things Based on Network Coding. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). :353–357.
Along with the continuous progress of the information technology, Internet of Things is the inevitable way for realizing the fusion of communication and traditional network technology. Network coding, an important breakthrough in the field of communication, has many applied advantages in information network. This article analyses the eavesdropping problem of Internet of Things and presents an information secure network coding scheme against the eavesdropping adversaries. We show that, if the number of links the adversaries can eavesdrop on is less than the max-flow of a network, the proposed coding scheme not only `achieves the prefect information secure condition but also the max-flow of the network.
Zhou, Yaqiu, Ren, Yongmao, Zhou, Xu, Yang, Wanghong, Qin, Yifang.  2019.  A Scientific Data Traffic Scheduling Algorithm Based on Software-Defined Networking. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :62–67.
Compared to ordinary Internet applications, the transfer of scientific data flows often has higher requirements for network performance. The network security devices and systems often affect the efficiency of scientific data transfer. As a new type of network architecture, Software-defined Networking (SDN) decouples the data plane from the control plane. Its programmability allows users to customize the network transfer path and makes the network more intelligent. The Science DMZ model is a private network for scientific data flow transfer, which can improve performance under the premise of ensuring network security. This paper combines SDN with Science DMZ, designs and implements an SDN-based traffic scheduling algorithm considering the load of link. In addition to distinguishing scientific data flow from common data flow, the algorithm further distinguishes the scientific data flows of different applications and performs different traffic scheduling of scientific data for specific link states. Experiments results proved that the algorithm can effectively improve the transmission performance of scientific data flow.
Zhou, Y., Shi, J., Zhang, J., Chi, N..  2018.  Spectral Scrambling for High-security PAM-8 Underwater Visible Light Communication System. 2018 Asia Communications and Photonics Conference (ACP). :1–3.
We propose a spectral scrambling scheme to enhance physical layer security for an underwater VLC system which also simplifies the real-value signal generation procedure. A 1.08-Gb/s PAM-8 encrypted data over 1.2m transmission is experimentally demonstrated.
Zhou, Y., Zeng, Z..  2019.  Info-Retrieval with Relevance Feedback using Hybrid Learning Scheme for RS Image. 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :135—138.

Relevance feedback can be considered as a learning problem. It has been extensively used to improve the performance of retrieval multimedia information. In this paper, after the relevance feedback upon content-based image retrieval (CBIR) discussed, a hybrid learning scheme on multi-target retrieval (MTR) with relevance feedback was proposed. Suppose the symbolic image database (SID) of object-level with combined image metadata and feature model was constructed. During the interactive query for remote sensing image, we calculate the similarity metric so as to get the relevant image sets from the image library. For the purpose of further improvement of the precision of image retrieval, a hybrid learning scheme parameter also need to be chosen. As a result, the idea of our hybrid learning scheme contains an exception maximization algorithm (EMA) used for retrieving the most relevant images from SID and an algorithm called supported vector machine (SVM) with relevance feedback used for learning the feedback information substantially. Experimental results show that our hybrid learning scheme with relevance feedback on MTR can improve the performance and accuracy compared the basic algorithms.

Zhou, Xinyan, Ji, Xiaoyu, Yan, Chen, Deng, Jiangyi, Xu, Wenyuan.  2019.  NAuth: Secure Face-to-Face Device Authentication via Nonlinearity. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :2080–2088.
With the increasing prevalence of mobile devices, face-to-face device-to-device (D2D) communication has been applied to a variety of daily scenarios such as mobile payment and short distance file transfer. In D2D communications, a critical security problem is verifying the legitimacy of devices when they share no secrets in advance. Previous research addressed the problem with device authentication and pairing schemes based on user intervention or exploiting physical properties of the radio or acoustic channels. However, a remaining challenge is to secure face-to-face D2D communication even in the middle of a crowd, within which an attacker may hide. In this paper, we present Nhuth, a nonlinearity-enhanced, location-sensitive authentication mechanism for such communication. Especially, we target at the secure authentication within a limited range such as 20 cm, which is the common case for face-to-face scenarios. Nhuth contains averification scheme based on the nonlinear distortion of speaker-microphone systems and a location-based-validation model. The verification scheme guarantees device authentication consistency by extracting acoustic nonlinearity patterns (ANP) while the validation model ensures device legitimacy by measuring the time difference of arrival (TDOA) at two microphones. We analyze the security of Nhuth theoretically and evaluate its performance experimentally. Results show that Nhuth can verify the device legitimacy in the presence of nearby attackers.
Zhou, Xingyu, Li, Yi, Barreto, Carlos A., Li, Jiani, Volgyesi, Peter, Neema, Himanshu, Koutsoukos, Xenofon.  2019.  Evaluating Resilience of Grid Load Predictions under Stealthy Adversarial Attacks. 2019 Resilience Week (RWS). 1:206–212.
Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction loss while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network.
Zhou, Xiaojun, Lin, Ping, Li, Zhiyong, Wang, Yunpeng, Tan, Wei, Huang, Meng.  2019.  Security of Big Data Based on the Technology of Cloud Computing. 2019 4th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :703—7033.
To solve the problem of big data security and privacy protection, and expound the concept of cloud computing, big data and the relationship between them, the existing security and privacy protection method characteristic and problems were studied. A reference model is proposed which is based on cloud platform. In this model the physical level, data layer, interface layer and application layer step by step in to implement the system security risk early warning and threat perception, this provides an effective solution for the research of big data security. At the same time, a future research direction that uses the blockchain to solve cloud security and privacy protection is also pointed out.
Zhou, X..  2020.  Improvement of information System Audit to Deal With Network Information Security. 2020 International Conference on Communications, Information System and Computer Engineering (CISCE). :93–96.
With the rapid development of information technology and the increasing popularity of information and communication technology, the information age has come. Enterprises must adapt to changes in the times, introduce network and computer technologies in a timely manner, and establish more efficient and reasonable information systems and platforms. Large-scale information system construction is inseparable from related audit work, and network security risks have become an important part of information system audit concerns. This paper analyzes the objectives and contents of information system audits under the background of network information security through theoretical analysis, and on this basis, proposes how the IS audit work will be carried out.
Zhou, X., Yao, X., Li, H., Ma, J..  2017.  A bisectional multivariate quadratic equation system for RFID anti-counterfeiting. 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA). :19–23.

