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Sun, Peng, Boukerche, Azzedine.  2017.  Analysis of Underwater Target Detection Probability by Using Autonomous Underwater Vehicles. Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks. :39–42.

Due to the trend of under-ocean exploration, realtime monitoring or long-term surveillance of the under-ocean environment, e.g., real-time monitoring for under-ocean oil drilling, is imperative. Underwater wireless sensor networks could provide an optimal option, and have recently attracted intensive attention from researchers. Nevertheless, terrestrial wireless sensor networks (WSNs) have been well investigated and solved by many approaches that rely on the electromagnetic/optical transmission techniques. Deploying an applicable underwater wireless sensor network is still a big challenge. Due to critical conditions of the underwater environment (e.g., high pressure, high salinity, limited energy etc), the cost of the underwater sensor is significant. The dense sensor deployment is not applicable in the underwater condition. Therefore, Autonomous Underwater Vehicle (AUV) becomes an alternative option for implementing underwater surveillance and target detection. In this article, we present a framework to theoretically analyze the target detection probability in the underwater environment by using AUVs. The experimental results further verify our theoretical results.

Sun, Pengfei, Han, Rui, Zhang, Mingbo, Zonouz, Saman.  2016.  Trace-free Memory Data Structure Forensics via Past Inference and Future Speculations. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :570–582.

A yet-to-be-solved but very vital problem in forensics analysis is accurate memory dump data type reverse engineering where the target process is not a priori specified and could be any of the running processes within the system. We present ReViver, a lightweight system-wide solution that extracts data type information from the memory dump without its past execution traces. ReViver constructs the dump's accurate data structure layout through collection of statistical information about possible past traces, forensics inspection of the present memory dump, and speculative investigation of potential future executions of the suspended process. First, ReViver analyzes a heavily instrumented set of execution paths of the same executable that end in the same state of the memory dump (the eip and call stack), and collects statistical information the potential data structure instances on the captured dump. Second, ReViver uses the statistical information and performs a word-byword data type forensics inspection of the captured memory dump. Finally, ReViver revives the dump's execution and explores its potential future execution paths symbolically. ReViver traces the executions including library/system calls for their known argument/return data types, and performs backward taint analysis to mark the dump bytes with relevant data type information. ReViver's experimental results on real-world applications are very promising (98.1%), and show that ReViver improves the accuracy of the past trace-free memory forensics solutions significantly while maintaining a negligible runtime performance overhead (1.8%).

Sun, Pengfei, Garcia, Luis, Zonouz, Saman.  2019.  Tell Me More Than Just Assembly! Reversing Cyber-Physical Execution Semantics of Embedded IoT Controller Software Binaries. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :349–361.
The safety of critical cyber-physical IoT devices hinges on the security of their embedded software that implements control algorithms for monitoring and control of the associated physical processes, e.g., robotics and drones. Reverse engineering of the corresponding embedded controller software binaries enables their security analysis by extracting high-level, domain-specific, and cyber-physical execution semantic information from executables. We present MISMO, a domain-specific reverse engineering framework for embedded binary code in emerging cyber-physical IoT control application domains. The reverse engineering outcomes can be used for firmware vulnerability assessment, memory forensics analysis, targeted memory data attacks, or binary patching for dynamic selective memory protection (e.g., important control algorithm parameters). MISMO performs semantic-matching at an algorithmic level that can help with the understanding of any possible cyber-physical security flaws. MISMO compares low-level binary symbolic values and high-level algorithmic expressions to extract domain-specific semantic information for the binary's code and data. MISMO enables a finer-grained understanding of the controller by identifying the specific control and state estimation algorithms used. We evaluated MISMO on 2,263 popular firmware binaries by 30 commercial vendors from 6 application domains including drones, self-driving cars, smart homes, robotics, 3D printers, and the Linux kernel controllers. The results show that MISMO can accurately extract the algorithm-level semantics of the embedded binary code and data regions. We discovered a zero-day vulnerability in the Linux kernel controllers versions 3.13 and above.
Sun, R., Yuan, X., Lee, A., Bishop, M., Porter, D. E., Li, X., Gregio, A., Oliveira, D..  2017.  The dose makes the poison \#x2014; Leveraging uncertainty for effective malware detection. 2017 IEEE Conference on Dependable and Secure Computing. :123–130.

