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arXiv 2021年

作者： Jiang, Shanqing Yang, Lin Cheng, Guang Gao, Xianming Feng, Tao Zhou, Yuyang School of Cyber Science and Engineering Southeast University Nanjing China National Key Laboratory of Science and Technology on Information System Security Beijing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China

Measuring and evaluating network resilience has become an important aspect since the network is vulnerable to both uncertain disturbances and malicious attacks. networked systems are often composed of many dynamic components and change over time, which makes it difficult for existing methods to access the changeable situation of network resilience. This paper establishes a novel quantitative framework for evaluating network resilience using the Dynamic Bayesian network. The proposed framework can be used to evaluate the network's multistage resilience processes when suffering various attacks and recoveries. First, we define the dynamic capacities of network components and establish the network's five core resilience capabilities to describe the resilient networking stages including preparation, resistance, adaptation, recovery, and evolution;the five core resilience capabilities consist of rapid response capability, sustained resistance capability, continuous running capability, rapid convergence capability, and dynamic evolution capability. Then, we employ a two-time slices approach based on the Dynamic Bayesian network to quantify five crucial performances of network resilience based on core capabilities proposed above. The proposed approach can ensure the time continuity of resilience evaluation in time-varying networks. Finally, our proposed evaluation framework is applied to different attacks and recovery conditions in typical simulations and real-world network topology. Results and comparisons with extant studies indicate that the proposed method can achieve more accurate and comprehensive evaluation and can be applied to network scenarios under various attack and recovery intensity. Copyright © 2021, The Authors. All rights reserved.

关键词： Recovery

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Enhancing Microbe-Disease Association Prediction via Multi-View Graph Convolution and Latent Feature Learning

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Computational Biology and Chemistry 2025年

作者： Bo Wang Peilong Wu Xiaoxin Du Chunyu Zhang Shanshan Fu Tang Sun Xue Yang School of Computer and Control Engineering Qiqihar University Qiqihar 161006 Heilongjiang China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University Qiqihar 161006 Heilongjiang China

Microbes play a crucial role in the onset, progression, and treatment of diseases. To address the challenges of missing information and insufficient feature fusion in microbe-disease association prediction, this paper proposes an innovative computational model named MVGCVAE. MVGCVAE is the first model to synergistically integrate multi-view graph convolutional networks (GCNs), variational autoencoders (VAEs), and dynamic kernel matrix weighting for microbe-disease association (MDA) prediction. First, we construct multiple similarity networks between microbes and diseases, using GCN to independently process the node features in each view. To better fuse information from different similarity views, we introduce an attention mechanism to assign different weights to each perspective, thereby generating an initial comprehensive feature representation of diseases and microbes. This enables the model to more effectively integrate features from various perspectives and enhances its sensitivity and discriminative ability for key features. Next, based on a heterogeneous network, we feed the fused node features into the GCN for further representation learning. After each layer of feature extraction, we use a Variational Autoencoder (VAE) for variational inference to optimize node representations and enhance adaptation to sparse data and nonlinear relationships. Then, we propose a dynamic weighted kernel matrix strategy. This strategy uses a multi-layer perceptron (MLP) to adaptively generate weights, flexibly integrating kernel matrices computed from different embeddings at each layer to optimize the feature fusion process. Finally, we combine the weighted matrix with the feature matrix using matrix multiplication to calculate the microbe-disease association, and further optimize the model’s predictive capability through Laplacian Regularization. Experimental results show that MVGCVAE outperforms six existing comparison methods on multiple evaluation metrics. Additionally, cas

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Risk Modeling and Traceability of Boundary Attacks on Internet of Power Systems Based on Complex networks

Risk Modeling and Traceability of Boundary Attacks on Intern...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Xin Li Zhaoyang Qu Tong Yu Ming Xie Fu Yu Wei Ding School of Electrical Engineering Northeast Electric Power University Jilin China People’s Bank of China Qinghai Provincial Branch Xining China China Southern Power Grid Guangxi Power Grid Co. Ltd. Research Institute Nanning China Bijie Power Supply Bureau China Guizhou Power Grid Co. Ltd. Bijie China 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