This paper proposes a novel scheme for RFID anti-counterfeiting by applying bisectional multivariate quadratic equations (BMQE) system into an RF tag data encryption. In the key generation process, arbitrarily choose two matrix sets (denoted as A and B) and a base Rab such that [AB] = λRABT, and generate 2n BMQ polynomials (denoted as p) over finite field Fq. Therefore, (Fq, p) is taken as a public key and (A, B, λ) as a private key. In the encryption process, the EPC code is hashed into a message digest dm. Then dm is padded to d'm which is a non-zero 2n×2n matrix over Fq. With (A, B, λ) and d'm, Sm is formed as an n-vector over F2. Unlike the existing anti-counterfeit scheme, the one we proposed is based on quantum cryptography, thus it is robust enough to resist the existing attacks and has high security.

Zhou, X., Lu, Y., Wang, Y., Yan, X..  2018.  Overview on Moving Target Network Defense. 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). :821–827.
Moving Target Defense (MTD) is a research hotspot in the field of network security. Moving Target Network Defense (MTND) is the implementation of MTD at network level. Numerous related works have been proposed in the field of MTND. In this paper, we focus on the scope and area of MTND, systematically present the recent representative progress from four aspects, including IP address and port mutation, route mutation, fingerprint mutation and multiple mutation, and put forward the future development directions. Several new perspectives and elucidations on MTND are rendered.
Zhou, Wenxuan, Croft, Jason, Liu, Bingzhe, Caesar, Matthew.  2017.  NEAt: Network Error Auto-Correct. Proceedings of the Symposium on SDN Research. :157–163.

Configuring and maintaining an enterprise network is a challenging and error-prone process. Administrators must often consider security policies from a variety of sources simultaneously, including regulatory requirements, industry standards, and to mitigate attack vectors. Erroneous implementation of a policy, however, can result in costly data breaches and intrusions. Relying on humans to discover and troubleshoot violations is slow and prone to error, considering the speed at which new attack vectors propagate and the increasing network dynamics, partly an effect of SDN. To ensure the network is always in a state consistent with the desired policies, administrators need frameworks to automatically diagnose and repair violations in real-time. To address this problem, we present NEAt, a system analogous to a smartphone's autocorrect feature that enables on-the-fly repair to policy-violating updates. NEAt modifies the forwarding behavior of updates to automatically repair violations of properties such as reachability, service chaining, and segmentation. NEAt sits between an SDN controller and the forwarding devices, and intercepts updates proposed by SDN applications. If an update violates the policy defined by an administrator, such as reachability or segmentation, NEAt transforms the update into one that complies with the policy. Unlike domain-specific languages or synthesis platforms, NEAt allows enterprise networks to leverage the advanced functionality of SDN applications while simultaneously achieving strong, automated enforcement of general policies.

Zhou, Wei, Wang, Jin, Li, Lingzhi, Wang, Jianping, Lu, Kejie, Zhou, Xiaobo.  2019.  An Efficient Secure Coded Edge Computing Scheme Using Orthogonal Vector. 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom). :100—107.

