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Crucial Interaction Elements Detection for 5G Core network based on the Complex network Model

Crucial Interaction Elements Detection for 5G Core Network b...

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International Conference on networking and network Applications (NaNA)

作者： Ruinan Peng Xinhong Hei Yichuan Wang Xiaoxue Liu Yeqiu Xiao School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

This paper focuses on the issue of crucial interaction elements detection in the 5G core network. Given the current absence of an effective method for quantification evaluating the importance of interaction elements, we define the crucial interaction elements as crucial nodes in this paper, and also undertake a comprehensive exploration of various data interaction scenarios within the 5G core network. This aims to overcome the limitations of traditional network analysis methods, which often operate independently in multi-layer data processing. By extracting multilayer data interaction elements across diverse scenarios, these elements are characterized within a complex network framework. Subsequently, a quantification scoring system for crucial nodes is constructed, incorporating multiple centrality indicators and node weights. Notably, the fitness function within the genetic algorithm is improved to facilitate a dynamic weight optimization strategy, responding to real-time changes in network topology. The optimized weight combination is then allocated to various evaluation indicators within the system, culminating in the design of a quantification scoring model for crucial node. Based on this model, a suite of 5G core network crucial point detection methods, tailored to different data scenarios is proposed. This method enable the quantification evaluation of each node’s importance, realizes crucial interaction elements detection. Such an approach serves to bolster the stable operation of the 5G core network, thereby more effectively meeting modern society’s security imperatives concerning information transmission.

关键词： 5G mobile communication Transfer learning Complex networks Reinforcement learning Nonhomogeneous media Data models Robustness

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A Gradient-Guided Method for Hyperspectral Band Selection Based on Spatial-Spectral Weighting

A Gradient-Guided Method for Hyperspectral Band Selection Ba...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Jing Hu Ke Lit Yunfei Ju Minghua Zhao Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Hyperspectral image(HSI) band selection is a crucial task in image processing. It is necessary to screen out bands with rich information and low correlation, to achieve data dimensionality reduction and retain key information, thereby improving image processing efficiency. Aiming at the differences in reflectance characteristics of targets in different regions in hyperspectral images(HSls) and the strong correlation between bands, this paper proposes a gradient- guided spatial-spectral weighted hyperspectral band selection method. Firstly, gradient information is introduced to enhance the edge texture features of different targets. Then, the entropy rate superpixel segmentation algorithm is used to segment the first principal component of the image and divide multiple uniform regions. Secondly, to more comprehensively capture the global relationship between all bands in the image, a multi-graph complementary strategy is proposed to construct a regional similarity graph, aiming to maximize the mutual contribution between superpixels with spatial-spectral similarity. Finally, a unified clustering graph is generated by spectral clustering. After normalized cutting, the band with the least noise information in each sub-cube is selected to form a new band subset. The effectiveness of the proposed method is verified by classification results and data analysis.

关键词： Reflectivity Dimensionality reduction Image segmentation Correlation Data analysis Image edge detection Noise Smart systems Entropy Hyperspectral imaging

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Hyperspectral Band Selection Based on Classification Reward Adjustment

Hyperspectral Band Selection Based on Classification Reward ...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Jing Hu Jiawei Liang Yunfei Ju Minghua Zhao Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Hyperspectral image band selection is a crucial link in image processing. It is necessary to screen out bands with rich information and low correlation, to achieve data dimensionality reduction and retain key information, thereby improving image processing efficiency. Aiming at the differences in reflectance characteristics of targets in different regions in hyperspectral images and the strong correlation between bands, this paper proposes a gradient-guided spatial-spectral weighted hyperspectral band selection method. Firstly, gradient information is introduced to enhance the edge texture features of different targets. Then, the entropy rate superpixel segmentation algorithm is used to segment the first principal component of the image and divide multiple uniform regions. Secondly, to more comprehensively capture the global relationship between all bands in the image, a multi-graph complementary strategy is proposed to construct a regional similarity graph, aiming to maximize the mutual contribution between superpixels with spatial-spectral similarity. Finally, a unified clustering graph is generated by spectral clustering. After normalized cutting, the band with the least noise information in each sub-cube is selected to form a new band subset. The effectiveness of the proposed method is verified by classification experimental results and data analysis.

