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Formal Modeling and Defense Methods for 5G network Endpoint Access Denial of Service Attacks

Formal Modeling and Defense Methods for 5G Network Endpoint ...

引用

2023 International Conference on networking and network Applications, NaNA 2023

作者： Zhang, Tong Wei, Wei Wang, Yichuan Deng, Xi Zhu, Lei Ji, Wenjiang School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technolog Xi'an China School of Computer Science Xi'an Shiyou University Xi'an China

ISBN: (纸本)9798350327380

5G terminal access denial-of-service attack is one of the more common types of 5G signaling network attacks. It manifests itself as a large number of abnormal access requests occupying core network resources. Normal requests cannot obtain resources, resulting in unstable network services, and users will be unable to connect or drop connections, eventually leading to frequent terminal takeoffs and landings. This paper proposes a formal modeling scheme for a 5G terminal access denial-of-service attack. The attack principle is analyzed, a defense scheme is designed and modeled, and analyzed, and then quantitative experiments are conducted, and the experimental results show the effectiveness of the defense scheme. © 2023 IEEE.

关键词： network security

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Activity Recognition Method Based on Kernel Supervised Laplacian Eigenmaps

Activity Recognition Method Based on Kernel Supervised Lapla...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Pengjia Tu Cheng Tian Dandan Du Junhuai Li Huaijun Wang School of Computer Science and Engineering Xi’an University of Technology China Shaanxi Key Laboratory for Network Computing and Security Technology China

Laplacian dimensionality reduction can effectively achieve feature transformation and preserve the important structure of high-dimensional features. However, the trained model with this method usually require better generalization ability to new samples. Hence, a human activity recognition method based on kernel-supervised laplacian eigenmaps (KSLE) by combining the kernel method, laplacian mapping, and supervised learning is proposed. Firstly, the adjacency distance relationship of original samples features in the high-dimensional kernel space is obtained. Secondly, the category labels in the high-dimensional feature set are incorporated in the manifold learning for dimensionality reduction. Then, kernel trick is utilized to directly solve the low-dimensional embedding structure of the test feature set. Finally, the obtained low-dimensional feature set is input into the classifier for recognition. Extensive experiments conduct on two public datasets confirm the effectiveness of the proposed approach for improving the generalization ability of new samples.

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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...

引用

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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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 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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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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A Novel Multipath Data Transmission Method with Traffic Obfuscation

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

引用

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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Self-Evaluation of Large Language Model based on Glass-box Features

Self-Evaluation of Large Language Model based on Glass-box F...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Huang, Hui Qu, Yingqi Liu, Jing Yang, Muyun Xu, Bing Zhao, Tiejun Lu, Wenpeng Faculty of Computing Harbin Institute of Technology Harbin China Baidu Inc. Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

ISBN: (纸本)9798891761681

The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing on training and prompting strategies. However, a crucial aspect - model-aware glass-box features - is overlooked. In this study, we explore the utility of glass-box features under the scenario of self-evaluation, namely applying an LLM to evaluate its own output. We investigate various glass-box feature groups and discovered that the softmax distribution serves as a reliable quality indicator for self-evaluation. Experimental results on public benchmarks validate the feasibility of self-evaluation of LLMs using glass-box features. © 2024 Association for Computational Linguistics.

关键词： Open systems

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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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