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8th International Symposium on security and Privacy in Social Networks and Big Data, SocialSec 2022

作者： Zhang, Zhuoran Tian, Haibo Zhang, Fangguo School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9789811972416

Blockchain has raised public concern due to its public, distributed, and decentration characteristics. This technique has been applied not only in the financial area, but also in e-government, e-health and many other digital systems. However, the security of the most widely used blockchain technique was built on the elliptic curve cryptography, which will be insecure in the will-coming quantum computing era. In this paper, we propose to use a quantum-resisted digital signature scheme from coding theory instead of ECDSA. More precisely, we modified the well-known WAVE signature scheme from (U, U+ V) code into (U, U+ V, U+ V+ W) code, and achieve better performance. With this post-quantum signature scheme, we build a post-quantum blockchain system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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NPIPVis:A visualization system involving NBA visual analysis and integrated learning model prediction

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Virtual Reality & Intelligent Hardware 2022年第5期4卷 444-458页

作者： Zhuo SHI Mingrui LI Meng WANG Jing SHEN Wei CHEN Xiaonan LUO Department of Art and Design University of Guilin Electronic TechnologyGuilin 541004China Department of Computer and Information Security University of Guilin Electronic TechnologyGuilin 541004China Department of Guangxi Key Laboratory of Image and Graphic Intelligent Processing University of Guilin Electronic TechnologyGuilin 541004China Department of State Key Laboratory of CAD&CG University of ZhejiangHangzhou 310058China

Background Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players. Methods This study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology,and i Storyline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a team’s wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and Random Under Sampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using GridSearch CV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive ex Planations(SHAP) model was introduced to enhance the interpretability of the model. Results The experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model. Conc

关键词： Sports visualization Parallel aggregated ordered hypergraph Calliope IStoryline Integrated learning SHAP model

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Syn-BERT: Bi-level Synonymous Data Augmented Self-supervised Embedding-based information Retrieval with BERT 5

Syn-BERT: Bi-level Synonymous Data Augmented Self-supervised...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Yu, Zhaojian Huang, Tianlin Chen, Rong Chen, Haosheng Lin, Shuyuan College of Cyber Security Jinan University Guangzhou510632 China School of Mathematics and Computer Science Quanzhou Normal University Quanzhou362000 China Information Engineering College Xizang Minzu University Shanyang712082 China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China

ISBN: (纸本)9798350355925

information retrieval, which seeks to locate the most relevant documents from a document pool given a user query, is critical in a wide range of applications. However, current embedding-based retrieval models require a high quantity of labeled documents as training data to construct appropriate target training tasks, which can be expensive and difficult to obtain in practical applications. In this paper, we present a Synonymous BERT (Syn-BERT) model with strong training data augmentation with the aim of building an effective and efficient retrieval system on unlabeled documents. Unlike most existing embedding-based retrieval models, which require high-quality labeled documents to learn the semantic connections between original documents and labels, the proposed Syn-BERT generates synonymous queries and irrelevant queries for each original document in unlabeled documents and exploits the distances between them to obtain better embeddings. We evaluated the proposed Syn-BERT on two publicly-available Frequently Asked Question datasets, with the experimental results demonstrating that the proposed Syn-BERT achieved state-of-the-art results in embedding-based retrieval tasks with unlabeled documents. © 2024 IEEE.

关键词： Semantics

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Trajectory Planning of Rechargeable UAVs for AoS Minimization in IoT Networks

Trajectory Planning of Rechargeable UAVs for AoS Minimizatio...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yunchao Liu Suling Lin Jie Gong Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Freshness of information is a critical issue for real-time Internet of Things (IoT) applications. Unmanned aerial vehicles (UAVs), due to their high flexibility and low cost, are widely deployed in time-sensitive IoT scenarios. In this article, we explore the trajectory planning problem in rechargeable-UAV-aided IoT Networks. To accurately measure the freshness of information, we introduce Age of information (AoI) and Age of Synchronization (AoS). AoI is utilized to plan the UAVs' trajectories for accessing sensors (SNs) and reduce the average AoS of the system. UAVs can recharge at charging stations to maintain energy above a certain threshold. We formulate a Markov decision process with the objective of minimizing the average AoS under the energy constraint. To adjust the dynamic environment, we propose a multi-agent deep reinforcement learning method to jointly optimize the UAVs' flight trajectories and battery recharging. Simulation results demonstrate that the proposed algorithm can significantly reduce the total average AoS under the energy constraint.

关键词： Wireless communication Trajectory planning Simulation Signal processing algorithms Signal processing Trajectory Sensors Internet of Things Synchronization Vehicle dynamics

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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Learning-Based Computation Offloading in Hierarchical MEC system with Energy Harvesting

Learning-Based Computation Offloading in Hierarchical MEC Sy...

