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Security issues of news data dissemination in internet environment 互联网环境下新闻数据传播的安全问题
Pub Date : 2024-03-22 DOI: 10.1186/s13677-024-00632-w
Kang Song, Wenqian Shang, Yong Zhang, Tong Yi, Xuan Wang
With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep learning methods to mine the changing trend of user features related to rumor events, and designs a rumor detection model called Time Based User Feature Capture Model(TBUFCM). To obtain a new feature vector representing the user's comprehensive features under the current event, the proposed model first recomputes the user feature vector by using feature enhancement function. Then it utilizes GRU(Gate Recurrent Unit, GRU) and CNN(Convolutional Neural Networks, CNN) models to learn the global and local changes of user features, respectively. Finally, the hidden rumor features in the process of rumor propagation can be discovered by user and time information. The experimental results show that TBUFCM outperforms the baseline model, and when there are only 20 forwarded posts, it can also reach an accuracy of 92%. The proposed method can effectively solve the security problem of news data dissemination in the Internet environment.
随着人工智能的兴起和社交媒体的发展,人们的沟通交流更加便捷。然而,在互联网环境下,新闻数据的不真实传播导致了大量问题。因此,高效、自动地检测社交平台中的谣言成为近年来的一个重要研究方向。本文利用深度学习方法挖掘与谣言事件相关的用户特征变化趋势,设计了一种谣言检测模型--基于时间的用户特征捕捉模型(TBUFCM)。为了获得代表当前事件下用户综合特征的新特征向量,所提出的模型首先利用特征增强函数重新计算用户特征向量。然后,它利用 GRU(门递归单元,GRU)和 CNN(卷积神经网络,CNN)模型分别学习用户特征的全局和局部变化。最后,通过用户和时间信息发现谣言传播过程中隐藏的谣言特征。实验结果表明,TBUFCM 优于基线模型,当转发帖子只有 20 个时,其准确率也能达到 92%。所提出的方法能有效解决互联网环境下新闻数据传播的安全问题。
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引用次数: 0
Short-term forecasting of surface solar incident radiation on edge intelligence based on AttUNet 基于 AttUNet 的边缘智能地表太阳入射辐射短期预报
Pub Date : 2024-03-22 DOI: 10.1186/s13677-024-00624-w
Mengmeng Cui, Shizhong Zhao, Jinfeng Yao
Solar energy has emerged as a key industry in the field of renewable energy due to its universality, harmlessness, and sustainability. Accurate prediction of solar radiation is crucial for optimizing the economic benefits of photovoltaic power plants. In this paper, we propose a novel spatiotemporal attention mechanism model based on an encoder-translator-decoder architecture. Our model is built upon a temporal AttUNet network and incorporates an auxiliary attention branch to enhance the extraction of spatiotemporal correlation information from input images. And utilize the powerful ability of edge intelligence to process meteorological data and solar radiation parameters in real-time, adjust the prediction model in real-time, thereby improving the real-time performance of prediction. The dataset utilized in this study is sourced from the total surface solar incident radiation (SSI) product provided by the geostationary meteorological satellite FY4A. After experiments, the SSIM has been improved to 0.86. Compared with other existing models, our model has obvious advantages and has great prospects for short-term prediction of surface solar incident radiation.
