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International Conference on networking and network Applications (NaNA)

作者： Wenjiang Ji Jiangcheng Yang Yichuan Wang Lei Zhu Yuan Qiu Xinhong Hei College of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

Driving safety is an essential prerequisite to the rapid development of autonomous trams. However, the relationship of driving risk factors is nonlinear, which makes modeling difficult. To improve the accuracy of driving risk prediction, a data driven approach based on Generative Adversarial networks was proposed. First of all, a communication and alarming scenario of Vehicular Ad-hoc networks was demonstrated, in which the original data sets can be collected and transmitted by the help of sensors and Road Side Units. Then the RFE feature selection algorithm was used to keep the key features. To deal the sample asymmetry problem, a DCGAN model was designed for sparse samples expansion. At last, the XGBoost algorithm was used to classification and output the risk prediction result. During the experiment implemented with the public and real data sets, the risk prediction accuracy of proposed approach can up to 97.24%, for which takes the advantages in generating of the sparse samples.

关键词： Road side unit Transportation Prediction methods Prediction algorithms Generative adversarial networks Classification algorithms Sensors

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ConvCatb: An Attention-based CNN-CATBOOST Risk Prediction Model for Driving Safety

ConvCatb: An Attention-based CNN-CATBOOST Risk Prediction Mo...

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International Conference on networking and network Applications (NaNA)

作者： Xinhong Hei Hao Zhang Wenjiang Ji Yichuan Wang Lei Zhu Yuan Qiu College of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

Risk prediction is one of the most important tasks in assistant and automatic driving. In recent years, by the help of VANET and various sensors in the cars, the status of cars and roads can be collected in real time and used for data-driven based driving risk prediction. However, it is challenging to predict the driving risk due to the complex relationship between multiple environment factors like location, weather, time etc. Thus, a deep learning model ConvCatb was proposed in this paper, which improves the attention mechanism to the traditional Convolutional Neural networks, and combines the CatBoost algorithm to predict the current driving safety. The main idea is to emphasize the combination relationship between driving environment features through Non-local attention mechanism, and then use CatBoost to replace the softmax of CNN for classification. Finally, the experiment results show that the ConvCatb achieved superiorities in accuracy and F1-score, compared with existing schemes.

关键词： Machine learning algorithms Roads Vehicular ad hoc networks Prediction algorithms Real-time systems Classification algorithms Safety

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A fine-grained petri model for SQL time-blind injection

A fine-grained petri model for SQL time-blind injection

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International Conference on networking and network Applications (NaNA)

作者： Guiling Zhang Yaling Zhang Yichuan Wang Lei Zhu Wenjiang Ji School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

With the background of severe SQL injection attacks, the existing SQL injection modeling methods cannot reflect the process of SQL injection attacks in a fine-grained manner. Based on the discussion of attack technology, this paper takes SQL time-blind injection as an example to model its process with Petri Net. The validity of the model is verified by quantitative analysis and qualitative analysis. Try to inject 10, 20, 30, 40 and 50 times into target aircraft and Petri Net model respectively. The blind injection time is recorded and compared. The results show that the injection time increases with the increase of injection times. Under the same injection times, the Petri Net model takes less time. The sending time in the token can be set. When the sending time is short, the injection speed is fast, and super real-time simulation can be realized, which can realize the rapid prediction of attacks and resource vulnerability effects. When the sending time is long, the injection process slows down. It is beneficial to observe the details of the injection process and whether conflicts occur at a fine-grained level, analyze the purpose of the attack and achieve the purpose of building a patch model. The patch model can effectively take countermeasures against attacks, predict unknown vulnerabilities and ensure network information security.

关键词： Analytical models Statistical analysis Firing Atmospheric modeling Petri nets Predictive models SQL injection

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MGRR-SQL: Multi-Granularity Relevant Retrieval-Based in-Context Learning Approach for Text-to-SQL Generation

MGRR-SQL: Multi-Granularity Relevant Retrieval-Based in-Cont...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Yiming Zhan Zhao Li Tong Chen Shiwei Wu Yang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Evay Info Technology Co. Ltd. Jinan China School of Cyberspace Security (School of Cryptology) Hainan University Haikou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In-Context learning serves as a vital component for enhancing Text-to-SQL task performance. However, current in-context learning methodologies for Text-to-SQL applications fail to sufficiently bridge the significant cross-domain correlation gap that exists between few-shot examples and the intended query target. Our approach overcomes these challenges through a novel relevant retrieval-based in-context learning method, which operates across three distinct granularity levels: (1) semantic similarity retrieval for analogous questions, (2) query kernel entity matching for retrieving strongly relevant questions and (3) character-level similarity analysis for fine-grained question retrieval. Additionally, we introduce a novel Object-Relational Mapping (ORM)-based database prompt and a post cell value linking method that effectively describes the schema while avoiding error propagation common in traditional schema linking approaches. Our experimental evaluations conducted on the benchmark datasets BIRD and Spider reveal that our approach delivers strong performance, comparable to state-of-the-art methods.

关键词： Learning systems Structured Query Language Databases Large language models Computational modeling Semantics Benchmark testing Birds Business intelligence Standards

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Stackelberg Game for Cognitive Ambient Backscatter network with Perfect and Imperfect Spectrum Sensing

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2025年

作者： Ning, Bing Dong, Yulei Li, Jianjun Ye, Yinghui Hao, Wanming Zhongyuan University of Technology School of Information and Communication Engineering Zhengzhou451191 China Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi’an710121 China Xidian University School of Telecommunication Engineering Xi’an710071 China Zhengzhou University School of Information Engineering Zhengzhou450001 China

The combination of cognitive radio and ambient backscatter communication can effectively improve spectrum utilization and reduce power consumption. However, when multiple backscatter devices coexist in this network, the competition among secondary backscatter users (SBUs) and conflict of interest in licensed bands due to spectrum sharing are issues that cannot be ignored. In this paper, we study the Stackelberg game to achieve a win-win transmission between primary users (PUs) and SBUs in the sensing-based cognitive ambient backscatter network. The sensing-based dual bandwidth pricing mechanisms under perfect spectrum sensing and imperfect spectrum sensing are proposed, respectively. For the perfect spectrum sensing scenario, resource allocation optimization algorithms based on the Stackelberg game are formulated to maximize the bandwidth revenue of primary users and the throughput utility function of SBUs. For the imperfect spectrum sensing scenario, hybrid transmission modes and the interference to legacy links based on different detection results are considered. Furthermore, the non-convex optimization problem based on the Stackelberg game is solved in closed-form expressions by corresponding theoretical derivation. Simulation results show that the proposed pricing mechanisms can effectively improve the revenue of primary users and the throughput performance of SBUs. © 2017 IEEE.

关键词： Convex optimization

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Learning Fine-Grained Information Alignment for Calibrated Cross-Modal Retrieval

Learning Fine-Grained Information Alignment for Calibrated C...

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

作者： Jianhua Dong Shengrong Zhao Hu Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China

Masked Language Modeling (MLM) and Image-Text Matching (ITM) are always used in fusion encoder to learn the joint representation of images and text. In existing methods, the masking strategy of MLM leads to the neglect of image details during the modeling process. Meanwhile, the sampling strategy of ITM struggles to consistently select high-difficulty hard negative instances, reducing the effectiveness of constraints. This leads to challenges in aligning fine-grained information in cross-modal retrieval. In response to this challenge, a fine-grained information alignment-based visual language model (FAM) is proposed in this paper. On one hand, the attribute-based masking strategy is employed in MLM, helping the model focus on the details of objects in images during modeling. On the other hand, the robust hard negative sample generation strategy provides challenging negative samples for ITM by altering the relationships between objects. This enables the model to align relationships between objects in different modalities and thus calibrates cross-modal retrieval. Extensive experiments demonstrate the effectiveness of the model in cross-modal retrieval tasks.

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HG-ETC: Fine-Grained Application Behaviors Classification From Encrypted network Traffic

HG-ETC: Fine-Grained Application Behaviors Classification Fr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Weihua Liu Xiaohui Han Wenbo Zuo Yu Sun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quancheng Provincial Laboratory Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The classification of encrypted traffic has garnered widespread attention from both researchers and industrial companies. However, existing application behavior-based encrypted traffic classification methods often extract limited information from flow features, typically focusing on either statistical or temporal characteristics of application behavior flows. They fail to capture higher-order features among flows, such as communication behavior characteristics. Therefore, this paper proposes a flow higher-order feature extraction model based on a self-attention hypergraph network. Specifically, we design a six-layer Temporal Convolutional network (TCN) to simultaneously learn the temporal feature representations of flows, and then fuse them together to obtain a more robust feature representation. Experimental results on two real-world datasets demonstrate that HG-ETC outperforms various state-of-the-art methods in fine-grained encrypted traffic classification tasks.

