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检索条件"机构=State Key Laboratory of Mathmatical Engineering and Advanced Computing"

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Network Topology Change Detection Based on Statistical Process Control 20

Network Topology Change Detection Based on Statistical Proce...

引用

4th High Performance computing and Cluster Technologies Conference, HPCCT 2020 and the 3rd International Conference on Big Data and Artificial Intelligence, BDAI 2020

作者： Liu, Yan Liu, Lian Yan, Yu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Investigation Technology Center PLCMC Beijing China First Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (纸本)9781450375603

Network topology is one of the most important parts in network security situation awareness tasks. Considering that the topology may change due to network intrusion, adjustment of routing policies, etc., the traditional static topology analysis methods cannot capture the dynamic change of network topology with time, which leads to the problem of weak early warning ability. To find out the small trend changes of the network topology over time, a network topology change detection method is proposed based on statistical process control: 1) to simplify the continuous dynamics of network topology, the observation network is regarded as a sampling sequence of the topological networks that dynamically change with time, a longitudinal topological network is constructed;2) to quantify the differences of network structure from multiple perspectives, network structure parameters of each period are selected and measured;3) to find out the trendy structural change in the longitudinal topological network, the cumulative sum method in industrial control is introduced to evaluate the change of topological parameters and further track to the starting time of change. Extensive experiments are performed on the simulation data, showing that compared with simple parameter statistics method, the method proposed can be sensitive to network changes and trace back to the beginning of the trend change. © 2020 ACM.

关键词： Network topology

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Cluster-Based Routing Algorithm for WSN Based on Subtractive Clustering

Cluster-Based Routing Algorithm for WSN Based on Subtractive...

引用

International Wireless Communications and Mobile computing Conference, IWCMC

作者： Ling Chen Wenwen Liu Daofu Gong Yan Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

The Cluster-based Routing Algorithm for Wireless Sensor Network based on Subtractive Clustering (SCC algorithm) generates nodes of cluster head in dense area of the node by relying on subtractive clustering. The application of this algorithm effectively solves the attribution problem of non-cluster head node in conventional algorithms, and consumption is evenly distributed throughout the network. Through simulation experiments, it can be found that the application of the SCC algorithm can map the distribution of nodes of cluster head reasonably and delay the death time of the first node to prolong the lifetime of network and balance the energy consumption of nodes.

关键词： Clustering algorithms Wireless sensor networks Routing Routing protocols Base stations Electronic mail Energy consumption

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

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

引用

International Conference on Big Data and Information Analytics (BigDIA)

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

ISBN: (数字)9781665422321

ISBN: (纸本)9781665446662

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.

关键词： Jacobian matrices Numerical simulation Stability analysis Heterogeneous networks Numerical models Numerical stability Lyapunov methods

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A Software Upgrade Security Analysis Method on Network Traffic Classification using Deep Learning

A Software Upgrade Security Analysis Method on Network Traff...

引用

Urban engineering and Management Science (ICUEMS), International Conference on

作者： Bing Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728188324

ISBN: (纸本)9781728188331

Nowadays, the vulnerability in the software upgrade process are extremely harmful to network security. However, the detection of upgrade vulnerability is facing serious difficulties and problems. Aiming at the current software upgrade vulnerability analysis problem, we propose a neural network model for traffic classification based on two levels of data packets and network flows. This method does not need to manually extract features and can learn the dependency relationship between data packets and networks well. It can make full use of comprehensive flow features for traffic classification. In order to evaluate the model, we built a dataset of software upgrade vulnerabilities and conducted relevant experiments on the CICIDS 2017 dataset. The experimental results show that compared with other RNN models, our model has the best performance, reaching a precision of 99.86% and an F1 score of 99.44%. At the same time, based on the pre-trained model, we use the software upgrade vulnerability dataset to fine-tuning the model, with the F1 score reaching 99.78%.

关键词： Conferences Software Measurement

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Recommendation of cyber attack method based on knowledge graph

Recommendation of cyber attack method based on knowledge gra...

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Computer engineering and Intelligent Control (ICCEIC) International Conference on

作者： Yunjia Ou Tianyang Zhou Junhu Zhu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728185736

ISBN: (纸本)9781728185743

The lack of correlation between the new cyber attack intelligence and the knowledge database makes it difficult for the recommendation system to recommend effective cyber attack knowledge in terms of the new security intelligence. This paper proposes an cyber attack method intelligence recommendation algorithm based on knowledge graph. Firstly, it proposes a cyber attack knowledge graph construction scheme based on four kinds of open security databases. Then, based on the idea of collaborative filtering recommendation, the meta-path is introduced to describe different relations between nodes. At last, the recommendation list could be generated by calculating the correlation score of each path with node vector. Compared with the content-based recommendation method, the effectiveness of this method is proved.

关键词： Knowledge engineering Correlation Databases Knowledge based systems Security Cyberattack Intelligent control

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Recurrent Neural Network Based Binary Code Vulnerability Detection 19

Recurrent Neural Network Based Binary Code Vulnerability Det...

