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20th IEEE International Conference on Communication Technology, ICCT 2020

作者： Xu, Hang Ren, Wei Liu, Zimian Chen, Jiajun Zhu, Junhu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728181417

Dynamic memory error detection technology is widely utilized in locating and mitigating memory-corruption vulnerabilities. High false negative in existing technologies impairs the effectiveness of detection, especially when the detector has no source code. We proposed a dynamic memory error detection method based on dynamic binary translation. It realized the detection by combining a call stack tracing method and an IR language level memory error detection method. Experiments show that the detection success rate of this method in the heap and stack memory destruction is more than 80%. © 2020 IEEE.

关键词： Error detection

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Stability Analysis of a Rumor Propagation Model in Complex Networks with Holling II Type Functional Response 6

Stability Analysis of a Rumor Propagation Model in Complex N...

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6th International Conference on Big Data and Information Analytics, BigDIA 2020

作者： Yu, Ze Zhou, Gang Luo, Junyong Zhang, Mengli Huang, Ningbo State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

In this paper, we propose a SH2I2 rumor propagation model considering self-learning mechanism and echo chamber situation with Holling type II functional response in homogeneous and heterogeneous networks. Firstly, we obtain the basic reproduction number through Jacobian Matrix method and calculate the equilibrium points. Secondly, we prove the existence of rumor-prevailing equilibrium point as well as the local and globally stabilities by Routh-Huiwitz judgment and Lyapunov function. Finally, we use theoretical analysis and numerical simulations to investigate the effect of parameters for suppressing the prevalence of rumors. © 2020 IEEE.

关键词： Heterogeneous networks

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Moving Target Defense Decision-Making Method: A Dynamic Markov Differential Game Model 7

Moving Target Defense Decision-Making Method: A Dynamic Mark...

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7th ACM Workshop on Moving Target Defense, MTD 2020

作者： Zhang, Hengwei Tan, Jinglei Liu, Xiaohu Wang, Jindong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781450380850

Today most of the moving target defense decision-making methods are based on models of a discrete dynamic game. To more accurately study network attack-defense strategies against continuous confrontations, we analyze offensive and defensive behavior from a dynamic perspective. We propose a moving target defense decision-making method based on a model of a dynamic Markov differential game. We implement dynamic analysis and deduction of multi-stage continuous attack and defense confrontations for scenarios of continuous real-Time network attack-defense. We take into account the influence of random factors and changes of the network system in the gaming process, combine differential gaming with the Markov decision-making method, and construct models of attack-defense games. We propose a solution for game equilibrium based on an objective function designed according to the total discounted payoff of the offensive and defensive game and the analysis of the characteristics of multi-staged game equilibrium. On this basis an optimal strategy selection method is designed. We apply and verify the game model and the defense strategy selection algorithm by using the moving target defense technique. We conduct simulations to verify the effectiveness and feasibility of the model and algorithm. © 2020 ACM.

关键词： Decision making

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Software supply chain analysis based on function similarity 4

Software supply chain analysis based on function similarity

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2020 4th International Conference on Electrical, Mechanical and Computer engineering, ICEMCE 2020

作者： Sun, Wenjie Shan, Zheng Liu, Fudong Li, Xingwei Qiao, Meng Zhang, Chunyan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

The supply of open source and open source components is growing at an alarming rate, while vulnerabilities in open source components are everywhere. Software supply chain analysis aims to discover third-party components and open source code used in a software, and analyze the software's dependence on components. In this paper, we propose a software component analysis method and a known vulnerabilities detecting method. By scanning the open source components of the binary file and conducting vulnerability analysis, the known vulnerabilities are detected. This paper mainly solves the problem of detecting known vulnerabilities in the supply chain of binary files. We conducted a case analysis and achieved good results. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Supply chains

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Thinking and practice of online teaching under COVID-19 Epidemic 2

Thinking and practice of online teaching under COVID-19 Epid...

