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检索条件"任意字段=3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020"

共 81 条记录，以下是31-40 订阅

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A Verifiable E-Lottery Scheme 3rd

A Verifiable E-Lottery Scheme

引用

3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Zhang, Yin An, Fanglin Zhao, Kejie Ye, Jun School of Computer Science and Cyberspace Security Hainan University Haikou China

ISBN: (纸本)9783030622220

With the development of Internet and computer technology, e-lottery gradually becomes the main form of lottery tickets purchased buy people. The e-lottery existing at present only realizes the function that the traditional lottery tickets can be purchased online, and the generation of winning numbers is still making use of the traditional way, did not consider the situation where the lottery issuers or lottery buyer interfered with the final lottery result. Based on the discussion of the history and development of lottery and some e-lottery schemes, this article presents a new scheme of electronic lottery which references the linear equations combined with Hash function based on the verifiable random number to generate the winning numbers. While generating lottery numbers, we will provide a set of numbers for purchasers to verify. It’s worth noting that this scheme achieves that the players can purchase lottery anonymously and participate in the generation of winning number, and that they are able to verify whether they definitely take part in the generation. And to emphasize one point, this scheme is aimed at the two-color ball lottery with a wide range of popularity. © 2020, Springer Nature Switzerland AG.

关键词： Hash functions

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Spatio-Temporal Graph Convolutional Networks for DDoS Attack Detecting 1

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3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Xie, Qian Huang, Zheng Guo, Jie Qiu, Weidong Shanghai Jiao Tong University Shanghai China Westone Cryptologic Research Center Beijing China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Distributed Denial-of-Service (DDoS) attacks disrupts the availability of essential services, which are one of the most harmful threats in today’s Internet. Many DDoS detection algorithms based on machine learning technology have emerged in recent years, for example, the LUCID algorithm, GNN model. But considering that DDoS attacks are based on both time and space, these algorithms only considered time and ignored space. Besides, by piece network traffic data is often difficult to obtain. The model we used in this paper is based on the Spatio-Temporal Graph Convolutional Network (STGCN) proposed by Yu B. et al. And on this basis, the model is improved for the unique characteristics of DDoS attacks. By considering the time-dependence of the network topology and network traffic, this model has a good recognition rate on the online DDoS data set, and can achieve detection of DDoS attacks under the premise of using only two-way traffic information. © 2020, Springer Nature Switzerland AG.

关键词： machine learning

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A Deep learning Approach to Radar Extrapolation 3rd

A Deep Learning Approach to Radar Extrapolation

引用

3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Yi, Lei Tian, Wei Wang, Xiang Xu, Xiaolong Nanjing University of Information Science and Technology Nanjing210044 China Institute for Climate and Application Research Nanjing China

ISBN: (纸本)9783030624620

Nowcasting is really important for citizen and industry since it matters to everyone on social and economic activities and is significant for monitoring and forecasting disasters. As the foundation of the task, radar extrapolation is the most difficulty to be solved. However, the accuracy of the extrapolation is in a large bias. With the rapid development of computing power, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of extrapolation, given an ample account of radar echo maps. Since ConvLSTM has successfully introduced spatiotemporal sequence into the traditional LSTM, in this study, we modified a recurrent neural network (RNN) called bidirectional ConvLSTM (Bi-ConvLSTM) based on the ConvLSTM and used a reversed encoding-forecasting structure. The experiments show that the Bi-ConvLSTM achieved a better performance than the ConvLSTM. © 2020, Springer Nature Switzerland AG.

关键词： Radar

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Optimal Group Consensus of Second-Order Multi-agent Systems 1

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3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Yang, Yize Yang, Hongyong Li, Yuling Liu, Yuanshan School of Information and Electrical Engineering Ludong University Yantai264025 China School of Electrical and Electronic Engineering University of Adelaide Adelaide Australia Key Laboratory of Cyber-Physical System and Intelligent Control in Universities of Shandong Ludong University Yantai264025 China

ISBN: (数字)9783030624637

ISBN: (纸本)9783030624620

The optimal control problems play an important role in modern control theory. This paper focuses on the optimal problem for group flocking movement of multi-agent systems (MAS). Two new cost functions are proposed with distributed optimal cooperative control. By using modern control theory and algebraic graph theory, optimal control of group consensus trajectory for dynamic MAS is studied. Moreover, a properly chosen value of the optimal scaling factor is presented, where the optimal cost functions for group consensus of MAS can be achieved by choosing the appropriate scaling factor. Numerical simulations are provided to illustrate the effectiveness of the theoretical results. © 2020, Springer Nature Switzerland AG.