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Proceedings of the 2019 2nd International Conference on Algorithms, computing and Artificial Intelligence

作者： Jianyun Zheng Jianmin Pang Xiaochuan Zhang Xin Zhou MingLiang Li Jun Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781450372619

Vulnerability detection, especially vulnerability automatic detection, have long been an area of active research. Considering that RNN can model dependencies automatically, we propose a recurrent neural network (RNN) based method for binary code vulnerability detection. We collected correspond to 4 types of vulnerabilities, including CWE-134, CWE-191, CWE-401 and *** find which RNN is better in vulnerability detection, we evaluated 6 different RNNs, including SRNN, BSRNN, LSTM, BLSTM, GRU and BGRU. The results demonstrated that BSRNN is better than others in vulnerability detection.

关键词： Deep Learning Vulnerability detection Recurrent Neural Network

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Dynamic Array Double-Access Attack in Ethereum

Dynamic Array Double-Access Attack in Ethereum

引用

International Conference on Computer and Communications (ICCC)

作者： Xiangyang Chang Junhu Zhu Shibin Zhao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

We propose a new type of variable coverage method called Dynamic Array Double-Access Attack (DADA Attack) in Ethereum Virtual Machine (EVM). Such attack can read and write operations anywhere in the storage area, resulting in abnormal execution of contract and out of control. Vulnerable contracts with such attacks will directly lead to the loss of a large amount of property. To evaluate the effectiveness of such Double-Access Attack, we conducted theoretical analysis and experimental verification, finding attack success rate is 100% under the condition that dynamic array length is controllable.

关键词： Conferences Manuals Writing Virtual machining Contracts

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Prediction Method of Attack Behavior Based on Stackelberg Security Game 5

Prediction Method of Attack Behavior Based on Stackelberg Se...

引用

5th IEEE International Conference on Computer and Communications, ICCC 2019

作者： Zhou, Tianyang Ge, Xiaoyue Zang, Yichao Wang, Qingxian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728147437

The essence of Network attack and Defense confrontation is the Game process between attacker and Defender. In the research of the existing network attack strategy, there is a priori hypothesis defect of equal status between the subjects of the attack and defense game. Threat intelligence cannot be shared between defenders. Game theory is introduced into the process of constructing network defense strategy generation model, and prediction method of attack behavior based on Stackelberg Security Game is proposed. By establishing the network model, the Stackelberg security game strong equilibrium strategy algorithm is used to generate the optimal attack strategy, which not only fully considers the influence of the relationship between the two sides in the attack and defense behavior and shares Information, but also ensures the accuracy of the attack strategy generation. The experimental results show that the proposed model and method are feasible and effective, and can improv the attack effect. © 2019 IEEE.

关键词： Game theory

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A Survey on Neural Machine Translation Applied to Sign Language Generation

A Survey on Neural Machine Translation Applied to Sign Langu...

引用

International Conference on Applied Machine Learning (ICAML)

作者： Yue Zhang Lihong Cao State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665421263

Sign languages are the primary medium of communication for the deaf and hearing-impaired community, and the number of human beings with hearing impairment is increasing at a rapid rate. There is a significant communication barrier between the hearing-impaired and normal-hearing communities. One solution to this issue is to offload the effort of text understanding for the deaf and hearing-impaired community in the form of Sign Language Generation (SLG). In this paper, first of all, we systematically review the mainstream Neural Machine Translation (NMT)-based SLG approaches. Specifically, according to their model architectures, we divide them into two categories: the Recurrent Neural Networks (RNNs)-based and Transformer-based models, and discuss their advantages and limitations. Next, we introduce the existing publicly available datasets and the data preprossessing methods in the field of SLG. Then, we summarize the performance evaluation methods for NMT-based SLG models. Finally, we offer two possible future research directions for better development in the field of SLG.

关键词： Performance evaluation Recurrent neural networks Pipelines Data preprocessing Gesture recognition Machine learning Auditory system

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Unknown Traffic Identification Based on Deep Adaptation Networks

Unknown Traffic Identification Based on Deep Adaptation Netw...

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IEEE Conference on ALocal Computer Networks Workshops (LCN Workshops)

作者： Ziji Yang Wei Lin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728183145

ISBN: (纸本)9781728183152

Network traffic classification has become an important basis for computer networks. However, the emergence of new applications, which generate unknown traffic constantly, has brought new challenges. The most critical challenge is how to divide the mixed unknown traffic into clusters containing only one category. In this paper, we propose a transfer learning approach using Deep Adaptation Network (DAN). This approach utilizes a few labeled samples from known traffic to improve the clustering purity of unknown traffic. We first trained a Convolutional Neural Network (CNN) model on unlabeled dataset with sampled time-series features. Then the model was extended to an adaptation model, co-trained on labeled and unlabeled samples. We evaluated our model using two publicly available datasets, achieving a purity of 98.23%. Our results demonstrate the effectiveness of DAN model in unknown traffic clustering. Moreover, we studied three sampling techniques and five clustering algorithms in our model for better clustering performance.

关键词： Adaptation models Protocols Transfer learning Telecommunication traffic Labeling Convolutional neural networks Knowledge transfer

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