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2nd International Conference on Computer Science and Educational Informatization, CSEI 2020

作者： Ping, Zhang Fudong, Liu Zheng, Shan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728170077

Response to the outbreak of COVID-19, classes in this semester are mainly replaced by online teaching to ensure continual learning. Teachers are actively exploring effective online teaching methods to provide a good learning experience for students at home. This paper presents an online teaching model based on the idea of Problem-Based Learning (PBL) and take 'Data Structure' as an example to discuss the design of teaching process and problems. © 2020 IEEE.

关键词： COVID-19

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Security-as-a-Service with Cyberspace Mimic Defense Technologies in Cloud 7th

Security-as-a-Service with Cyberspace Mimic Defense Technolo...

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7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021

作者： Wang, Junchao Pang, Jianmin Wei, Jin State Key Laboratory of Mathematical Engineering and Advanced Computing ZhengZhou China School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China

ISBN: (纸本)9789811659423

Users usually focus on the application-level requirements which are quite friendly and direct to them. However, there are no existing tools automating the application-level requirements to infrastructure provisioning and application deployment. Although some security issues have been solved during the development phase, the undiscovered vulnerabilities remain hidden threats to the application’s security. Cyberspace mimic defense (CMD) technologies can help to enhance the application’s security despite the existence of the vulnerability. In this paper, the concept of SECurity-as-a-Service (SECaaS) is proposed with CMD technologies in cloud environments. The experiment on it was implemented. It is found that the application’s security is greatly improved to meet the user’s security and performance requirements within budgets through SECaaS. The experimental results show that SECaaS can help the users to focus on application-level requirements (monetary costs, required security level, etc.) and automate the process of application orchestration. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Budget control

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An Extraction Method of Network Security Situation Elements Based on Gradient Lifting Decision Tree

An Extraction Method of Network Security Situation Elements ...

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advanced computing and Endogenous Security, International Conference on

作者： Zhaorui Ma Shicheng Zhang Yiheng Chang Qinglei Zhou Xinhao Hu Xia Li State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Zhengzhou University of Light Industry Zhengzhou China

ISBN: (纸本)9781665424509

The primary purpose of acquiring network security situation elements is to detect and discover potential security threats from discrete and isolated data. In the complex network environment, the existing network security situation element acquisition technology has the problems of low extraction accuracy and low extraction efficiency. To solve these problems, a method for extracting network security situation elements based on Gradient Boosting Decision Tree (GBDT) is proposed. This method uses the attribute reduction function of rough set to preprocess the original data, which can effectively reduce redundancy. Furthermore, the initial parameters of GBDT are optimized by quantum particle swarm optimization (QPSO) algorithm to improve stability. Finally, the optimized GBDT classifier is used to classify and train with the reduced data set, and the accuracy of the final classifier is continuously improved by reducing the deviation via iterative optimization. Experiments demonstrate that the proposed algorithm achieves significant results and outperforms several state-of-the-art algorithms to the extraction of network security situation elements on the UNSW-NB15 dataset.

关键词： Termination of employment Rough sets Complex networks Network security Stability analysis Classification algorithms Decision trees

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Street-Level IP Geolocation Algorithm Based on Landmarks Clustering

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Computers, Materials & Continua 2021年第3期66卷 3345-3361页

作者： Fan Zhang Fenlin Liu Rui Xu Xiangyang Luo Shichang Ding Hechan Tian PLA Strategic Support Force Information Engineering University Zhengzhou450001China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Cyberspace Security Key Laboratory of Sichuan Province Chengdu610000China China Electronic Technology Cyber Security Co. Ltd.Chengdu610000China University of Goettingen Goettingen37075Germany

Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar ***,this principle is often invalid in real Internet environment,which leads to unreliable geolocation *** improve the accuracy and reliability of locating IP in real Internet,a street-level IP geolocation algorithm based on landmarks clustering is ***,we use the probes to measure the known landmarks to obtain their delay vectors,and cluster landmarks using ***,the landmarks are clustered again by their latitude and longitude,and the intersection of these two clustering results is taken to form training ***,we train multiple neural networks to get the mapping relationship between delay and location in each training ***,we determine one of the neural networks for the target by the delay similarity and relative hop counts,and then geolocate the target by this *** it brings together the delay and geographical coordinates clustering,the proposed algorithm largely improves the inconsistency between them and enhances the mapping relationship between *** evaluate the algorithm by a series of experiments in Hong Kong,Shanghai,Zhengzhou and New *** experimental results show that the proposed algorithm achieves street-level IP geolocation,and comparing with existing typical streetlevel geolocation algorithms,the proposed algorithm improves the geolocation reliability significantly.

