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MINDFLOW: A NETWORK TRAFFIC ANOMALY DETECTION MODEL BASED ON MINDSPORE

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arXiv 2025年

作者： Xiang, Qiuyan Wu, Shuang Wu, Dongze Liu, Yuxin Qin, Zhenkai School of Information Technology Guangxi Police College 530028 China College of Computer Science and AI Southwest Minzu University 610213 China Institute of Software Chinese Academy of Sciences 100190 China School of Computing and Information China Network Security Research Center China Big Data and Policing Technology Laboratory Guangxi Police College Guangxi Nanning China

With the wide application of IoT and industrial IoT technologies, the network structure is becoming more and more complex, and the traffic scale is growing rapidly, which makes the traditional security protection mechanism face serious challenges in dealing with high-frequency, diversified, and stealthy cyber-attacks. To address this problem, this study proposes MindFlow, a multi-dimensional dynamic traffic prediction and anomaly detection system combining convolutional neural network (CNN) and bi-directional long and short-term memory network (BiLSTM) architectures based on the MindSpore framework, and conducts systematic experiments on the NF-BoT-IoT dataset. The experimental results show that the proposed model achieves 99% in key metrics such as accuracy, precision, recall and F1 score, effectively verifying its accuracy and robustness in network intrusion detection. © 2025, CC0.

关键词： Convolutional neural networks

A Prediction-Correction Based Iterative Convolution-Thresholding Method for Topology Optimization of Heat Transfer Problems

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SSRN

SSRN 2024年

关键词： Convolution

Addressing the traveling salesperson problem with frequency fitness assignment and hybrid algorithms

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Soft computing 2024年第17-18期28卷 9495-9508页

作者： Liang, Tianyu Wu, Zhize Lässig, Jörg Berg, Daan van den Thomson, Sarah L. Weise, Thomas Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Jinxiu Dadao 99 Anhui Hefei230601 China Fakultät Elektrotechnik und Informatik Hochschule Zittau/Görlitz Brückenstraße 1 Saxony Görlitz02826 Germany Abteilung Kognitive Energiesysteme Fraunhofer IOSB-AST Wilhelmsplatz 11 Saxony Görlitz02826 Germany Department of Computer Science Vrije Universiteit Amsterdam De Boelelaan 1111 Amsterdam1081 HV Netherlands School of Computing Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Jiulong Road 111 Anhui Hefei230601 China

The traveling salesperson problem (TSP) is one of the most iconic hard optimization tasks. With frequency fitness assignment (FFA), a new approach to optimization has recently been proposed: instead of directing the search towards better solutions, the optimization process prefers those with rarely encountered objective values. FFA can be plugged into existing algorithms and can improve their performance on some problems that are hard for them. However, problems like the TSP, where the number of possible objective values (here: tour lengths) is high, cause the performance of FFA-based algorithms to deteriorate. We choose 56 symmetric instances from TSPLib as a testbed for our experiments. We plug FFA both into the (1+1) EA and simulated annealing (SA) and obtain the FEA and FSA, respectively. We find that the resulting FEA substantially outperforms the (1+1) EA and that FSA can reach the global optima more often than SA within our computational budget. We propose two ways to hybridize FFA and traditional optimization in a round-robin scheme where objective function evaluations are alternatingly assigned to both components. The FFA-based parts of the algorithms sometimes send a solution over to the traditional-optimization-based ones. This can lead to further performance improvements. In particular, the hybrids combining simulated annealing with the FEA solve most problems and find close-to-optimal solutions on other instances. Even for several instances with many possible different objective values, which are therefore not suited to FFA, hybrids combining FFA and traditional objective-guided optimization algorithms can offer substantial performance improvements. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Simulated annealing

A Crawler Detection Method for The College Entrance Examination Information Website

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A Crawler Detection Method for The College Entrance Examinat...

Information and Communication Technology (ICTech), International Conference of

作者： YaoYao Chen Yue Fan Xin Li YongHong Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Criminal Investigation Police University of China Shenyang China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Jinan China Institute of Future Artificial Intelligence Technology and Innovative Applications Beijing China

The college entrance examination information website provides users with a variety of college entrance examination data that can be used for reference. Due to the public nature of the website and the large scale of the data, it is vulnerable to attacks by web crawlers, which can threaten the stability of the website and the security of the data, so it is especially important to effectively control the crawlers on the college entrance examination information website. In this paper, we propose a crawler detection method for the college entrance examination information website. Firstly, we set multi-level access frequency thresholds to screen out users who may have crawling behavior, and then we detect and identify suspicious users' access according to the characteristics of college entrance examination information website crawlers by SVM, and finally identify the real web crawlers and prohibit them from accessing the college entrance examination information website. Through experiments, it is proved that this method can effectively detect a large part of crawlers and ensure the data security of the college entrance examination information website.