太阳能因其普遍性、无害性和可持续性,已成为可再生能源领域的重要产业。准确预测太阳辐射对于优化光伏电站的经济效益至关重要。在本文中,我们提出了一种基于编码器-翻译器-解码器架构的新型时空注意力机制模型。我们的模型建立在时空 AttUNet 网络的基础上,并加入了辅助注意力分支,以增强从输入图像中提取时空相关信息的能力。并利用边缘智能的强大能力实时处理气象数据和太阳辐射参数,实时调整预测模型,从而提高预测的实时性。本研究使用的数据集来自地球静止气象卫星 FY4A 提供的地表太阳总入射辐射(SSI)产品。经过实验,SSIM 已提高到 0.86。与其他现有模型相比,我们的模型具有明显优势,在地表太阳入射辐射短期预测方面具有广阔前景。
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引用次数: 0
Edge intelligence empowered delivery route planning for handling changes in uncertain supply chain environment 边缘智能支持交付路线规划,以应对不确定供应链环境中的变化
Pub Date : 2024-03-22 DOI: 10.1186/s13677-024-00613-z
Gaoxian Peng, Yiping Wen, Wanchun Dou, Tiancai Li, Xiaolong Xu, Qing Ye
Traditional delivery route planning faces challenges in reducing logistics costs and improving customer satisfaction with growing customer demand and complex road traffic, especially in uncertain supply chain environment. To address these challenges, we introduce an innovative two-phase delivery route planning method integrating edge intelligence technology. The novelty of our approach lies in utilizing edge computing devices to monitor real-time changes in road conditions and dynamically adjust delivery routes, thereby providing an effective solution for efficient and flexible logistics. Initially, we construct a mixed-integer programming model that minimizes the total cost under constraints such as customer destinations and time windows. Subsequently, in the cloud-edge collaborative mode, edge computing devices are utilized to collect real-time road conditions and transmit it to the cloud server. The cloud server comprehensively considers customer demand and road condition changes and employs adaptive genetic algorithms and A-star algorithms to adjust the delivery routes dynamically. Finally, comprehensive experiments are conducted to validate the effectiveness of our method. The results demonstrate that our approach can promptly respond to changes in customer demands and road conditions and flexibly plan the optimal delivery routes, thereby significantly reducing overall costs and enhancing customer satisfaction.
面对日益增长的客户需求和复杂的道路交通,尤其是在不确定的供应链环境中,传统的配送路线规划在降低物流成本和提高客户满意度方面面临挑战。为了应对这些挑战,我们引入了一种融合边缘智能技术的创新型两阶段配送路线规划方法。这种方法的新颖之处在于利用边缘计算设备实时监控路况变化,动态调整配送路线,从而为高效灵活的物流提供有效的解决方案。首先,我们构建了一个混合整数编程模型,在客户目的地和时间窗口等约束条件下使总成本最小化。随后,在云-边缘协作模式下,利用边缘计算设备收集实时路况并传输到云服务器。云服务器综合考虑客户需求和路况变化,采用自适应遗传算法和 A-star 算法动态调整配送路线。最后,我们进行了综合实验来验证我们方法的有效性。结果表明,我们的方法能够及时响应客户需求和路况的变化,灵活规划最优配送路线,从而显著降低总体成本,提高客户满意度。
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引用次数: 0
A secure cross-domain authentication scheme based on threshold signature for MEC 基于 MEC 门限签名的安全跨域认证方案
Pub Date : 2024-03-22 DOI: 10.1186/s13677-024-00631-x
Lei Chen, Chong Guo, Bei Gong, Muhammad Waqas, Lihua Deng, Haowen Qin
The widespread adoption of fifth-generation mobile networks has spurred the rapid advancement of mobile edge computing (MEC). By decentralizing computing and storage resources to the network edge, MEC significantly enhances real-time data access services and enables efficient processing of large-scale dynamic data on resource-limited devices. However, MEC faces considerable security challenges, particularly in cross-domain service environments, where every device poses a potential security threat. To address this issue, this paper proposes a secure cross-domain authentication scheme based on a threshold signature tailored to MEC’s multi-subdomain nature. The proposed scheme employs a (t,n) threshold mechanism to bolster system resilience and security, catering to large-scale, dynamic, and decentralized MEC scenarios. Additionally, the proposed scheme features an efficient authorization update function that facilitates the revocation of malicious nodes. Security analysis confirmed that the proposed scheme satisfies unforgeability, collusion resistance, non-repudiation and forward security. Theoretical evaluation and experimental simulation verify the effectiveness and feasibility of the proposed scheme. Compared with existing schemes, the proposed scheme has higher computational performance while implementing secure authorization updates.