关键词： Multi agent systems

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A Variant of Sun Toughness and the Existence of Path Factors in Networks 1

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3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Zhu, Linli Baskonus, Haci Mehmet Gao, Wei School of Computer Engineering Jiangsu University of Technology Changzhou213001 China Department of Mathematics and Science Education Faculty of Education Harran University Sanliurfa Turkey

ISBN: (数字)9783030624606

ISBN: (纸本)9783030624590

Among several parameters in computer networks to measure its vulnerability, toughness is the most important one which has raised widespread concern from mathematicians and computer scientists. Starting from the requirements of network security, other toughness related parameters are successively defined and applied to related engineering fields. Very recently, Zhou et al. [1] defined a new parameter called "sun toughness" and also show the essential connection with path factors. We first introduce a new variant related on sun toughness, and then we give the bounds of this variant to ensure a graph has path factor in different settings in this paper. Furthermore, we show that the sun toughness bounds are sharp in some sense. © 2020, Springer Nature Switzerland AG.

关键词： Network security

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Analysis on Industrial Internet Traffic Characteristics 3rd

Analysis on Industrial Internet Traffic Characteristics

引用

3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Chen, Shiyu Li, Qianmu Long, Huaqiu Jiang, Jian School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Intelligent Manufacturing Department Wuyi University Jiangmen529020 China Jiangsu Zhongtian Internet Technology Co. Ltd. Nantong226009 China

ISBN: (纸本)9783030622220

Digital economy is an important engine to promote high-quality growth of China’s economy. In the future, Chinese enterprises will face the inevitable requirement to transform from traditional economy to digital economy. Industrial Internet is an important support for digital economy transformation of enterprises. Analysis on network traffic characteristics is the premise to guarantee the service quality and improve the performance of industrial Internet. These services include refined operation and maintenance management, traffic control, network protocol design and network performance analysis. Moreover, analysis on network traffic characteristics is also helpful for industrial Internet platform to build a more effective network model, configure network node resources efficiently and maintain industrial Internet security. Data that conform to norms of feature analysis are acquired by preprocessing datasets of industrial Internet traffic provided by ZTE. Then, industrial Internet traffic characteristics, such as self-similarity, periodicity and burstiness, are analyzed and verified. The research results of this paper are helpful to predict industrial Internet traffic and analyze its performance. © 2020, Springer Nature Switzerland AG.

关键词： Network security

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Q-learning-Based Adaptive Bacterial Foraging Optimization 3rd

Q-Learning-Based Adaptive Bacterial Foraging Optimization

引用

3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Niu, Ben Xue, Bowen College of Management Shenzhen University Shenzhen518060 China Great Bay Area International Institute for Innovation Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783030624590

As a common biological heuristic algorithm, Bacterial Foraging Optimization (BFO) is often used to solve optimization problems. Aiming at increasing the solution accuracy and convergence performance while enhancing the capability of individual’s self-learning and exploration, a Q-learning-Based Adaptive Bacterial Foraging Optimization (QABFO) is proposed in this paper. The basic chemotaxis, reproduction and elimination/dispersal operations in standard BFO are redesigned in a Q-learning mechanism, and the Q-table will be updated on the basis of the changed fitness values in each iteration. In chemotaxis operation, modified search direction and secondary cruising mechanism are introduced into tumbling and swimming behaviors, with the purpose of improving the search efficiency and balancing local and global search. Additionally, a generation skipping adaptive reproduction is designed to control the accuracy and convergence of QABFO. Experimental results demonstrate that compared with BFO, PSO and GA, the proposed algorithm performs better in terms of accuracy and stability on most of the test functions and can effectively improve the premature convergence problem due to the original reproduction operation in BFO. © 2020, Springer Nature Switzerland AG.

关键词： Heuristic algorithms

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Attribute Propagation Enhanced Community Detection Model for Bitcoin De-anonymizing 3rd

Attribute Propagation Enhanced Community Detection Model for...