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IEEE Conference on Vehicular technology (VTC)

作者： Chufan Jian Jie Gong Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

With the prevalence of real-time Internet of Things (IOT) applications, mobile edge computing (MEC) has emerged as a paradigm to ensure low-latency computation. Hierarchical MEC system, which deploy computation resources in a hetero-geneous way, is more applicable than traditional MEC system in scenarios with various types of offloading tasks. On the other hand, the boom of AI applications has led to an increasing energy consumption of MEC servers. Energy harvesting technologies effectively reduce the grid energy consumption by leveraging renewable energy sources. To this end, we propose a task offloading scheme in the scenario with hierarchical task offloading and green energy harvesting. Specifically, We formulate the offloading problem as minimizing the weighted sum of execution latency and energy consumption. To obtain the optimal offloading decisions, we propose a computing offloading algorithm based on deep reinforcement learning (DRL). Simulation results demonstrate that our proposed scheme efficiently reduces the sum cost compared with other approaches.

关键词： Green energy Energy consumption Vehicular and wireless technologies Costs Multi-access edge computing Simulation Real-time systems

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PB-UAP: Hybride Universal Adversarial Attack for Image Segmentation

PB-UAP: Hybride Universal Adversarial Attack for Image Segme...

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

作者： Yufei Song Ziqi Zhou Minghui Li Xianlong Wang Hangtao Zhang Menghao Deng Wei Wan Shengshan Hu Leo Yu Zhang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, i.e., adversarial attacks on deep neural networks. Existing works primarily focus on image classification tasks, aiming to alter the model’s predicted labels. Due to the output complexity and deeper network architectures, research on adversarial examples for segmentation models is still limited, particularly for universal adversarial perturbations. In this paper, we propose a novel universal adversarial attack method designed for segmentation models, which includes dual feature separation and low-frequency scattering modules. The two modules guide the training of adversarial examples in the pixel and frequency space, respectively. Experiments demonstrate that our method achieves high attack success rates surpassing the state-of-the-art methods, and exhibits strong transferability across different models.

关键词： Training Image segmentation Perturbation methods Semantics Scattering Signal processing Predictive models Network architecture Robustness Speech processing

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Improving Code Search with Multi-Modal Momentum Contrastive Learning

Improving Code Search with Multi-Modal Momentum Contrastive ...

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IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Zejian Shi Yun Xiong Yao Zhang Zhijie Jiang Jinjing Zhao Lei Wang Shanshan Li Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Peng Cheng Laboratory Shenzhen China School of Computer National University of Defense Technology China National Key Laboratory of Science and Technology on Information System Security China

Contrastive learning has recently been applied to enhancing the BERT-based pre-trained models for code search. However, the existing end-to-end training mechanism cannot sufficiently utilize the pre-trained models due to the limitations on the number and variety of negative samples. In this paper, we propose MoCoCS, a multi-modal momentum contrastive learning method for code search, to improve the representations of query and code by constructing large-scale multi-modal negative samples. MoCoCS increases the number and the variety of negative samples through two optimizations: integrating multi-batch negative samples and constructing multi-modal negative samples. We first build momentum contrasts for query and code, which enables the construction of large-scale negative samples out of a mini-batch. Then, to incorporate multi-modal code information, we build multi-modal momentum contrasts by encoding the abstract syntax tree and the data flow graph with a momentum encoder. Experiments on CodeSearchNet with six programming languages demonstrate that our method can further improve the effectiveness of pre-trained models for code search.

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Reputation based Adaptive Federated Learning in Medical Scenarios

Reputation based Adaptive Federated Learning in Medical Scen...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Lianhai Wang Tianrui Liu Yingxiaochun Wang Xinlei Wang Qi 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Federated Learning (FL) is a distributed machine learning paradigm designed to address data silos and protect data privacy. However, in medical scenarios, the heterogeneity in data quality and the non-independent and identically distributed (Non-IID) nature of medical data from different hospitals can lead to degraded model performance in FL. To tackle this challenge, this paper proposes an adaptive federated learning framework based on reputation. By using a reputation evaluation model, the framework adaptively adjusts the aggregation weights of the models to enhance the performance of the global model. Additionally, our reputation evaluation model, integrated with blockchain technology, can rapidly identify malicious clients and effectively prevent poisoning attacks. Experimental results on two medical datasets demonstrate that this framework achieves higher model accuracy and faster convergence.

关键词： Adaptation models Data privacy Federated learning Hospitals Data integrity Computational modeling Evaluation models Data models security Convergence

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