第五代移动网络的广泛应用推动了移动边缘计算(MEC)的快速发展。通过将计算和存储资源分散到网络边缘,MEC 显著增强了实时数据访问服务,并能在资源有限的设备上高效处理大规模动态数据。然而,MEC 面临着相当大的安全挑战,特别是在跨域服务环境中,每个设备都构成潜在的安全威胁。为解决这一问题,本文提出了一种基于阈值签名的安全跨域验证方案,该方案是针对 MEC 的多子域特性而量身定制的。该方案采用 (t,n) 门限机制来增强系统的弹性和安全性,以适应大规模、动态和分散的 MEC 场景。此外,该方案还具有高效的授权更新功能,便于撤销恶意节点。安全分析证实,所提出的方案满足不可伪造性、抗串通性、不可抵赖性和前向安全性。理论评估和实验模拟验证了所提方案的有效性和可行性。与现有方案相比,拟议方案在实现安全授权更新的同时具有更高的计算性能。
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引用次数: 0
AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition 利用多源传感器集成与降噪序列分解实现人工智能物联网驱动的多源传感器排放监测和预测
Pub Date : 2024-03-21 DOI: 10.1186/s13677-024-00598-9
Mughair Aslam Bhatti, Zhiyao Song, Uzair Aslam Bhatti, Syam M. S
The integration of multi-source sensors based AIoT (Artificial Intelligence of Things) technologies into air quality measurement and forecasting is becoming increasingly critical in the fields of sustainable and smart environmental design, urban development, and pollution control. This study focuses on enhancing the prediction of emission, with a special emphasis on pollutants, utilizing advanced deep learning (DL) techniques. Recurrent neural networks (RNNs) and long short-term memory (LSTM) neural networks have shown promise in predicting air quality trends in time series data. However, challenges persist due to the unpredictability of air quality data and the scarcity of long-term historical data for training. To address these challenges, this study introduces the AIoT-enhanced EEMD-CEEMDAN-GCN model. This innovative approach involves decomposing the input signal using EEMD (Ensemble Empirical Mode Decomposition) and CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to extract intrinsic mode functions. These functions are then processed through a GCN (Graph Convolutional Network) model, enabling precise prediction of air quality trends. The model’s effectiveness is validated using air pollution datasets from four provinces in China, demonstrating its superiority over various deep learning models (GCN, EMD-GCN) and series decomposition models (EEMD-GCN, CEEMDAN-GCN). It achieves higher accuracy and better data fitting, outperforming other models in key metrics such as MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and R2 (Coefficient of Determination). The implementation of this AIoT-enhanced model in air pollution prediction allows decision-makers to more accurately anticipate changes in air quality, particularly concerning carbon emissions. This facilitates more effective planning of mitigation measures, improvement of public health, and optimization of resource allocation. Moreover, the model adeptly addresses the complexities of air quality data, contributing significantly to enhanced monitoring and management strategies in the context of sustainable urban development and environmental conservation.