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the increasing intelligence of power IoT terminal devices, the massive volume of data transmission, and the widespread adoption of shared services, traditional terminal boundary security mechanisms are unable to keep up with the rapid development of network technologies and applications. Additionally, they fail to trace and source network attacks, leading to frequent organized and purposeful malicious attack incidents. To address these issues, this paper proposes a research method for risk modeling and traceability of boundary attacks on power IoT terminals based on complex networks. First, based on the node information of power IoT terminal devices and complex network theory, a terminal boundary attack network (TBAN) is established. Next, using indicators related to complex network theory, such as degree, in-degree, and out-degree, the terminal boundary attack network is analyzed. Furthermore, a staged TBAN is proposed to analyze the attack risks at different times. Then, combining causal theory, a method for tracing and sourcing terminal boundary attacks based on a causal Bayesian network is proposed to identify and trace malicious attacks. Finally, validation and testing analysis are conducted using the actual deployment of power IoT terminal devices. The proposed analysis method transforms the spatial characteristics of the power IoT terminal boundary into a temporally sequential TBAN, which not only visually reflects attack risks but also reveals the temporal relationships of terminal boundary attacks between lines.

关键词： Complex networks Transforms Data science Data models Bayes methods Power systems Internet security Data communication Testing

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Regenerative Particle Thompson Sampling

arXiv

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arXiv 2022年

作者： Zhou, Zeyu Hajek, Bruce Choi, Nakjung Walid, Anwar Department of Radiology Mayo Clinic RochesterMN United States Department of Electrical and Computer Engineering The Coordinated Science Laboratory University of Illinois at Urbana-Champaign United States Network System and Security Research Nokia Bell Labs Murray HillNJ United States Amazon New York United States

This paper proposes regenerative particle Thompson sampling (RPTS), a flexible variation of Thompson sampling. Thompson sampling itself is a Bayesian heuristic for solving stochastic bandit problems, but it is hard to implement in practice due to the intractability of maintaining a continuous posterior distribution. Particle Thompson sampling (PTS) is an approximation of Thompson sampling obtained by simply replacing the continuous distribution by a discrete distribution supported at a set of weighted static particles. We observe that in PTS, the weights of all but a few fit particles converge to zero. RPTS is based on the heuristic: delete the decaying unfit particles and regenerate new particles in the vicinity of fit surviving particles. Empirical evidence shows uniform improvement from PTS to RPTS and flexibility and efficacy of RPTS across a set of representative bandit problems, including an application to 5G network slicing. © 2022, CC BY-SA.

关键词： Stochastic systems

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An enhanced MinHash encryption scheme for encrypted deduplication

An enhanced MinHash encryption scheme for encrypted deduplic...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Qiaowen Jia Guanxiong Ha Hanwei Wang Haoyu Ma Hang Chen State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Zhejiang Lab Hangzhou China The School of Cyber Engineering Xidian University Xi’an China

The encrypted deduplication can provide both storage savings and data confidentiality for cloud storage systems. Convergent encryption (CE) is a well-known solution for encrypted deduplication, but it brings huge computation and storage overheads for key management. MinHash encryption is an effective solution to this issue. It reduces the number of keys by grouping multiple consecutive chunks into segments and generating one key for each segment. However, MinHash encryption does not take full advantage of the segment similarity, which can be used to further reduce the overhead for key management. To this end, we augment MinHash encryption with Bloom filter and Locality Sensitivity Hash (LSH) to design an enhanced MinHash encryption scheme. Firstly, our scheme generates a sketch for each segment based on the Bloom filter, and projects sketches to the points in a hash table through LSH functions. Secondly, we detect the segment similarity by the distance between points, and similar segments are grouped into super-segments. Finally, we combine MinHash encryption and server-aided message-locked encryption to encrypt the super-segments for achieving encrypted deduplication. We conduct trace-driven experiments using a realworld dataset. Compared with MinHash encryption, our scheme has higher storage efficiency and better encryption performance.