Malware has become sophisticated and organizations don't have a Plan B when standard lines of defense fail. These failures have devastating consequences for organizations, such as sensitive information being exfiltrated. A promising avenue for improving the effectiveness of behavioral-based malware detectors is to combine fast (usually not highly accurate) traditional machine learning (ML) detectors with high-accuracy, but time-consuming, deep learning (DL) models. The main idea is to place software receiving borderline classifications by traditional ML methods in an environment where uncertainty is added, while software is analyzed by time-consuming DL models. The goal of uncertainty is to rate-limit actions of potential malware during deep analysis. In this paper, we describe Chameleon, a Linux-based framework that implements this uncertain environment. Chameleon offers two environments for its OS processes: standard - for software identified as benign by traditional ML detectors - and uncertain - for software that received borderline classifications analyzed by ML methods. The uncertain environment will bring obstacles to software execution through random perturbations applied probabilistically on selected system calls. We evaluated Chameleon with 113 applications from common benchmarks and 100 malware samples for Linux. Our results show that at threshold 10%, intrusive and non-intrusive strategies caused approximately 65% of malware to fail accomplishing their tasks, while approximately 30% of the analyzed benign software to meet with various levels of disruption (crashed or hampered). We also found that I/O-bound software was three times more affected by uncertainty than CPU-bound software.

Sun, S., Zhang, H., Du, Y..  2017.  The electromagnetic leakage analysis based on arithmetic operation of FPGA. 2017 IEEE 5th International Symposium on Electromagnetic Compatibility (EMC-Beijing). :1–5.

The chips in working state have electromagnetic energy leakage problem. We offer a method to analyze the problem of electromagnetic leakage when the chip is running. We execute a sequence of addition and subtraction arithmetic instructions on FPGA chip, then we use the near-field probe to capture the chip leakage of electromagnetic signals. The electromagnetic signal is collected for analysis and processing, the parts of addition and subtraction are classified and identified by SVM. In this paper, for the problem of electromagnetic leakage, six sets of data were collected for analysis and processing. Good results were obtained by using this method.

Sun, S. C., Guo, W..  2020.  Approximate Symbolic Explanation for Neural Network Enabled Water-Filling Power Allocation. 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). :1–4.
Water-filling (WF) is a well-established iterative solution to optimal power allocation in parallel fading channels. Slow iterative search can be impractical for allocating power to a large number of OFDM sub-channels. Neural networks (NN) can transform the iterative WF threshold search process into a direct high-dimensional mapping from channel gain to transmit power solution. Our results show that the NN can perform very well (error 0.05%) and can be shown to be indeed performing approximate WF power allocation. However, there is no guarantee on the NN is mapping between channel states and power output. Here, we attempt to explain the NN power allocation solution via the Meijer G-function as a general explainable symbolic mapping. Our early results indicate that whilst the Meijer G-function has universal representation potential, its large search space means finding the best symbolic representation is challenging.
Sun, Shi-Feng, Gu, Dawu, Liu, Joseph K., Parampalli, Udaya, Yuen, Tsz Hon.  2016.  Efficient Construction of Completely Non-Malleable CCA Secure Public Key Encryption. Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. :901–906.
Non-malleability is an important and intensively studied security notion for many cryptographic primitives. In the context of public key encryption, this notion means it is infeasible for an adversary to transform an encryption of some message m into one of a related message m' under the given public key. Although it has provided a strong security property for many applications, it still does not suffice for some scenarios like the system where the users could issue keys on-the-fly. In such settings, the adversary may have the power to transform the given public key and the ciphertext. To withstand such attacks, Fischlin introduced a stronger notion, known as complete non-malleability, which requires that the non-malleability property be preserved even for the adversaries attempting to produce a ciphertext of some related message under the transformed public key. To date, many schemes satisfying this stronger security have been proposed, but they are either inefficient or proved secure in the random oracle model. In this work, we put forward a new encryption scheme in the common reference string model. Based on the standard DBDH assumption, the proposed scheme is proved completely non-malleable secure against adaptive chosen ciphertext attacks in the standard model. In our scheme, the well-formed public keys and ciphertexts could be publicly recognized without drawing support from unwieldy techniques like non-interactive zero knowledge proofs or one-time signatures, thus achieving a better performance.
Sun, Shi-Feng, Yuan, Xingliang, Liu, Joseph K., Steinfeld, Ron, Sakzad, Amin, Vo, Viet, Nepal, Surya.  2018.  Practical Backward-Secure Searchable Encryption from Symmetric Puncturable Encryption. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :763-780.

