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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Tao, Jing Cao, Ken liu, Teng Shenzhen Research Institute Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China Xi' An Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China

ISBN: (纸本)9798350304602

Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic matrices based on historical network traffic data. It is widely applied in network planning, resource management, and network security. In this paper, we propose a CAGNN (Cross-aggregate GNN) model for predicting the traffic size of OD (Origin-Destination) pairs, and develop a TM prediction framework based on this *** traditional GNN models, our CAGNN model leverages the cross-aggregation of information from the network node graph and edge graph to obtain node representations and edge representations in the network. This cross-aggregation approach allows us to integrate different forms of traffic information in the network, providing a foundation for training downstream prediction models. We validate our framework on real traffic data from the Abilene network and compare our prediction performance with that of the traditional LSTM-RNN framework. The experimental results demonstrate that our model achieves superior TM prediction performance. © 2023 IEEE.

关键词： Aggregates

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Balanced Hashgraph Based on Dynamic MCMC: Efficient, Practical and Compatible

Balanced Hashgraph Based on Dynamic MCMC: Efficient, Practic...

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作者： Tao, Jing Peng, Jianghua liu, Tian Li, Yangfan Tian, Ruifeng Wu, Chengpeng Shenzhen Research Institute Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China Xi' An Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China

ISBN: (纸本)9798350304602

In a blockchain system based on the HashGraph consensus algorithm, picking a target server for block propagation is a key step. The traditional HashGraph blockchain system uses a completely random approach to pick the target server, which has the advantage of ensuring the security of the system and reducing the impact of attacks and single server failures received by the system. However, the random selection of target servers may lead to a situation where low performance servers are under high load but high performance servers are under load. In order to make each server process the block data with maximum performance, this paper proposes a balanced up-linking strategy, which calculates the server's reputation by evaluating the status of four dimensions, such as remaining performance, security, response time, and trustworthiness of the server, and then constructs a geometric probability model by Markov model and Monte Carlo method, so that the block is randomly propagated to each server with a specific probability according to the server status. In the process of running the blockchain system with this strategy, the probability of propagation to each server is dynamically adjusted according to the changing server status, so that the frequency distribution of all servers in the cluster receiving blocks obeys the target probability distribution. We evaluate the performance of our approach through extensive simulations and experiments, and the results demonstrate the feasibility and effectiveness of the proposed approach. © 2023 IEEE.

关键词： Monte Carlo methods

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Lip-Audio Modality Fusion for Deep Forgery Video Detection

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Computers, Materials & Continua 2025年第2期82卷 3499-3515页

作者： Yong liu Zhiyu Wang Shouling Ji Daofu Gong Lanxin Cheng Ruosi Cheng College of Cyberspace Security Information Engineering UniversityZhengzhou450001China Research Institute of Intelligent Networks Zhejiang LabHangzhou311121China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001China Key Laboratory of Cyberspace Security Ministry of EducationZhengzhou450001China

In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.

关键词： Deep forgery video detection lip-audio modality fusion mel frequency cepstrum coefficient siamese neural network band-pass filter

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EvolGCN: A Co-evolutionary Graph Convolutional Network Model for Dynamically Spatio-Temporal Anomaly Event Inference

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： liu, Xiaoming Pu, Hang Chen, Zhuo Zhang, Zhanwei Lan, Yu Shen, Chao Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Shaanxi Xi'an710049 China