关键词： Fuses Federated learning Focusing Telecommunication traffic Forestry Feature extraction Cryptography

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A Knowledge Distillation-Driven Lightweight CNN Model for Detecting Malicious Encrypted network Traffic

A Knowledge Distillation-Driven Lightweight CNN Model for De...

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International Joint Conference on Neural networks (IJCNN)

作者： Yuecheng Wen Xiaohui Han Wenbo Zuo Weihua Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Quancheng Provincial Laboratory Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the realm of cybersecurity, efficiently and precisely identifying and mitigating potential threats from malicious encrypted traffic is crucial. As deep learning evolves, methods relying on Convolutional Neural networks (CNNs) to convert traffic payloads into visual representations for analysis have become popular. Although this technique effectively discerns malicious encrypted traffic, it faces inherent limitations, such as difficulties in deployment due to extensive network scales and high computational demands, especially in edge computing environments. To address these challenges, we propose a model for maliciously encrypted traffic detection, named LightMETD. The model consists of two stages. In the first stage, we utilize Random Forest (RF) with a feature selection algorithm to quickly identify easily distinguishable traffic patterns. In the second stage, the filtered traffic data is converted into grayscale images and classified using an innovative lightweight MobileNetV3-S architecture. Our LightMETD model addresses the problem of large volume and high computational requirements of CNN models. To enhance the effectiveness of the LightMETD, we propose an innovative knowledge distillation method to train LightMETD. This method significantly improves the accuracy of malicious encrypted traffic detection by designing a distillation loss function that enables the model to better capture the intricate relationships between samples. We use a USTC-TFC2016 public traffic dataset and a locally collected dataset to demonstrate the effectiveness of LightMETD. LightMETD achieves an impressive 97% average classification accuracy. LightMETD significantly outperforms other baselines in terms of volume and detection speed, underscoring its viability and superiority in addressing real-world challenges posed by malicious encrypted traffic.

关键词： Training Solid modeling Visualization Accuracy Computational modeling Telecommunication traffic Traffic control

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Augmentation-Aware Self-Supervision for Data-Efficient GAN Training

arXiv

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arXiv 2022年

作者： Hou, Liang Cao, Qi Yuan, Yige Zhao, Songtao Ma, Chongyang Pan, Siyuan Wan, Pengfei Wang, Zhongyuan Shen, Huawei Cheng, Xueqi CAS Key Laboratory of AI Safety and Security Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Kuaishou Technology China

Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting. Previously proposed differentiable augmentation demonstrates improved data efficiency of training GANs. However, the augmentation implicitly introduces undesired invariance to augmentation for the discriminator since it ignores the change of semantics in the label space caused by data transformation, which may limit the representation learning ability of the discriminator and ultimately affect the generative modeling performance of the generator. To mitigate the negative impact of invariance while inheriting the benefits of data augmentation, we propose a novel augmentation-aware self-supervised discriminator that predicts the augmentation parameter of the augmented data. Particularly, the prediction targets of real data and generated data are required to be distinguished since they are different during training. We further encourage the generator to adversarially learn from the self-supervised discriminator by generating augmentation-predictable real and not fake data. This formulation connects the learning objective of the generator and the arithmetic − harmonic mean divergence under certain assumptions. We compare our method with state-of-the-art (SOTA) methods using the class-conditional BigGAN and unconditional StyleGAN2 architectures on data-limited CIFAR-10, CIFAR-100, FFHQ, LSUN-Cat, and five low-shot datasets. Experimental results demonstrate significant improvements of our method over SOTA methods in training data-efficient GANs. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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AMHF-TP: Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期 44-58页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing School of Electronic Engineering Xi'an University of Posts and Telecommunications

Multifunctional therapeutic peptides(MFTP) hold immense potential in diverse therapeutic contexts, yet their prediction and identification remain challenging due to the limitations of traditional methodologies, such as extensive training durations, limited sample sizes, and inadequate generalization capabilities. To address these issues, we present AMHF-TP, an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance performance. The AMHF-TP is composed of four key components: a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences; a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures; a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences; and a hierarchical feature extraction module that integrates multimodal peptide sequence features. Compared with leading methods,the proposed AMHF-TP demonstrates superior precision, accuracy, and coverage, underscoring its effectiveness and robustness in MFTP recognition. The comparative analysis of separate hierarchical models and the combined model, as well as with five contemporary models, reveals AMHF-TP's exceptional performance and stability in recognition tasks.

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