Symmetric Searchable Encryption (SSE) has received wide attention due to its practical application in searching on encrypted data. Beyond search, data addition and deletion are also supported in dynamic SSE schemes. Unfortunately, these update operations leak some information of updated data. To address this issue, forward-secure SSE is actively explored to protect the relations of newly updated data and previously searched keywords. On the contrary, little work has been done in backward security, which enforces that search should not reveal information of deleted data. In this paper, we propose the first practical and non-interactive backward-secure SSE scheme. In particular, we introduce a new form of symmetric encryption, named symmetric puncturable encryption (SPE), and construct a generic primitive from simple cryptographic tools. Based on this primitive, we then present a backward-secure SSE scheme that can revoke a server's searching ability on deleted data. We instantiate our scheme with a practical puncturable pseudorandom function and implement it on a large dataset. The experimental results demonstrate its efficiency and scalability. Compared to the state-of-the-art, our scheme achieves a speedup of almost 50x in search latency, and a saving of 62% in server storage consumption.

Sun, Shi-Feng, Gu, Dawu, Liu, Joseph K., Parampalli, Udaya, Yuen, Tsz Hon.  2016.  Efficient Construction of Completely Non-Malleable CCA Secure Public Key Encryption. Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. :901–906.

Non-malleability is an important and intensively studied security notion for many cryptographic primitives. In the context of public key encryption, this notion means it is infeasible for an adversary to transform an encryption of some message m into one of a related message m' under the given public key. Although it has provided a strong security property for many applications, it still does not suffice for some scenarios like the system where the users could issue keys on-the-fly. In such settings, the adversary may have the power to transform the given public key and the ciphertext. To withstand such attacks, Fischlin introduced a stronger notion, known as complete non-malleability, which requires that the non-malleability property be preserved even for the adversaries attempting to produce a ciphertext of some related message under the transformed public key. To date, many schemes satisfying this stronger security have been proposed, but they are either inefficient or proved secure in the random oracle model. In this work, we put forward a new encryption scheme in the common reference string model. Based on the standard DBDH assumption, the proposed scheme is proved completely non-malleable secure against adaptive chosen ciphertext attacks in the standard model. In our scheme, the well-formed public keys and ciphertexts could be publicly recognized without drawing support from unwieldy techniques like non-interactive zero knowledge proofs or one-time signatures, thus achieving a better performance.