将基于多源传感器的 AIoT(人工智能物联网)技术整合到空气质量测量和预测中,在可持续智能环境设计、城市发展和污染控制领域正变得越来越重要。本研究的重点是利用先进的深度学习(DL)技术,加强对污染物排放的预测。递归神经网络(RNN)和长短期记忆(LSTM)神经网络在预测时间序列数据中的空气质量趋势方面已显示出良好的前景。然而,由于空气质量数据的不可预测性和用于训练的长期历史数据的稀缺性,挑战依然存在。为了应对这些挑战,本研究引入了 AIoT 增强型 EEMD-CEEMDAN-GCN 模型。这种创新方法包括使用 EEMD(集合经验模式分解)和 CEEMDAN(带自适应噪声的完全集合经验模式分解)对输入信号进行分解,以提取内在模式函数。然后通过 GCN(图形卷积网络)模型对这些函数进行处理,从而实现对空气质量趋势的精确预测。该模型的有效性通过中国四个省份的空气污染数据集进行了验证,证明其优于各种深度学习模型(GCN、EMD-GCN)和序列分解模型(EEMD-GCN、CEEMDAN-GCN)。它实现了更高的精度和更好的数据拟合,在 MAE(平均绝对误差)、MSE(平均平方误差)、MAPE(平均绝对百分比误差)和 R2(判定系数)等关键指标上优于其他模型。在空气污染预测中实施这一人工智能物联网增强型模型后,决策者可以更准确地预测空气质量的变化,尤其是碳排放方面的变化。这有助于更有效地规划缓解措施、改善公众健康和优化资源分配。此外,该模型还能巧妙地解决空气质量数据的复杂性问题,为在城市可持续发展和环境保护的背景下加强监测和管理策略做出了重要贡献。
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引用次数: 0
Edge computing-oriented smart agricultural supply chain mechanism with auction and fuzzy neural networks 利用拍卖和模糊神经网络建立面向边缘计算的智能农业供应链机制
Pub Date : 2024-03-21 DOI: 10.1186/s13677-024-00626-8
Qing He, Hua Zhao, Yu Feng, Zehao Wang, Zhaofeng Ning, Tingwei Luo
Powered by data-driven technologies, precision agriculture offers immense productivity and sustainability benefits. However, fragmentation across farmlands necessitates distributed transparent automation. We developed an edge computing framework complemented by auction mechanisms and fuzzy optimizers that connect various supply chain stages. Specifically, edge computing offers powerful capabilities that enable real-time monitoring and data-driven decision-making in smart agriculture. We propose an edge computing framework tailored to agricultural needs to ensure sustainability through a renewable solar energy supply. Although the edge computing framework manages real-time crop monitoring and data collection, market-based mechanisms, such as auctions and fuzzy optimization models, support decision-making for smooth agricultural supply chain operations. We formulated invisible auction mechanisms that hide actual bid values and regulate information flows, combined with machine learning techniques for robust predictive analytics. While rule-based fuzzy systems encode domain expertise in agricultural decision-making, adaptable training algorithms help optimize model parameters from the data. A two-phase hybrid learning approach is formulated. Fuzzy optimization models were formulated using domain expertise for three key supply chain decision problems. Auction markets discover optimal crop demand–supply balancing and pricing signals. Fuzzy systems incorporate domain knowledge into interpretable crop-advisory models. An integrated evaluation of 50 farms over five crop cycles demonstrated the high performance of the proposed edge computing-oriented auction-based fuzzy neural network model compared with benchmarks.
在数据驱动技术的推动下,精准农业带来了巨大的生产力和可持续发展效益。然而,由于农田分散,需要分布式透明自动化。我们开发了一个边缘计算框架,辅以拍卖机制和模糊优化器,将供应链的各个阶段连接起来。具体来说,边缘计算提供了强大的功能,可在智能农业中实现实时监控和数据驱动决策。我们提出了一个适合农业需求的边缘计算框架,以通过可再生太阳能供应确保可持续性。虽然边缘计算框架可管理实时作物监测和数据收集,但拍卖和模糊优化模型等基于市场的机制可为农业供应链的平稳运营提供决策支持。我们制定了隐形拍卖机制,以隐藏实际出价并规范信息流,同时结合机器学习技术进行稳健的预测分析。在基于规则的模糊系统编码农业决策领域专业知识的同时,适应性训练算法有助于从数据中优化模型参数。本文提出了一种两阶段混合学习方法。针对三个关键的供应链决策问题,利用领域专业知识制定了模糊优化模型。拍卖市场发现最佳作物供需平衡和定价信号。模糊系统将领域知识纳入可解释的作物咨询模型。对 50 个农场的五个作物周期进行的综合评估表明,与基准相比,所提出的以边缘计算为导向、基于拍卖的模糊神经网络模型具有很高的性能。
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引用次数: 0
An integrated SDN framework for early detection of DDoS attacks in cloud computing 用于早期检测云计算中 DDoS 攻击的集成 SDN 框架
Pub Date : 2024-03-20 DOI: 10.1186/s13677-024-00625-9
Asha Varma Songa, Ganesh Reddy Karri
Cloud computing is a rapidly advancing technology with numerous benefits, such as increased availability, scalability, and flexibility. Relocating computing infrastructure to a network simplifies hardware and software resource monitoring in the cloud. Software-Defined Networking (SDN)-based cloud networking improves cloud infrastructure efficiency by dynamically allocating and utilizing network resources. While SDN cloud networks offer numerous advantages, they are vulnerable to Distributed Denial-of-Service (DDoS) attacks. DDoS attacks try to stop genuine users from using services and drain network resources to reduce performance or shut down services. However, early-stage detection of DDoS attack patterns in cloud environments remains challenging. Current methods detect DDoS at the SDN controller level, which is often time-consuming. We recommend focusing on SDN switches for early detection. Due to the large volume of data from diverse sources, we recommend traffic clustering and traffic anomalies prediction which is of DDoS attacks at each switch. Furthermore, to consolidate the data from multiple clusters, event correlation is performed to understand network behavior and detect coordinated attack activities. Many existing techniques stay behind for early detection and integration of multiple techniques to detect DDoS attack patterns. In this paper, we introduce a more efficient and effectively integrated SDN framework that addresses a gap in previous DDoS solutions. Our framework enables early and accurate detection of DDoS traffic patterns within SDN-based cloud environments. In this framework, we use Recursive Feature Elimination (RFE), Density Based Spatial Clustering (DBSCAN), time series techniques like Auto Regressive Integrated Moving Average (ARIMA), Lyapunov exponent, exponential smoothing filter, dynamic threshold, and lastly, Rule-based classifier. We have evaluated the proposed RDAER model on the CICDDoS 2019 dataset, that achieved an accuracy level of 99.92% and a fast detection time of 20 s, outperforming existing methods.
云计算是一项快速发展的技术,具有许多优点,如可用性更高、可扩展性和灵活性更强。将计算基础设施迁移到网络可简化云中的硬件和软件资源监控。基于软件定义网络(SDN)的云网络可动态分配和利用网络资源,从而提高云基础设施的效率。虽然 SDN 云网络具有众多优势,但也容易受到分布式拒绝服务 (DDoS) 攻击。DDoS 攻击试图阻止真正的用户使用服务,并消耗网络资源以降低性能或关闭服务。然而,在云环境中对 DDoS 攻击模式进行早期检测仍具有挑战性。目前的方法是在 SDN 控制器级别检测 DDoS,这通常非常耗时。我们建议将早期检测的重点放在 SDN 交换机上。由于来自不同来源的数据量巨大,我们建议在每个交换机上对 DDoS 攻击进行流量聚类和流量异常预测。此外,为了整合来自多个集群的数据,我们还进行了事件关联,以了解网络行为并检测协同攻击活动。在早期检测和整合多种技术以检测 DDoS 攻击模式方面,现有的许多技术还处于落后状态。在本文中,我们介绍了一种更高效、更有效的集成式 SDN 框架,它弥补了以往 DDoS 解决方案的不足。我们的框架可在基于 SDN 的云环境中实现对 DDoS 流量模式的早期准确检测。在该框架中,我们使用了递归特征消除(RFE)、基于密度的空间聚类(DBSCAN)、时间序列技术(如自回归综合移动平均(ARIMA)、Lyapunov 指数、指数平滑滤波器、动态阈值)以及基于规则的分类器。我们在 CICDDoS 2019 数据集上对所提出的 RDAER 模型进行了评估,其准确率达到 99.92%,快速检测时间为 20 秒,优于现有方法。
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引用次数: 0
An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications 利用 AdaHessian 优化神经网络预测加密劫持攻击,确保 MEC 应用程序的加密交换操作安全