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Comprehensive Review to Build a Smart Guide for the Government E-services in Oman

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Comprehensive Review to Build a Smart Guide for the Governme...

IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Sana Fathima Amal Al Abri Fatma Al Shamli Sara Shukaili Moza Al Balushi Mastan Vali Shaik Nooritawati Md Tahir College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman College of Engineering Universiti Teknologi MARA (UiTM) Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Malaysia Applied College Princess Nourah Bint Abdul Rahman University (PNU) Riyadh Saudi Arabia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

In the modern age, the idea of the E-government has attained a lot of attention due to its impactful benefits and advantages in achieving the significant goals of the country. The E-government systems play a vital role in facilitating the government’s duties and responsibilities along with the citizen’s interaction and participation. Despite the availabilities of the various studies on the E government, there is still a need to review the previous related literature and combine the issues emerging out of it such that an appropriate system is built incorporating the right blend of AI, social media, and public dissemination of the services. This type of guidance is required in many countries including Oman. The aim of this review is to provide the recent studies that have been done in similar areas focusing on providing the approach and the framework deployed to help build a model for Oman’s e-government systems endorsing social media in its delivery.

关键词： Deep learning Reviews Social networking (online) Buildings Focusing Control systems Rendering (computer graphics) Market research Artificial intelligence Electronic government

Dancing between Threats and Conflicts: How Chinese Energy Companies Invest Amidst Global Geopolitical Risks

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SSRN

SSRN 2023年

作者： Huang, Zhuo Ye, Chengfang Lai, Fujun Li, Yunzhong National School of Development Peking University Changsha Institute of Computing and Digital Economy Peking University Beijing100091 China School of Public Administration Xiangtan University Xiangtan411105 China School of Finance Yunnan University of Finance and Economics Kunming650221 China National Engineering Laboratory for Big Data Analysis and Application Peking University Changsha Institute of Computing and Digital Economy Peking University Beijing100091 China

In an increasingly interconnected world, global geopolitical risk (GPR) poses significant challenges to the investment decisions of energy companies. However, few studies have examined the effects of such risk, particularly global geopolitical acts (GPRA) and global geopolitical threat (GPRT), on Chinese energy companies. Utilizing data from publicly listed energy companies in China, this study fills this gap by investigating the effects of GPRA and GPRT on these companies’ investment decisions. The results indicate that GPRT significantly inhibits the investment activities of Chinese energy companies, whereas GPRA promotes them. Capital hedging demand is identified as the primary channel through which GPRA and GPRT affect these companies. Additionally, we determine that factors such as companies’ business credit capability, valuation, operational risk, and degree of internationalization, along with macroeconomic characteristics such as China’s economic opportunities and the business confidence index, moderate the relationships between GPRT, GPRA, and companies’ investment decisions. The findings of this study enhance our understanding of the impact of GPR on the investment decisions of Chinese energy companies and provide valuable insights for both policymakers and industry practitioners navigating the complex global energy landscape. © 2023, The Authors. All rights reserved.

关键词： Investments

Hyperbolic Geometric Latent Diffusion Model for Graph Generation

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arXiv 2024年

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

computing optimal partition problems via Lagrange multiplier approach

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arXiv 2024年

作者： Cheng, Qing Guo, Jing Wang, Dong Department of Mathematics Tongji University Shanghai200092 China Key Laboratory of Intelligent Computing and Applications Tongji University Ministry of Education China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong518172 China

In this paper, we consider numerical approximations for the optimal partition problem using Lagrange multipliers. By rewriting it into constrained gradient flows, three and four steps numerical schemes based on the Lagrange multiplier approach [16, 17] are proposed to solve the constrained gradient system. Numerical schemes proposed for the constrained gradient flows satisfy the nice properties of orthogonality-preserving, norm-preserving, positivity-preserving and energy dissipating. The proposed schemes are very efficient in which only linear Poisson equations are solved at each time step. Extensive numerical results in 2D and 3D for optimal partition problem are presented to validate the effectiveness and accuracy of the proposed numerical schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers