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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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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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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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Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet

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Nuclear Science and Techniques 2022年第3期33卷 62-76页

作者： Kai Chen Li-Bo Zhang Jia-Shun Liu Yuan Gao Zhan Wu Hai-Chen Zhu Chang-Ping Du Xiao-Li Mai Chun-Feng Yang Yang Chen Southeast University Nanjing 210096China School of Cyber Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing 210096China Department of Radiology General Hospital of the Northern Theater of the Chinese People’s Liberation ArmyShenyang 110016China Laboratory of Image Science and Technology The School of Computer Science and EngineeringSoutheast UniversityNanjing 210096China Department of Radiology Nanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjing 210008China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of RadiologyZhongda HospitalSoutheast UniversityNanjing 210009China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and EngineeringSoutheast UniversityNanjing 210096China

Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke *** widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion *** the dose of X-rays causes severe noise and artifacts in PCT *** solve this problem,we propose a deep learning method called *** exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into *** decomposes the convolved features into high-and low-frequency *** decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image *** Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent *** extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact *** and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.

关键词： Cerebral perfusion CT Low-dose Image denoising Perfusion parameters

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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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Activity Recognition Method Based on Kernel Supervised Laplacian Eigenmaps

Activity Recognition Method Based on Kernel Supervised Lapla...

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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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Cross Elitist Learning Multifactorial Evolutionary Algorithm 1

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3rd International Conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Li, Wei Luo, Haonan Wang, Lei School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China

ISBN: (数字)9789811961427

ISBN: (纸本)9789811961410

Multifactorial Evolutionary Algorithm (MFEA) is a popular optimization algorithm in recent years. It has implicit parallelism and can solve different problems at the same time in the same search space. However, premature convergence is a significant shortcoming of MFEA. The main reason for this phenomenon is the lack of population diversity and the inability to migrate effectively between tasks. To address this shortcoming, this paper proposes a cross elitist learning multifactorial evolutionary algorithm (CEL-MFEA). Firstly, the cross elitist strategy guides the individuals to learn from the elite individual, which can improve the convergence of the proposed CEL-MFEA. Secondly, the Nelder-Mead algorithm is used to provide an effective knowledge transfer between different tasks. Finally, the opposition learning mechanism is employed to ensure population diversity. The comprehensive ability of the proposed CEL-MFEA is evaluated by 9 classical multifactorial optimization problem test sets. Experimental results show that the CEL-MFEA is more competitive than MFEA algorithm and several popular multifactorial optimization algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

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Laryngeal Leukoplakia Classification Via Dense Multiscale Feature Extraction in White Light Endoscopy Images 48

Laryngeal Leukoplakia Classification Via Dense Multiscale Fe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： You, Zhenzhen Yan, Yan Shi, Zhenghao Zhao, Minghua Yan, Jing Liu, Haiqin Hei, Xinhong Ren, Xiaoyong Xi'An University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an China Xi'An Jiaotong University Second Affiliated Hospital of Medical College Department of Otorhinolaryngology Xi'an China

ISBN: (纸本)9781728163277

Laryngeal leukoplakia classification is challenging using white light endoscopy images. Relevant research focus on normal tissues versus non normal tissues, cancer versus non cancer classification. The objective of this paper is to classify laryngeal leukoplakia in white light endoscopy images into six classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia and squamous cell carcinoma. We proposed a dense multiscale convolutional neural network including parallel multiscale convolution, dense convolution and recurrent convolution in favor of extracting dense multiscale features of laryngeal leukoplakia for fine classification. The proposed network achieved an overall accuracy of 0.8958 for the six-class classification. It has high sensitivity and specificity for each class which are, respectively, 1.0000 and 0.9394 for normal tissues, 0.6667 and 1.0000 for inflammatory keratosis, 0.8889 and 0.9744 for mild dysplasia and moderate dysplasia, 0.7500 and 1.0000 for severe dysplasia, 1.0000 and 0.9767 for squamous cell carcinoma. The experimental results show that our proposed model is superior to the state-of-the-art deep learning-based models. © 2023 IEEE.

关键词： Endoscopy

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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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