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Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security intelligent agent reinforcement learning Offensive and defensive game

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9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Xu, Changlin Wu, Wenyuan School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Ningxia Yinchuan750021 China

ISBN: (纸本)9781643684703

The similarity of triangular fuzzy numbers could be served as a measurement of similarity between two triangular cloud models. However, it has been found that existing methods for describing the similarity of triangular cloud models has the drawbacks that can't make full use of utilizing graphic information and assigning inappropriate weights to distances through research and analysis. The method proposed in this literature has adjusted the weight of distance similarity aim to ensure that the characteristic information of the model will not be covered. In addition, the proposed method provides a measurement to describe geometric shape bases on the expected curve included angle of the triangular cloud model. On the foundation of taking abundantly the similarity of distance and shape into account, we provide the measurement to achieve better accuracy. The feasibility and effectiveness of the proposed method have been demonstrated through the time series of KNN algorithm. © 2023 The authors and IOS Press.

关键词： Cloud computing

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Three-Way Decision-Making Based on Incomplete Information System and Its Application 9

Three-Way Decision-Making Based on Incomplete Information Sy...

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9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Xu, Changlin Mao, Songqian School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Ningxia Yinchuan750021 China

ISBN: (纸本)9781643684703

In order to avoid decision makers from making wrong decisions as much as possible and reduce the losses caused by wrong decisions, a new aggregation method of interval loss function is proposed based on incomplete information system. Firstly, the missing values are filled according to their characteristics and combined with probability, and on this basis, the measurement method of similarity is given, and the horizontal similarity class of each object is obtained according to the similarity between objects. Secondly, according to the number of times each object in the similar class appears in its corresponding horizontal similar class, the corresponding weight is obtained;Then the interval loss function of similar class is defined as the aggregation of interval loss functions of all objects in similar class, and each interval is weighted according to the weight, so that multiple intervals in similar class are aggregated into an interval, and then the aggregated interval is converted into a single value by using the conversion function. Finally, according to the aggregation method, three decision rules are given to make decisions, and the effectiveness of the method is verified by a case. © 2023 The authors and IOS Press.

关键词： Decision making

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TF-DTA: A Deep Learning Approach Using Transformer Encoder to Predict Drug-Target Binding Affinity

TF-DTA: A Deep Learning Approach Using Transformer Encoder t...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Wenjun Zhou, Yiqiang Tang, Xiwei Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Hunan Changsha China Hunan First Normal University School of Computer Science Hunan Changsha China

ISBN: (纸本)9798350337488

Bioinformatics is a rapidly growing field that involves the application of computational methods to analyze and interpret biological data. One important task in bioinformatics is predicting the drug-target affinity (DTA), which plays a significant role in drug discovery through virtual screening. Effectively predicting the association between drug molecules and target molecules can speed up the drug discovery process. The DTA can be quantitatively measured. This quantifiable affinity is more precise than a simple binary relationship. In this study, we propose a deep learning model for DTA prediction that utilizes the encoder module of the Transformer architecture. Our proposed model utilizes Convolutional Neural Networks (CNNs) and the encoder module of Transformer to characterize protein and drug sequences. The model outperforms some methods such as KronRLS, SimBoost and DeepDTA as evidenced by superior evaluation metrics such as Mean Squared Error (MSE), Concordance Index (CI), and Regression toward the Mean Index (rm2). These results demonstrate the effectiveness of the Transformer's encoder in extracting meaningful representations from sequences, thereby improving the accuracy of DTA prediction. Deep learning models for DTA prediction can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, the use of machine learning technology enables more effective and efficient drug discovery. © 2023 IEEE.

关键词： deep learning drug-target affinity multi-head attention mechanism

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9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Xu, Changlin Wang, Kuan School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Ningxia Yinchuan750021 China

ISBN: (纸本)9781643684703

Uncertainty exists widely in nature and human society. The handling and analysis of these uncertain phenomena have long been a focal point of research in natural sciences, representing a hot and crucial topic. The cloud model uses three numerical characteristics to realize qualitative and quantitative transformation of uncertain knowledge. Normal cloud is one of the more important and common cloud models. The similarity measurement of cloud model is used to judge the similarity of two cloud concepts, and the purpose is to understand the correlation between different cloud concepts, so as to improve the efficiency of classification and clustering. As a deformation extension of f divergence, Hellinger distance has good properties and is suitable for describing the difference between two probability distributions. HECMk and HCCMk algorithms are proposed by combining Hellinger distance and expectation curve cluster with entropy. Compared with previous algorithms, these two algorithms have higher distinguishing ability. Among them, HCCMk algorithm has better comprehensive performance and is feasible in actual similarity measurement. © 2023 The authors and IOS Press.