关键词： Training Reflectivity Image segmentation Correlation Noise Memory modules Predictive models Smart systems Spatiotemporal phenomena Hyperspectral imaging

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AdvTG: An Adversarial Traffic Generation Framework to Deceive DL-Based Malicious Traffic Detection Models 25

AdvTG: An Adversarial Traffic Generation Framework to Deceiv...

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34th ACM Web Conference, WWW 2025

作者： Sun, Peishuai Yun, Xiaochun Li, Shuhao Yin, Tao Si, Chengxiang Xie, Jiang Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (纸本)9798400712746

Deep learning-based (DL-based) malicious traffic detection models are effective but vulnerable to adversarial attacks. Existing adversarial attacks have shown promising results when targeting traffic detection models based on statistics and sequence features. However, these attacks are less effective against models that rely on payload analysis. The main reason is the difficulty in generating semantic, compliant, and functional payloads, which limits their practical application. In this paper, we propose AdvTG, an adversarial traffic generation framework to deceive DL-based malicious traffic based on the large language model (LLM) and reinforcement learning (RL). Specifically, AdvTG is designed to attack various DL-based detection models across diverse payload features and architectures, thereby enhancing the generalization capabilities of the generated adversarial traffic. Moreover, we design a specialized prompt for traffic generation tasks, where functional fields and target types are supplied as input, while non-functional fields are generated to produce the mutated traffic. This fine-tuning endows the LLM with task comprehension and traffic pattern reasoning abilities, allowing it to generate traffic that remains compliant and functional. Furthermore, leveraging RL, AdvTG automatically selects traffic fields that exhibit more robust adversarial properties. Experimental results show that AdvTG achieves over 40% attack success rate (ASR) across six detection models on four base datasets and two extended datasets, significantly outperforming other adversarial attack methods. © 2025 Copyright held by the owner/author(s).

关键词： Reinforcement learning

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A Novel Auralization Model for network security Situation Assessment

A Novel Auralization Model for Network Security Situation As...

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International Conference on networking and network Applications (NaNA)

作者： Yeqiu Xiao Yibin Ma Yichuan Wang Kun Jiang Lanlan Cui School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

Situation assessment is one of indispensable part for protecting network security. However, with the development of network attacks, the timeliness of attack threat assessment using traditional mathematical methods has gradually become difficult to apply to large-scale and high-speed network attacks. In order to solve this problem, this paper explores the relationship between the influencing factors of network threats and music elements, studies a new method of situation assessment that combines network situation with MIDI music, and builds a network situation auralization model. First, for the network situation assessment model, this paper starts from the perspective of network attacks, designs and extracts attack attributes, and constructs a sliding window-based attack threat dynamic assessment strategy to evaluate risk levels and reflect the current network security status. Secondly, for the MIDI music mapping model, extract key attributes in music, design an emotion classification model based on MIDI music and build a MIDI music library, and finally map it to the network situation. Experimental results show that the auralization model in this paper is more efficient in situation assessment and presentation.

关键词： Deep learning Accuracy High-speed networks network security Threat assessment Libraries

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A Novel Multipath Data Transmission Method with Traffic Obfuscation

A Novel Multipath Data Transmission Method with Traffic Obfu...

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International Conference on networking and network Applications (NaNA)

作者： Xinhong Hei Jianfeng Chen Yichuan Wang Xin Song Lei Zhu School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

With the rapid progress of network communication technology, the issues of user identity and privacy are critical. However, there are many disadvantages in traditional single-path data transmission, for example, the link is susceptible to monitoring and the traffic feature can be easily analyzed. To solve this issue, this paper introduces a multipath data transmission strategy with traffic obfuscation. First, we design a multipath traffic transmission scheme that divides traffic into multiple sub streams and transmits them through multiple paths. The ability of the communication system to resist surveillance and traffic analysis is enhanced by this approach. Second, we effectively confused multipath traffic by changing the size of the data packets, controlling the delay of multipath transmission, and adding redundant traffic. Through experiments, it has been shown that in this advanced multipath transmission strategy, there is no significant difference in the traffic features generated by each node. The effectiveness of the proposed method in obfuscating communication traffic is demonstrated by this.

关键词： Privacy Surveillance Resists Traffic control Delays Data communication Streams

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Compiler Differentiation Detection Method for Binary Similarity

Compiler Differentiation Detection Method for Binary Similar...