Accurately spatio-temporal anomaly event inference is significant to enhance society's safety, such as crime prevention and traffic collision reduction, etc. However, it is hard to achieve good performance for its complicated process being influenced by various kinds of factors. Previous works mainly focus on employing feature-based regression or fitting models with supposed spatio-temporal distribution to tackle this challenge, but are normally short of the following considerations: 1) mutual evolutionary influence, i.e. The dynamic evolution of interactions and dependencies among anomaly events, is changing along with the timeline and alters the probabilities or patterns of their occurrences dynamically;2) messy features, i.e. complex attributes in the data but with noise, are difficult to select and aggregate for the representation learning algorithm with uncertainty and redundancy. To address the research gap, we put forward a co-evolutionary graph convolutional network model to explicate the dynamically spatio-temporal anomaly patterns. Specifically, we firstly take advantage of a fuzzy-rough set-based algorithm to select features by discovering the specialty and permanence attributes of different interaction features. Then, we propose a co-evolutionary learning method to embed the dynamically temporal influence into latent features with selected interaction information. Finally, we design a graph convolutional network with an attention mechanism to formulate the mutually spatial effects among the anomaly events. The proposed model is verified on the New York City crime records from the real-world, and extensive experiments show that our approach achieves 0.10129, 0.09958 and 0.10034 of the MAE (hour) in the action, location, and action-location time inference tasks, and 0.7973 and 0.4678 of the accuracy in the action and location type inference tasks, which outperform state-of-arts by 24.00%, 50.60%, 11.10%, 10.56% and 21.53% at most, respectively. Hyper-param

关键词： Convolutional neural networks

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PyAnalyzer: An Effective and Practical Approach for Dependency Extraction from Python Code 24

PyAnalyzer: An Effective and Practical Approach for Dependen...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Jin, Wuxia Xu, Shuo Chen, Dawei He, Jiajun Zhong, Dinghong Fan, Ming Chen, Hongxu Zhang, Huijia liu, ting Xi'an Jiaotong University Xi'an China The School of Software Engineering Xi'an Jiaotong University China Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University China Huawei Technologies Co. Ltd Shenzhen China

ISBN: (纸本)9798400702174

Dependency extraction based on static analysis lays the ground-work for a wide range of applications. However, dynamic language features in Python make code behaviors obscure and nondeter-ministic;consequently, it poses huge challenges for static analyses to resolve symbol-level dependencies. Although prosperous techniques and tools are adequately available, they still lack sufficient capabilities to handle object changes, first-class citizens, varying call sites, and library dependencies. To address the fundamental difficulty for dynamic languages, this work proposes an effective and practical method namely PyAnalyzer for dependency extraction. PyAnalyzer uniformly models functions, classes, and modules into first-class heap objects, propagating the dynamic changes of these objects and class inheritance. This manner better simulates dynamic features like duck typing, object changes, and first-class citizens, resulting in high recall results without compromising pre-cision. Moreover, PyAnalyzer leverages optional type annotations as a shortcut to express varying call sites and resolve library depen-dencies on demand. We collected two micro-benchmarks (278 small programs), two macro-benchmarks (59 real-world applications), and 191 real-world projects (10MSLOC) for comprehensive comparisons with 7 advanced techniques (i.e., Understand, Sourcetrail, Depends, ENRE19, PySonar2, PyCG, and Type4Py). The results demonstrated that PyAnalyzer achieves a high recall and hence improves the F1 by 24.7% on average, at least 1.4x faster without an obvious compromise of memory efficiency. Our work will benefit diverse client applications. © 2024 ACM.

关键词： Python

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Robust Approximate Dynamic Programming for Large-scale Unit Commitment with Energy Storages

arXiv

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arXiv 2023年

作者： Lan, Yu Zhai, Qiaozhu liu, Xiaoming Guan, Xiaohong The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University China

The multistage robust unit commitment (UC) is of paramount importance for achieving reliable operations considering the uncertainty of renewable realizations. The typical affine decision rule method and the robust feasible region method may achieve uneconomic dispatches as the dispatch decisions just rely on the current-stage information. Through approximating the future cost-to-go functions, the dual dynamic programming based methods have been shown adaptive to the multistage robust optimization problems, while suffering from high computational complexity. Thus, we propose the robust approximate dynamic programming (RADP) method to promote the computational speed and the economic performance for large-scale robust UC problems. RADP initializes the candidate points for guaranteeing the feasibility of upper bounding the value functions, solves the linear McCormick relaxation based bilinear programming to obtain the worst cases, and combines the primal and dual updates for this hybrid binary and continuous decision-making problem to achieve fast convergence. We can verify that the RADP method enjoys a finite termination guarantee for the multistage robust optimization problems with achieving suboptimal solutions. Numerical tests on 118-bus and 2383-bus transmission systems have demonstrated that RADP can approach the suboptimal economic performance at significantly improved computational efficiency. © 2023, CC BY-NC-ND.