In recent years, Edge Computing (EC) has attracted increasing attention for its advantages in handling latencysensitive and compute-intensive applications. It is becoming a widespread solution to solve the last mile problem of cloud computing. However, in actual EC deployments, data confidentiality becomes an unignorable issue because edge devices may be untrusted. In this paper, a secure and efficient edge computing scheme based on linear coding is proposed. Generally, linear coding can be utilized to achieve data confidentiality by encoding random blocks with original data blocks before they are distributed to unreliable edge nodes. However, the addition of a large amount of irrelevant random blocks also brings great communication overhead and high decoding complexities. In this paper, we focus on the design of secure coded edge computing using orthogonal vector to protect the information theoretic security of the data matrix stored on edge nodes and the input matrix uploaded by the user device, while to further reduce the communication overhead and decoding complexities. In recent years, Edge Computing (EC) has attracted increasing attention for its advantages in handling latencysensitive and compute-intensive applications. It is becoming a widespread solution to solve the last mile problem of cloud computing. However, in actual EC deployments, data confidentiality becomes an unignorable issue because edge devices may be untrusted. In this paper, a secure and efficient edge computing scheme based on linear coding is proposed. Generally, linear coding can be utilized to achieve data confidentiality by encoding random blocks with original data blocks before they are distributed to unreliable edge nodes. However, the addition of a large amount of irrelevant random blocks also brings great communication overhead and high decoding complexities. In this paper, we focus on the design of secure coded edge computing using orthogonal vector to protect the information theoretic security of the data matrix stored on edge nodes and the input matrix uploaded by the user device, while to further reduce the communication overhead and decoding complexities.

Zhou, Wei, Yang, Weidong, Wang, Yan, Zhang, Hong.  2018.  Generalized Reconstruction-Based Contribution for Multiple Faults Diagnosis with Bayesian Decision. 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS). :813–818.
In fault diagnosis of industrial process, there are usually more than one variable that are faulty. When multiple faults occur, the generalized reconstruction-based contribution can be helpful while traditional RBC may make mistakes. Due to the correlation between the variables, these faults usually propagate to other normal variables, which is called smearing effect. Thus, it is helpful to consider the pervious fault diagnosis results. In this paper, a data-driven fault diagnosis method which is based on generalized RBC and bayesian decision is presented. This method combines multi-dimensional RBC and bayesian decision. The proposed method improves the diagnosis capability of multiple and minor faults with greater noise. A numerical simulation example is given to show the effectiveness and superiority of the proposed method.
Zhou, Pengyuan, Kangasharju, Jussi.  2016.  Profiling and Grouping Users to Edge Resources According to User Interest Similarity. Proceedings of the 2016 ACM Workshop on Cloud-Assisted Networking. :43–48.

Cloud computing provides a shared pool of resources for large-scale distributed applications. Recent trends such as fog computing and edge computing spread the workload of clouds closer towards the edge of the network and the users. Exploiting the edge resources efficiently requires managing the resources and directing user traffic to the correct edge servers. In this paper we propose to profile and group users according to their interest profiles. We consider edge caching as an example and through our evaluation show the potential benefits of directing users from the same group to the same caches. We investigate a range of workloads and parameters and the same conclusions apply. Our results highlight the importance of grouping users and demonstrate the potential benefits of this approach.

Zhou, Mengyu, Sui, Kaixin, Ma, Minghua, Zhao, Youjian, Pei, Dan, Moscibroda, Thomas.  2016.  MobiCamp: A Campus-wide Testbed for Studying Mobile Physical Activities. Proceedings of the 3rd International on Workshop on Physical Analytics. :1–6.

Ubiquitous WiFi infrastructure and smart phones offer a great opportunity to study physical activities. In this paper, we present MobiCamp, a large-scale testbed for studying mobility-related activities of residents on a campus. MobiCamp consists of \textasciitilde2,700 APs, \textasciitilde95,000 smart phones, and an App with \textasciitilde2,300 opt-in volunteer users. More specifically, we capture how mobile users interact with different types of buildings, with other users, and with classroom courses, etc. To achieve this goal, we first obtain a relatively complete coverage of the users' mobility traces by utilizing four types of information from SNMP and by relaxing the location granularity to roughly at the room level. Then the popular App provides user attributes (grade, gender, etc.) and fine-grained behavior information (phone usages, course timetables, etc.) of the sampled population. These detailed mobile data is then correlated with the mobility traces from the SNMP to estimate the entire campus population's physical activities. We use two applications to show the power of MobiCamp.

Zhou, Man, Wang, Qian, Yang, Jingxiao, Li, Qi, Xiao, Feng, Wang, Zhibo, Chen, Xiaofeng.  2018.  PatternListener: Cracking Android Pattern Lock Using Acoustic Signals. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :1775-1787.