引用

3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Wang, Jiming Xie, Xueshuo Fang, Yaozheng Lu, Ye Li, Tao Wang, Guiling College of Computer Science Nankai University Tianjin300350 China College of Cyber Science Nankai University Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9783030622220

Bitcoin is a kind of decentralized cryptocurrency on a peer-to-peer network. Anonymity makes Bitcoin widely used in online payment but it is a disadvantage for regulatory purposes. We aim to de-anonymize Bitcoin to assist regulation. Many previous studies have used heuristic clustering or machine learning to analyze historical transactions and identify user behaviors. However, the accuracy of user identification is not ideal. Heuristic clustering only uses the topological structure of the transaction graph and ignores many transaction information, and supervised machine learning methods are limited by the size of labeled datasets. To identify user behaviors, we propose a community detection model based on attribute propagation, combining the topological structure of the transaction graph and additional transaction information. We first parse the transaction data of public ledger and construct a bipartite graph to describe correlations between addresses and transactions. We also extract address attributes from historical transactions to construct an attributed graph with the previous bipartite graph. Then, we design an adaptive weighted attribute propagation algorithm named AWAP running on the attributed graph to classify bitcoin addresses, and further identify user behaviors. Extensive experiments highlight that the proposed detection model based on AWAP achieves 5% higher accuracy on average compared to state-of-the-art address classification methods in Bitcoin. AWAP also achieves 25% higher F-score on average compared to previous community detection algorithms on two datasets. © 2020, Springer Nature Switzerland AG.

关键词： Bitcoin

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An Imbalanced learning Based Method for Esophageal Squamous Cell Carcinoma (ESCC) Distant Metastasis Predicting 3rd

An Imbalanced Learning Based Method for Esophageal Squamous ...

引用

3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Li, Xuemei Yang, Bo Peng, Lizhi Li, Baosheng Zhu, Jian Educational Technology Center Shandong College of Traditional Chinese Medicine Yantai264199 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Department of Radiation Oncology Shandong Cancer Hospital affiliated to Shandong University Jinan250117 China

ISBN: (纸本)9783030624620

Accurately predicting distant metastasis is very important for esophageal squamous cell carcinoma (ESCC) patients, as it is a key evidence to determine the degree of this disease. Traditional metastasis predictions are made by doctors manually, which is time and labor consuming. In recent years, some researchers try to predict ESCC distant metastasis using machine learning techniques. However, they ignored the factor that the problem of predicting ESCC distant metastasis is a highly imbalanced task. In this study, we try to build a new imbalanced learning based ESCC distant metastasis predicting scheme using blood cell analysis testing results. We first extract effective features from blood cell analysis testing data. And then we use over-sampling method to generate new positive instances (instances with ESCC distant metastasis). Finally, we apply k-nearest neighbor (k-NN) algorithms to predict unknown instances. Experimental results show that our method is effective in cases with few positive instances. © 2020, Springer Nature Switzerland AG.

关键词： machine learning

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Squeeze Criterion GANs: Double Adversarial learning Method 3rd

Squeeze Criterion GANs: Double Adversarial Learning Method

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3rd international conference on machine learning for cyber security, ml4cs 2020

作者： Gan, Yan Xiang, Tao Ye, Mao College of Computer Science Chongqing University Chongqing400044 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9783030624590

Generative adversarial networks (GANs) have attracted much attention since it is able to effective learn from an unknown real distribution. However, the instability of the training process greatly affects the quality of the generated images. To address this problem, the network structure-based, loss-based variant model and some training techniques are proposed. Unfortunately, there are some problems with the above methods, such as the limited effect of stabilizing the training process, the complex mathematical derivation, and the lack of universality of training techniques for different tasks. To this end, we propose a novel squeeze criterion GANs. In this method, we design a pseudo real module to synthesize adversarial sample and the double identity discriminator is designed. Then, the generated image and adversarial sample, as well as the generated image and real image form double adversarial learning. Through double adversarial learning, it forms a squeeze criterion to stabilize the training process of generator and discriminator. Finally, experimental results show that the proposed method has well portability and stabilizes the training process of existing GANs, and improves the quality of generated images. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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