关键词： Privacy Maximum likelihood estimation Data privacy Cloud computing Sensitivity Encryption

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Outlier-resistant Multi-hop Localization Based on Weighted Bounding-Box with the Virtual Anchor

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Journal of computers (Taiwan) 2021年第5期32卷 184-191页

作者： Yan, Hao Yan, Xiao-Yong Mo, Xiao-Hui School of Network Security Jinling Institute of Technology Nanjing China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou China School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Applied Mathematics Putian University Putian China School of Computer Engineering Jinling Institute of Technology Nanjing China

Multi-hop localization has attracted considerable research interests, recently. Most of the existing methods assume that the distribution of anchors is non-collinear, which often does not represent real-world scenes, particularly for irregular networks. In this paper, we present a novel multihop localization scheme to address the ill-posed issue caused by the inappropriate distribution of anchors. First, we recognize whether the preliminary estimated location of nonanchor obtained from existing methods deviates from its true location. So it becomes a new anchor (also called the virtual anchor) after it is identified that the preliminary location of a nonanchor is within an unfit area. With the support of both the virtual anchor and other anchors, a smaller and delimited area can be found in which the target node is situated. Next, we employ the weighted Bounding-Box algorithm to compute the non-anchors’ locations that are close to their true locations. We evaluate our scheme with different network topologies based on other state-of-the-art research and analyze its performance. The results demonstrate that our proposed scheme can improve the accuracy of localization with lower computation complexity. © 2021 computer Society of the Republic of China. All rights reserved.

关键词： Location

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User Privacy-Preserving Scheme Based on Anonymous Authentication in Smart Grid 1st

User Privacy-Preserving Scheme Based on Anonymous Authentica...

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1st International Conference on security and Privacy in Digital Economy, SPDE 2020

作者： Lai, Qichao Xu, Li Yuan, Manli Wang, Feng Fang, He College of Mathematics and Informatics Fujian Normal University Fuzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou China Department of Electrical and Computer Engineering The University of Western Ontario London Canada

ISBN: (纸本)9789811591280

One of the main functions of smart meter is to assist utility companies charge from users intelligently. To save computation and communication resources, while protecting the privacy of users, many data aggregation schemes for privacy protection have been proposed. However, the real-time power consumption data collected by utility companies may be insufficiently fine-grained (individual user electricity consumption data at time slot t, usually, t is 15 min) in these schemes, resulting in low data availability. For example, the collected data cannot be used for anomaly detection. To overcome this difficulty, we propose a new anonymous authentication scheme using Boneh-Schacham group signature which supports fine-grained data collection. Through this scheme, even though the gateway receives user’s electricity consumption information, it can protect the privacy of the user. Moreover, dynamic price, lower secret key management difficulty and track of the faulty smart meters can also be realized in our scheme. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Smart meters

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Enhanced Competitive Swarm Optimizer for Multi-task Optimization 2020

Enhanced Competitive Swarm Optimizer for Multi-task Optimiza...

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Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

作者： Wei Li Junqing Yuan Haonan Luo Zhou Lei Qingzheng Xu School of Computer Science and Engineering Xi'an University of Technology and Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China College of Information and Communication National University of Defense Technology Xi'an China

ISBN: (纸本)9781450387835

In the real world, many problems possess the high degree of similarity. Inspired by multifactorial inheritance model, evolutionary multi-task optimization paradigm is proposed to simultaneously solve multiple optimization problems. However, experimental results have revealed that the performance of multifactorial evolutionary algorithm deteriorates with negative knowledge transfer between uncorrelated tasks. To alleviate this issue, we proposed an enhanced competitive swarm optimizer to explore the generality of the multitasking paradigm. A new velocity update mechanism for losers is proposed to improve the search ability. Further, a mating approach is proposed to transfer implicit knowledge among tasks for generating offspring. Experimental and statistical analyses are performed on CEC2017 evolutionary multitask optimization competition. Results show that the proposed algorithm is competitive in comparison with other state-of-the-art multifactorial optimization algorithms.