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International Conference on networking and network Applications (NaNA)

作者： Xiaoxue Liu Xinwei Duan Xinhong Hei Yichuan Wang Xin Song School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

Compiler differential detection can be performed by detecting the similarity between the binary compiled by the compiler and the ideal compiled file. This article proposes a javac compiler differentiation detection system based on binary file similarity: we extract opcode sequences from binary files, use BERT to extract features of the opcodes, and finally obtain the similarity score by calculating the distance between vectors. Under the premise of ensuring the retrieval speed requirements, this algorithm is superior to most existing models in accuracy, with the accuracy rate of $99.7 \%$. It shows that the method in this article can effectively detect whether two binary files are similar. This method will have a good effect on detecting whether the javac compiler has been invaded by malicious code in the future.

关键词： Accuracy Limiting Source coding Semantics Neural networks Feature extraction Malware

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Research on Code Virtualization Methods for Cloud Applications

Research on Code Virtualization Methods for Cloud Applicatio...

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International Conference on networking and network Applications (NaNA)

作者： Zhoukai Wang Zuoyan Xu Yaling Zhang Xin Song Yichuan Wang School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

With the vigorous development of technologies such as the Internet, mobile devices, and the Internet of Things, cloud services have gained a crucial position in our life and have become an essential part of daily operations for modern enterprises and organizations. However, existing cloud application security solutions have failed to address the problem of runtime binary code security, focusing only on vulnerabilities inherent to the applications themselves. Code virtualization, also known as virtualization obfuscation, is a technique that protects software from malicious analysis by obfuscating the code. It hides the code’s control flow and data flow, thereby preventing the code from being decompiled. In this paper, we introduce a code virtualization method, VMENP, which splits the code into multiple modules and uses polymorphic and modular encryption mechanisms to secure binary code at runtime. Specifically, VMENP divides the protected program into several modules, designs a virtual instruction set along with its corresponding interpreter, encrypts each instruction individually after converting it into intermediate representations, and then encrypts it again at the basic block level. After compiling the segmented code into polymorphic bytecode sequences, it embeds the virtual code’s interpreter into the IR. During runtime, the protected code is dynamically decrypted and executed by the basic block level, with each basic block’s storage and execution in memory in a short time window. VMENP identifies functions annotated in the source code to locate the functions that need protection. This paper uses a large amount of $\mathrm{C} / \mathrm{C}++$ code to perform tests in a real cloud environment. Experimental results show that VMENP achieves a good balance between the granularity of protection and the performance overhead of the protected programs, while successfully protecting $\mathrm{C} / \mathrm{C}++$ code and programs running on X86/64 and ARM32/64 architectures

关键词： Cloud computing Codes Runtime Instruction sets Reverse engineering computer architecture Binary codes

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Detecting Fake-Normal Pornographic and Gambling Websites through one Multi-Attention HGNN 26

Detecting Fake-Normal Pornographic and Gambling Websites thr...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ma, Xiaoqing Zheng, Chao Li, Zhao Yin, Jiangyi Liu, Qingyun Chen, Xunxun Institute of Information Engineering Chinese Academy of Sciences Beijing China National Engineering Laboratory for Information Security Technologies Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Geedge Networks Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (纸本)9798350331684

The rapid development of pornographic and gambling websites, fueled by the widespread abuse of information technology, has become a growing concern. They pose a serious threat to the physical and mental health of children and can also endanger personal property. Therefore, it is necessary to detect them. However, pornographic and gambling websites become more and more tricky, which shows fake-normal to evade censorship and challenges traditional content-based detection methods. Therefore, it is essential to rely on information about relationships between *** propose HMAN, one Multi-Attention Heterogeneous Graph Neural network (HGNN) model to detect pornographic and gambling websites by integrating content features and structural information, even if they present fake-normal. By one multi-attention mechanism consisting of explicit weight, self-attention and attention mechanism, content features can be selectively utilized with the assistance of structural information. The experimental results show that our method achieves the best 95.1% Macro-Avg-F1 and outperforms all baselines. We also illustrate that all extracted metapaths do contribute to the detection, where the hyperlink, title/meta terms and IP address are relatively important. © 2023 IEEE.

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