Sun, Shuang, Chen, Shudong, Du, Rong, Li, Weiwei, Qi, Donglin.  2019.  Blockchain Based Fine-Grained and Scalable Access Control for IoT Security and Privacy. 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC). :598–603.
In this paper, we focuses on an access control issue in the Internet of Things (IoT). Generally, we firstly propose a decentralized IoT system based on blockchain. Then we establish a secure fine-grained access control strategies for users, devices, data, and implement the strategies with smart contract. To trigger the smart contract, we design different transactions. Finally, we use the multi-index table struct for the access right's establishment, and store the access right into Key-Value database to improve the scalability of the decentralized IoT system. In addition, to improve the security of the system we also store the access records on the blockchain and database.
Sun, Weiqi, Li, Yuanlong, Shi, Liangren.  2020.  The Performance Evaluation and Resilience Analysis of Supply Chain Based on Logistics Network. 2020 39th Chinese Control Conference (CCC). :5772—5777.
With the development of globalization, more and more enterprises are involved in the supply chain network with increasingly complex structure. In this paper, enterprises and relations in the logistics network are abstracted as nodes and edges of the complex network. A graph model for a supply chain network to specified industry is constructed, and the Neo4j graph database is employed to store the graph data. This paper uses the theoretical research tool of complex network to model and analyze the supply chain, and designs a supply chain network evaluation system which include static and dynamic measurement indexes according to the statistical characteristics of complex network. In this paper both the static and dynamic resilience characteristics of the the constructed supply chain network are evaluated from the perspective of complex network. The numeric experimental simulations are conducted for validation. This research has practical and theoretical significance for enterprises to make strategies to improve the anti-risk capability of supply chain network based on logistics network information.
Sun, Wenhua, Wang, Xiaojuan, Jin, Lei.  2019.  An Efficient Hash-Tree-Based Algorithm in Mining Sequential Patterns with Topology Constraint. 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). :2782–2789.
Warnings happen a lot in real transmission networks. These warnings can affect people's lives. It is significant to analyze the alarm association rules in the network. Many algorithms can help solve this problem but not considering the actual physical significance. Therefore, in this study, we mine the association rules in warning weblogs based on a sequential mining algorithm (GSP) with topology structure. We define a topology constraint from network physical connection data. Under the topology constraint, network nodes have topology relation if they are directly connected or have a common adjacency node. In addition, due to the large amount of data, we implement the hash-tree search method to improve the mining efficiency. The theoretical solution is feasible and the simulation results verify our method. In simulation, the topology constraint improves the accuracy for 86%-96% and decreases the run time greatly at the same time. The hash-tree based mining results show that hash tree efficiency improvements are in 3-30% while the number of patterns remains unchanged. In conclusion, using our method can mine association rules efficiently and accurately in warning weblogs.
Sun, Wenwen, Li, Yi, Guan, Shaopeng.  2019.  An Improved Method of DDoS Attack Detection for Controller of SDN. 2019 IEEE 2nd International Conference on Computer and Communication Engineering Technology (CCET). :249–253.
For controllers of Software Defined Network (SDN), Distributed Denial of Service (DDoS) attacks are still the simplest and most effective way to attack. Aiming at this problem, a real-time DDoS detection attack method for SDN controller is proposed. The method first uses the entropy to detect whether the flow is abnormal. After the abnormal warning is issued, the flow entry of the OpenFlow switch is obtained, and the DDoS attack feature in the SDN environment is analyzed to extract important features related to the attack. The BiLSTM-RNN neural network algorithm is used to train the data set, and the BiLSTM model is generated to classify the real-time traffic to realize the DDoS attack detection. Experiments show that, compared with other methods, this method can efficiently implement DDoS attack traffic detection and reduce controller overhead in SDN environment.
Sun, X., Liu, H., Zhang, M..  2016.  Multivariate symmetric cryptography with 2-dimesion chaotic disturbation. 2016 8th International Conference on Wireless Communications Signal Processing (WCSP). :1–4.

Multivariate public key cryptosystem acts as a signature system rather than encryption system due to the minus mode used in system. A multivariate encryption system with determinate equations in central map and chaotic shell protection for central map and affine map is proposed in this paper. The outputs of two-dimension chaotic system are discretized on a finite field to disturb the central map and affine map in multivariate cryptosystem. The determined equations meet the shortage of indeterminate equations in minus mode and make the general attack methods are out of tenable condition. The analysis shows the proposed multivariate symmetric encryption system based on chaotic shell is able to resist general attacks.

Sun, Xiaoli, Yang, Weiwei, Cai, Yueming, Tao, Liwei, Cai, Chunxiao.  2018.  Physical Layer Security in Wireless Information and Power Transfer Millimeter Wave Systems. 2018 24th Asia-Pacific Conference on Communications (APCC). :83–87.

This paper studies the physical layer security performance of a Simultaneous Wireless Information and Power Transfer (SWIPT) millimeter wave (mmWave) ultra-dense network under a stochastic geometry framework. Specifically, we first derive the energy-information coverage probability and secrecy probability in the considered system under time switching policies. Then the effective secrecy throughput (EST) which can characterize the trade-off between the energy coverage, secure and reliable transmission performance is derived. Theoretical analyses and simulation results reveal the design insights into the effects of various network parameters like, transmit power, time switching factor, transmission rate, confidential information rate, etc, on the secrecy performance. Specifically, it is impossible to realize the effective secrecy throughput improvement just by increasing the transmit power.

Sun, Xiaoyan, Dai, Jun, Liu, Peng, Singhal, Anoop, Yen, John.  2016.  Towards probabilistic identification of zero-day attack paths. 2016 IEEE Conference on Communications and Network Security (CNS). :64–72.
Zero-day attacks continue to challenge the enterprise network security defense. A zero-day attack path is formed when a multi-step attack contains one or more zero-day exploits. Detecting zero-day attack paths in time could enable early disclosure of zero-day threats. In this paper, we propose a probabilistic approach to identify zero-day attack paths and implement a prototype system named ZePro. An object instance graph is first built from system calls to capture the intrusion propagation. To further reveal the zero-day attack paths hiding in the instance graph, our system constructs an instance-graph-based Bayesian network. By leveraging intrusion evidence, the Bayesian network can quantitatively compute the probabilities of object instances being infected. The object instances with high infection probabilities reveal themselves and form the zero-day attack paths. The experiment results show that our system can effectively identify zero-day attack paths.
Sun, Xuezi, Xu, Guangxian, Liu, Chao.  2019.  A Network Coding Optimization Scheme for Niche Algorithm based on Security Performance. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 1:1969—1972.

The network coding optimization based on niche genetic algorithm can observably reduce the network overhead of encoding technology, however, security issues haven't been considered in the coding operation. In order to solve this problem, we propose a network coding optimization scheme for niche algorithm based on security performance (SNGA). It is on the basis of multi-target niche genetic algorithm(NGA)to construct a fitness function which with k-secure network coding mechanism, and to ensure the realization of information security and achieve the maximum transmission of the network. The simulation results show that SNGA can effectively improve the security of network coding, and ensure the running time and convergence speed of the optimal solution.

Sun, Xuguang, Zhou, Yi, Shu, Xiaofeng.  2018.  Multi-Channel Linear Prediction Speech Dereverberation Algorithm Based on QR-RLS Adaptive Filter. Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing. :109–113.

This paper proposes a multi-channel linear prediction (MCLP) speech dereverberation algorithm based on QR-decomposition recursive least squares (QR-RLS) adaptive filter, which can avoid the possible instability caused by the RLS algorithm, and achieve same speech dereverberation performance as the prototype MCLP dereverberation algorithm based on RLS. This can be confirmed by the theoretical derivation and experiments. Thus, the proposed algorithm can be a good alternative for practical speech applications.

Sun, Y., Zhang, L., Zhao, C..  2018.  A Study of Network Covert Channel Detection Based on Deep Learning. 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC). :637-641.

Information security has become a growing concern. Computer covert channel which is regarded as an important area of information security research gets more attention. In order to detect these covert channels, a variety of detection algorithms are proposed in the course of the research. The algorithms of machine learning type show better results in these detection algorithms. However, the common machine learning algorithms have many problems in the testing process and have great limitations. Based on the deep learning algorithm, this paper proposes a new idea of network covert channel detection and forms a new detection model. On the one hand, this algorithmic model can detect more complex covert channels and, on the other hand, greatly improve the accuracy of detection due to the use of a new deep learning model. By optimizing this test model, we can get better results on the evaluation index.

Sun, Y., Wang, J., Lu, Z..  2019.  Asynchronous Parallel Surrogate Optimization Algorithm Based on Ensemble Surrogating Model and Stochastic Response Surface Method. :74—84.
{Surrogate model-based optimization algorithm remains as an important solution to expensive black-box function optimization. The introduction of ensemble model enables the algorithm to automatically choose a proper model integration mode and adapt to various parameter spaces when dealing with different problems. However, this also significantly increases the computational burden of the algorithm. On the other hand, utilizing parallel computing resources and improving efficiency of black-box function optimization also require combination with surrogate optimization algorithm in order to design and realize an efficient parallel parameter space sampling mechanism. This paper makes use of parallel computing technology to speed up the weight updating related computation for the ensemble model based on Dempster-Shafer theory, and combines it with stochastic response surface method to develop a novel parallel sampling mechanism for asynchronous parameter optimization. Furthermore, it designs and implements corresponding parallel computing framework and applies the developed algorithm to quantitative trading strategy tuning in financial market. It is verified that the algorithm is both feasible and effective in actual application. The experiment demonstrates that with guarantee of optimizing performance, the parallel optimization algorithm can achieve excellent accelerating effect.
Sun, Yu, Zhao, Xiang.  2020.  On the Secrecy Performance of Random Mobile User in Visible Light Communication Systems. 2020 12th International Conference on Communication Software and Networks (ICCSN). :172–177.
For most of the current research on physical-layer security in indoor visible light communication (VLC) systems, a static communication environment was mainly considered, where secure communication about static users was investigated. However, much secure problems remain to be settled about mobile users. To improve the secrecy performance of mobile users, a two-dimensional circular optical atto-cell with security protected zone is considered. The proposed VLC systems include a LED transmitter Alice, a mobile user Bob and a passive eavesdropper Eve. A typical random waypoint model (RWP) being assumed, the secrecy outage probability (SOP) and secrecy throughput (ST) have been investigated for mobile users in VLC systems. The theoretical analysis results have been verified through Monte Carlo simulations. The simulation results show that the secrecy performance of mobile users in VLC can be improved by enlarging the radius of protected zone, and it also depends on the target secrecy rate and the LEDs' configuration.
Sun, Yuanyuan, Hua, Yu, Liu, Xue, Cao, Shunde, Zuo, Pengfei.  2017.  DLSH: A Distribution-aware LSH Scheme for Approximate Nearest Neighbor Query in Cloud Computing. Proceedings of the 2017 Symposium on Cloud Computing. :242–255.
Cloud computing needs to process and analyze massive high-dimensional data in a real-time manner. Approximate queries in cloud computing systems can provide timely queried results with acceptable accuracy, thus alleviating the consumption of a large amount of resources. Locality Sensitive Hashing (LSH) is able to maintain the data locality and support approximate queries. However, due to randomly choosing hash functions, LSH has to use too many functions to guarantee the query accuracy. The extra computation and storage overheads exacerbate the real performance of LSH. In order to reduce the overheads and deliver high performance, we propose a distribution-aware scheme, called DLSH, to offer cost-effective approximate nearest neighbor query service for cloud computing. The idea of DLSH is to leverage the principal components of the data distribution as the projection vectors of hash functions in LSH, further quantify the weight of each hash function and adjust the interval value in each hash table. We then refine the queried result set based on the hit frequency to significantly decrease the time overhead of distance computation. Extensive experiments in a large-scale cloud computing testbed demonstrate significant improvements in terms of multiple system performance metrics. We have released the source code of DLSH for public use.
Sun, Yueming, Zhang, Yi, Chen, Yunfei, Jin, Roger.  2016.  Conversational Recommendation System with Unsupervised Learning. Proceedings of the 10th ACM Conference on Recommender Systems. :397–398.

We will demonstrate a conversational products recommendation agent. This system shows how we combine research in personalized recommendation systems with research in dialogue systems to build a virtual sales agent. Based on new deep learning technologies we developed, the virtual agent is capable of learning how to interact with users, how to answer user questions, what is the next question to ask, and what to recommend when chatting with a human user. Normally a descent conversational agent for a particular domain requires tens of thousands of hand labeled conversational data or hand written rules. This is a major barrier when launching a conversation agent for a new domain. We will explore and demonstrate the effectiveness of the learning solution even when there is no hand written rules or hand labeled training data.

Sun, Yunhe, Yang, Dongsheng, Meng, Lei, Gao, Xiaoting, Hu, Bo.  2018.  Universal Framework for Vulnerability Assessment of Power Grid Based on Complex Networks. 2018 Chinese Control And Decision Conference (CCDC). :136-141.

Traditionally, power grid vulnerability assessment methods are separated to the study of nodes vulnerability and edges vulnerability, resulting in the evaluation results are not accurate. A framework for vulnerability assessment is still required for power grid. Thus, this paper proposes a universal method for vulnerability assessment of power grid by establishing a complex network model with uniform weight of nodes and edges. The concept of virtual edge is introduced into the distinct weighted complex network model of power system, and the selection function of edge weight and virtual edge weight are constructed based on electrical and physical parameters. In addition, in order to reflect the electrical characteristics of power grids more accurately, a weighted betweenness evaluation index with transmission efficiency is defined. Finally, the method has been demonstrated on the IEEE 39 buses system, and the results prove the effectiveness of the proposed method.

Sun, YunZhe, Zhao, QiXi, Zhang, PeiYun.  2019.  Trust Degree Calculation Method Based on Trust Blockchain Node. 2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). :122–127.
Due to the diversity and mobility of blockchain network nodes and the decentralized nature of blockchain networks, traditional trust value evaluation indicators cannot be directly used. In order to obtain trusted nodes, a trustworthiness calculation method based on trust blockchain nodes is proposed. Different from the traditional P2P network trust value calculation, the trust blockchain not only acquires the working state of the node, but also collects the special behavior information of the node, and calculates the joining time by synthesizing the trust value generated by the node transaction and the trust value generated by the node behavior. After the attenuation factor is comprehensively evaluated, the trusted nodes are selected to effectively ensure the security of the blockchain network environment, while reducing the average transaction delay and increasing the block rate.