关键词： Evolutionary multitasking competitive swarm optimizer evolutionary algorithms

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Uncertainty-Aware Relational Graph Neural network for Few-Shot Knowledge Graph Completion

arXiv

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arXiv 2024年

作者： Li, Qian Guo, Shu Chen, Yingjia Ji, Cheng Sheng, Jiawei Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The State Key Laboratory of Software Development Environment Beihang University Beijing100083 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100083 China School of Mathematics Edinburgh University Edinburgh United Kingdom Institute of Information Engineering Chinese Academy of Sciences Beijing100083 China School of Cyber Security University of Chinese Academy of Sciences Beijing100083 China

Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty of entities and triples may limit the few-shot learning, but existing FKGC works neglect such uncertainty, which leads them more susceptible to limited reference samples with noises. In this paper, we propose a novel uncertainty-aware few-shot KG completion framework (UFKGC) to model uncertainty for a better understanding of the limited data by learning representations under Gaussian distribution. Uncertainty representation is first designed for estimating the uncertainty scope of the entity pairs after transferring feature representations into a Gaussian distribution. Further, to better integrate the neighbors with uncertainty characteristics for entity features, we design an uncertainty-aware relational graph neural network (UR-GNN) to conduct convolution operations between the Gaussian distributions. Then, multiple random samplings are conducted for reference triples within the Gaussian distribution to generate smooth reference representations during the optimization. The final completion score for each query instance is measured by the designed uncertainty optimization to make our approach more robust to the noises in few-shot scenarios. Experimental results show that our approach achieves excellent performance on two benchmark datasets compared to its competitors. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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ResfEANet: ResNet-fused External Attention network for Tuberculosis Diagnosis using Chest X-ray Images

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computer Methods and Programs in Biomedicine Update 2024年 5卷

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Nnani, Ann O Deng, Fuhu Ejiyi, Thomas Ugochukwu Ejiyi, Makuachukwu Bennedith Agbesi, Victor Kwaku Bamisile, Olusola Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China College of Environmental Science and Engineering Hohai University Nanjing China Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu Nigeria Pharmacy Department University of Nigeria Nsukka Enugu Nigeria School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Centre Chengdu University of Technology Chengdu China

Pulmonary tuberculosis (TB), the most prevalent form of TB, remains a major global public health concern, contributing to more than a million deaths each year. The accurate and timely diagnosis of this disease is of paramount importance for effective control and treatment. Chest X-ray (CXR) images have emerged as a valuable tool for screening lung diseases, including TB, owing to their cost-effectiveness and non-invasiveness. Despite advancements in technology, the challenges associated with interpreting CXR images persist, primarily due to the scarcity of trained radiologists. This underscores the pressing need for an automated and cost-effective computer-aided system capable of diagnosing TB, assisting medical practitioners in distinguishing between TB-positive and negative CXR scans. In response to this need, we introduce an innovative approach called ResNet-fused External Attention network (ResfEANet). This network excels in accurately classifying TB from CXR images, achieving remarkable levels of accuracy and sensitivity. ResfEANet is built upon ResNet and incorporates an External Attention mechanism, albeit with fewer residual network blocks than ResNet-50 resulting in a relatively shallow network with fewer layers. This approach proves highly effective in feature extraction and yields competitive results in the classification of TB. Our method was employed to train a model that demonstrated an impressive accuracy rate of 97.59% and a remarkable sensitivity of 100% in binary classification tasks with optimal computational cost. These outcomes suggest that our proposed approach has the potential to serve as a valuable secondary tool in clinical decision-making, providing crucial assistance to radiologists and healthcare professionals. © 2023

关键词： Chest X-ray images External Attention ResfEANet ResNet Tuberculosis

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