Pattern lock has been widely used for authentication to protect user privacy on mobile devices (e.g., smartphones and tablets). Several attacks have been constructed to crack the lock. However, these approaches require the attackers to be either physically close to the target device or able to manipulate the network facilities (e.g., wifi hotspots) used by the victims. Therefore, the effectiveness of the attacks is highly sensitive to the setting of the environment where the users use the mobile devices. Also, these attacks are not scalable since they cannot easily infer patterns of a large number of users. Motivated by an observation that fingertip motions on the screen of a mobile device can be captured by analyzing surrounding acoustic signals on it, we propose PatternListener, a novel acoustic attack that cracks pattern lock by leveraging and analyzing imperceptible acoustic signals reflected by the fingertip. It leverages speakers and microphones of the victim's device to play imperceptible audio and record the acoustic signals reflected from the fingertip. In particular, it infers each unlock pattern by analyzing individual lines that are the trajectories of the fingertip and composed of the pattern. We propose several algorithms to construct signal segments for each line and infer possible candidates of each individual line according to the signal segments. Finally, we produce a tree to map all line candidates into grid patterns and thereby obtain the candidates of the entire unlock pattern. We implement a PatternListener prototype by using off-the-shelf smartphones and thoroughly evaluate it using 130 unique patterns. The real experimental results demonstrate that PatternListener can successfully exploit over 90% patterns in five attempts.

Zhou, Lu, Liu, Qiao, Wang, Yong, Li, Hui.  2017.  Secure Group Information Exchange Scheme for Vehicular Ad Hoc Networks. Personal Ubiquitous Comput.. 21:903–910.

In this paper, a novel secure information exchange scheme has been proposed for MIMO vehicular ad hoc networks (VANETs) through physical layer approach. In the scheme, a group of On Board Units (OBUs) exchange information with help of one Road Side Unit (RSU). By utilizing the key signal processing technique, i.e., Direction Rotation Alignment technique, the information to be exchanged of the two neighbor OBUs are aligned into a same direction to form summed signal at RSU or external eavesdroppers. With such summed signal, the RSU or the eavesdropper cannot recover the individual information from the OBUs. By regulating the transmission rate for each OBU, the information theoretic security could be achieved. The secrecy sum-rates of the proposed scheme are analyzed following the scheme. Finally, the numerical results are conducted to demonstrate the theoretical analysis.

Zhou, Lin, Feng, Jing, He, Haiguang, Mao, Zhijie, Chen, Yingmei, Gao, Mei, He, Zhuzhen.  2019.  A Construction Method of Security Mechanism Requirement for Wireless Access System Based on CC Standard. 2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS). :369–372.

Aiming at the incomplete and incomplete security mechanism of wireless access system in emergency communication network, this paper proposes a security mechanism requirement construction method for wireless access system based on security evaluation standard. This paper discusses the requirements of security mechanism construction in wireless access system from three aspects: the definition of security issues, the construction of security functional components and security assurance components. This method can comprehensively analyze the security threats and security requirements of wireless access system in emergency communication network, and can provide correct and reasonable guidance and reference for the establishment of security mechanism.

Zhou, Liming, Shan, Yingzi, Chen, Xiaopan.  2019.  An Anonymous Routing Scheme for Preserving Location Privacy in Wireless Sensor Networks. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :262-265.

Wireless sensor networks consist of various sensors that are deployed to monitor the physical world. And many existing security schemes use traditional cryptography theory to protect message content and contextual information. However, we are concerned about location security of nodes. In this paper, we propose an anonymous routing strategy for preserving location privacy (ARPLP), which sets a proxy source node to hide the location of real source node. And the real source node randomly selects several neighbors as receivers until the packets are transmitted to the proxy source. And the proxy source is randomly selected so that the adversary finds it difficult to obtain the location information of the real source node. Meanwhile, our scheme sets a branch area around the sink, which can disturb the adversary by increasing the routing branch. According to the analysis and simulation experiments, our scheme can reduce traffic consumption and communication delay, and improve the security of source node and base station.

Zhou, Liming, Shan, Yingzi.  2019.  Multi-branch Source Location Privacy Protection Scheme Based on Random Walk in WSNs. 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). :543–547.
In many applications, source nodes send the sensing information of the monitored objects and the sinks receive the transmitted data. Considering the limited resources of sensor nodes, location privacy preservation becomes an important issue. Although many schemes are proposed to preserve source or sink location security, few schemes can preserve the location security of source nodes and sinks. In order to solve this problem, we propose a novel of multi-branch source location privacy protection method based on random walk. This method hides the location of real source nodes by setting multiple proxy sources. And multiple neighbors are randomly selected by the real source node as receivers until a proxy source receives the packet. In addition, the proxy source is chosen randomly, which can prevent the attacker from obtaining the location-related data of the real source node. At the same time, the scheme sets up a branch interference area around the base station to interfere with the adversary by increasing routing branches. Simulation results describe that our scheme can efficiently protect source and sink location privacy, reduce the communication overhead, and prolong the network lifetime.