关键词： Entropy

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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An improved branching algorithm for the proper interval edge deletion problem

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Frontiers of Computer Science 2022年第2期16卷 178-180页

作者： Wenjun LI Xiaojing TANG Yongjie YANG School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on TransportationChangsha University of Science and TechnologyChangsha 410015China School of Computer Science and Engineering Central South UniversityChangsha 410083China

1 Introduction For a graph class G,the G-EDGE DELETION problem is to determine whether a given graph can be transformed into a graph in G by deleting at most k *** G-EDGE DELETION problem for a large body of graph cla... 详细信息

关键词： branching problem transformed

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Research on Highly Secure Metaverse Based on Extended Reality Under Edge Computing 16th

Research on Highly Secure Metaverse Based on Extended Realit...

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16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Fu, Jinrong Liu, Yiwen Yan, Haobo Yang, Yahui Peng, Ling Shi, Yuanquan Feng, Tao School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China School of Foreign Languages Huaihua University Huaihua418000 China

ISBN: (纸本)9783031580529

With the explosion of ChatGPT, the development of artificial intelligence technology ushered in another explosion. Similarly, with the rapid development of extended reality technology and Internet of Things technology, Metaverse will also usher in greater breakthroughs. However, in the development of the extended reality metaverse under edge computing, many security issues will arise. This paper focuses on data security, considering that data will be transmitted and processed between multiple devices and nodes instead of being concentrated in the cloud, which may bring To solve data security issues, relying on the integrated architecture of Metaverse educational applications based on edge computing (MEC), it provides an identity verification and access with higher security and scalability, better performance, and service requirements that meet the current environment Control mechanism solutions, while analyzing other problems that will arise during the development of the extended reality metaverse. Aiming at the security problem on the edge side, a signature authentication scheme is designed based on Elliptic Curve Cryptography (ECC) integrated blockchain encryption technology and the effectiveness of the method is proved. In order to promote the extended reality metaverse under edge computing, it provides a mirror for the application in the field of education under the condition of ensuring data security. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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GNEA: A Novel GCN-based Network Embedding Algorithm for Semantic Social Network 10

GNEA: A Novel GCN-based Network Embedding Algorithm for Sema...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Zhao, Jinbo He, Jinling Du, Yuanhan Tang, Ming Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

With the development of semantic social networks, social networks become more complex and their size expands rapidly, which brings significant challenges to social network analysis. Network Embedding can transform the network from a high-dimensional adjacency matrix to a low-dimensional one, which has excellent foreground in link prediction, vertex classification, and graph visualization. Nowadays, most algorithms are proposed for static networks with little consideration of the dynamic changes in the network. However, most real-world networks change over time. Directly applying static network embedding algorithms to dynamic networks will result in poor stability, flexibility, and efficiency. Because of these shortcomings, we propose a graph convolution-based dynamic network representation learning algorithm (GNEA), which combines Graph Convolutional Network (GCN) with Long Short-Term Memory (LSTM) to train the time series networks. GCN is applied to extract features in the network, and LSTM is utilized to capture temporal information. GNEA can not only perform characterization learning on the network but also obtain more abundant information during the dynamic evolution of the network. GNEA is compared with several typical algorithms on the datasets Stochastic Block Model (SBM), Autonomous Systems (AS), and Digital Bibliography and Library Project (DBLP). The results prove that GNEA has good performance in MAP and precision@k. © 2024 IEEE.

关键词： Complex networks

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Meta-path and Meta-schema Hierarchical Aggregation for Heterogeneous Graph Neural Networks

Meta-path and Meta-schema Hierarchical Aggregation for Heter...

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Smart World Congress (SWC), IEEE

作者： Ling Wu Pingping Gao Kun Guo Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Sciencen (Fuzhou University) China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

With deep learning’s advancement in recent years, heterogeneous graph neural networks (HGNNs) have drawn a lot of interest. They are a group of deep learning models specifically designed for handling heterogeneous graph data, in which nodes and edges are of various types and all nodes are assumed to have either direct or indirect associations. A metaschema, which serves as a thorough and unified blueprint for the heterogeneous graph, elegantly connects varied node types to other heterogeneous nodes via multiple heterogeneous edges with rich sematincs. However, many existing methods that utilize meta-schema to mine heterogeneous information often focus on the different meta-relationships inside the schema, limiting the meta-schema to describing only the local relational semantics. To better utilize meta-schema for efficiently integrating different heterogeneous information, we organize them into a hierarchical structure(i.e., hierarchical meta-schema), with all types of nodes distributed across different layers. The layers are interconnected through heterogeneous relationships. The target type is placed at the final layer and others are arranged hierarchically in the front according to the heterogeneous relationships defined in the meta-schema. During information aggregation, according to the hierarchical meta-schema, the information of each layer is gradually aggregated to the target type on the final layer from far to near via the attention mechanism. Moreover, metapaths regularly begin and end with the same type and help to define complex structures and semantic. Therefore, we also consider the semantic information of neighbors based on metapaths within the proposed hierarchical meta-schema to enhance the complex semantics among nodes of the same-type that may be lacking in the meta-schema. Finally, we proposed a meta-path and meta-schema hierarchical aggregation for heterogeneous graph neural network model, named HGNN-MMHA. And we may allow the meta-schema to not

关键词： Deep learning Correlation Limiting Attention mechanisms Semantics Graph neural networks Heterogeneous networks data models

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