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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Dong, Jie Fang, Wenyu Zheng, Wanling Liu, Jinkun Liu, Yanhua College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China

ISBN: (纸本)9798350356328

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm. © 2024 IEEE.

关键词： Differential privacy

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Deep Iterative Reconstruction network Based on Residual Constraint for Low-Dose CT Imaging 8

Deep Iterative Reconstruction Network Based on Residual Cons...

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8th International Conference on Systems and Informatics, ICSAI 2022

作者： Liu, Jin Kang, Yanqin Liu, Tao Zhang, TingYu Zhang, Yikun Anhui Polytechnic University School of Computer and Information Wuhu China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781665474481

clinical low X-ray dose computed tomography (LDCT) scanner often induce high intensity strip artifact and spot nosie, compromising diagnoses and intervention plans. Recently, sparsely constrained and network learning-based frameworks have been shown to be efficient in mitigating such issue. In this work, we propose a deep iterative reconstruction network (DIRNet) model with a residual constraint to synergize the advantages of feature learning and image reconstruction to address the LDCT imaging problem. DIR-Net compose by few iteration units, and all iteration units include three different network modules: projection restoration, residual constraint and image update block. DIR-Net is a promising approach for building an end-to-reconstruction mapping strategy and directly obtaining high-quality CT images. Furthermore, LISTA is used to conFigure the network, and the whole network architecture yields improved interpretability. Qualitative and quantitative analysis in test data shown the promising imaging effects of DIR-Net in quantum noise reduction, block artifact removal and tissue detail texture mantian. © 2022 IEEE.

关键词： Image reconstruction

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Yijun Hao Shusen Yang Fang Li Yifan Zhang Cong Zhao Xuebin Ren Peng Zhao Chenren Xu Shibo Wang National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Computer Science Peking University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China National Engineering Laboratory for Big Data Analytics XiÃan Jiaotong University XiÃan China

network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports $3.12\times $ more demand when compared to related existing approaches.

关键词： Topology network topology Planning IP networks Optical fiber networks Routing Optical fibers Optical feedback Production Graph neural networks

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Timo: In-memory temporal query processing for big temporal data

Timo: In-memory temporal query processing for big temporal d...

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作者： Zheng, Xiao Liu, Houkai Wang, Xiujun Wu, Xuangou Yu, Feng School of Computer Science and Technology Anhui University of Technology Maanshan China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Maanshan China School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Today's internet applications generate massive temporal data anywhere and anytime. Although some disk-based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in-memory temporal query and analytic model for big temporal data. Firstly, a space-efficient temporal index is proposed to support more efficiently query performance with less memory space than the state-of-art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark-based temporal systems in terms of both query latency and throughput. © 2021 John Wiley & Sons, Ltd.

关键词： Data handling

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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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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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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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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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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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