关键词： IP geolocation neural network landmarks clustering delay similarity relative hop

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Analysis of Instantaneous Angular Speed Accuracy Considering Encoder Errors

Analysis of Instantaneous Angular Speed Accuracy Considering...

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International Conference on Automation and computing (ICAC)

作者： Yuanhao Liu Fengshou Gu Qiang Zeng State Key Laboratory of Mechanical Transmissions for Advanced Equipment Chongqing University Chongqing China School of Computing and Engineering University of Huddersfield Huddersfield UK

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

Instantaneous Angular Speed (IAS) is closely related to the dynamic behavior of rotating machinery and plays a crucial role in diagnosing faults. However, the measurement of the IAS is subject to various errors inevitably, which directly affect the accuracy of IAS estimation and diminish the precision of fault diagnosis in rotating machinery. Currently, the estimation of IAS primarily relies on time counting methods, and there has been limited research conducted on the application of Frequency Domain Demodulation (FDD) as an IAS estimation method, despite its potential for providing lower noise levels. The study of IAS errors based on the FFD method has received even less attention. This study undertakes pertinent research and investigates the issue identified. Firstly, it develops a model for IAS estimation using FDD, considering the specific characteristics of the encoder disk signal. Secondly, it establishes IAS models for eccentric and inclined encoder disks, considering the spatial geometric characteristics associated with different types of encoder disk errors. In conclusion, the experimental results successfully validate the accuracy and validity of the proposed theoretical models. This study lays a theoretical foundation for the precise measurement of IAS, thereby enhancing the reliability of using IAS for condition monitoring in rotating machinery.

关键词： Fault diagnosis Accuracy Frequency-domain analysis Measurement uncertainty Estimation Machining Reliability theory Machinery Rotation measurement Noise level

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Population-Based Meta-Heuristic Optimization Algorithm Booster: An Evolutionary and Learning Competition Scheme

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Neurocomputing 2025年 643卷

作者： Wang, Jun Dong, Junyu Zhou, Huiyu Dong, Xinghui State Key Laboratory of Physical Oceanography and Faculty of Information Science and Engineering Ocean University of China China School of Computing and Mathematical Sciences University of Leicester United Kingdom

In a Population-Based Meta-Heuristic Optimization Algorithm (PMOA), individuals in the population will constantly generate new promising individuals, to form new populations. Although the population continuously changes, the variations in each individual are traceable in most algorithms. An individual in the population comes from the individual in the previous population. The direction of the evolution of populations can be identified on top of this historical inheritance relationship, which improves the efficiency of PMOAs and solves optimization problems more effectively. Since Recurrent Neural Networks (RNNs) are able to capture the temporal dependencies in sequences, we are motivated to propose a novel but simple Evolutionary and Learning Competition Scheme (ELCS), also referred to as the PMOA Booster, in which individuals keep changing for the better fitness based on the heuristic rules of the PMOA while an RNN is used to learn the process that each individual changes in order to guide the generation of promising individuals. The ELCS automatically selects the RNN or PMOA which generates more individuals with the better fitness. We test the proposed scheme using the benchmark of IEEE Congress on Evolutionary Computation 2022 competition (CEC 2022). The results show that this scheme is able to boost the performance of both the classical and state-of-the-art PMOAs and outperforms its counterparts. Also, the ELCS produces promising results in two real-world industrial scenarios. We believe that the effectiveness of the proposed ELCS is due to the adaptive competition between the RNN and the PMOA. © 2025 Elsevier B.V.

关键词： Particle swarm optimization (PSO)

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