关键词： Computational efficiency

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Balanced Hashgraph Based on Dynamic MCMC: Efficient, Practical and Compatible

Balanced Hashgraph Based on Dynamic MCMC: Efficient, Practic...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Jing Tao Jianghua Peng Tian liu Yangfan Li Ruifeng Tian Chengpeng Wu Ministry of Education Key Lab For Intelligent Networks and Network Security Shenzhen Research Institute Xi'an Jiaotong University Xi'an China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’ an Jiaotong University Xi’ an China

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Traffic Matrix Prediction Based on Cross Aggregate GNN

Traffic Matrix Prediction Based on Cross Aggregate GNN

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Jing Tao Ken Cao Teng liu Ministry of Education Key Lab For Intelligent Networks and Network Security Shenzhen Research Institute Xi’an Jiaotong University Xi’an China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’ an Jiaotong University Xi’ an China

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Rethinking Software Misconfigurations in the Real World: An Empirical Study and Literature Analysis

arXiv

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arXiv 2024年

作者： liu, Yuhao Zhou, Yingnan Zhang, Hanfeng Chang, Zhiwei Xu, Sihan Jia, Yan Wang, Wei liu, Zheli College of Cyber Science Nankai University Tianjin China Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi China

Software misconfiguration has consistently been a major reason for software failures. Over the past twenty decades, much work has been done to detect and diagnose software misconfigurations. However, there is still a gap between real-world misconfigurations and the literature. It is desirable to investigate whether existing taxonomy and tools are applicable for real-world misconfigurations in modern software. In this paper, we conduct an empirical study on 823 real-world misconfiguration issues, based on which we propose a novel classification of the root causes of software misconfigurations, i.e., constraint violation, resource unavailability, component-dependency error, and misunderstanding of configuration effects. Then, we systematically review the literature on misconfiguration troubleshooting, and study the trends of research and the practicality of the tools and datasets in this field. We find that the research targets have changed from fundamental software to advanced applications (e.g., cloud service). In the meanwhile, the research on non-crash misconfigurations such as performance degradation and security risks also has a significant growth. Despite the progress, a majority of studies lack reproducibility due to the unavailable tools and evaluation datasets. In total, only six tools and two datasets are publicly available. However, the adaptability of these tools limit their practical use on real-world misconfigurations. We also summarize the important challenges and several suggestions to facilitate the research on software misconfiguration. Copyright © 2024, The Authors. All rights reserved.

关键词： Application programs

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KNN-ADMM based Online Missing Data Recovery in Electricity Markets

KNN-ADMM based Online Missing Data Recovery in Electricity M...

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第34届中国控制与决策会议

作者： Jiacheng liu Jiang Wu Zhanbo Xu Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University

This paper proposes a missing electricity markets data online recovery strategy combining the improved KNearest Neighbor clustering(KNN) and Alternating Direction Method of Multipliers(ADMM).Considering the coupling consumption behaviors in different end users,the electricity markets data can be approximated in low-rank feature with singular value decomposition ***,the sliding time window is adopted as an online repair strategy to realize real-time recovery and improve the speed of ***,the effectiveness of proposed online KNNADMM algorithm is verified through the electricity markets data of four Chinese *** show that the algorithm is suitable for electricity markets data online recovery and the recovery error is within 5% with the 15% missing data.

关键词： Data Recovery Alternating Direction Method of Multipliers(ADMM) K-Nearest Neighbor(KNN) Electricity Markets Data

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