Pub Date : 2024-03-18 DOI: 10.1186/s13677-024-00630-y
Uma Rani, Sunil Kumar, Neeraj Dahiya, Kamna Solanki, Shanu Rakesh Kuttan, Sajid Shah, Momina Shaheen, Faizan Ahmad
Bitcoin exchange security is crucial because of MEC's widespread use. Cryptojacking has compromised MEC app security and bitcoin exchange ecosystem functionality. This paper propose a cutting-edge neural network and AdaHessian optimization technique for cryptojacking prediction and defense. We provide a cutting-edge deep neural network (DNN) cryptojacking attack prediction approach employing pruning, post-training quantization, and AdaHessian optimization. To solve these problems, this paper apply pruning, post-training quantization, and AdaHessian optimization. A new framework for quick DNN training utilizing AdaHessian optimization can detect cryptojacking attempts with reduced computational cost. Pruning and post-training quantization improve the model for low-CPU on-edge devices. The proposed approach drastically decreases model parameters without affecting Cryptojacking attack prediction. The model has Recall 98.72%, Precision 98.91%, F1-Score 99.09%, MSE 0.0140, RMSE 0.0137, and MAE 0.0139. Our solution beats state-of-the-art approaches in precision, computational efficiency, and resource consumption, allowing more realistic, trustworthy, and cost-effective machine learning models. We address increasing cybersecurity issues holistically by completing the DNN optimization-security loop. Securing Crypto Exchange Operations delivers scalable and efficient Cryptojacking protection, improving machine learning, cybersecurity, and network management.
由于 MEC 的广泛使用,比特币交易所的安全性至关重要。加密劫持破坏了 MEC 应用程序的安全性和比特币交易所生态系统的功能。本文提出了一种用于加密劫持预测和防御的前沿神经网络和 AdaHessian 优化技术。我们提供了一种前沿的深度神经网络(DNN)加密劫持攻击预测方法,该方法采用了剪枝、训练后量化和 AdaHessian 优化技术。为了解决这些问题,本文应用了剪枝、训练后量化和 AdaHessian 优化技术。利用 AdaHessian 优化的 DNN 快速训练新框架能以更低的计算成本检测出加密劫持企图。剪枝和训练后量化改进了低 CPU 边缘设备的模型。所提出的方法在不影响加密劫持攻击预测的情况下大幅降低了模型参数。该模型的 Recall 值为 98.72%,Precision 值为 98.91%,F1-Score 值为 99.09%,MSE 值为 0.0140,RMSE 值为 0.0137,MAE 值为 0.0139。我们的解决方案在精确度、计算效率和资源消耗方面都优于最先进的方法,从而可以建立更加真实、可信和经济高效的机器学习模型。我们通过完成 DNN 优化-安全循环,全面解决了日益严重的网络安全问题。Securing Crypto Exchange Operations 提供可扩展的高效加密劫持保护,改善机器学习、网络安全和网络管理。
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引用次数: 0
A secure data interaction method based on edge computing 基于边缘计算的安全数据交互方法
Pub Date : 2024-03-18 DOI: 10.1186/s13677-024-00617-9
Weiwei Miao, Yuanyi Xia, Rui Zhang, Xinjian Zhao, Qianmu Li, Tao Wang, Shunmei Meng
Deep learning achieves an outstanding success in the edge scene due to the appearance of lightweight neural network. However, a number of works show that these networks are vulnerable for adversarial examples, bringing security risks. The classical adversarial detection methods are used in white-box setting and show weak performances in black-box setting, like the edge scene. Inspired by the experimental results that different models give various predictions for the same adversarial example with a high probability, we propose a novel adversarial detection method called Ensemble-model Adversarial Detection Method (EADM). EADM defenses the prospective adversarial attack on edge devices by cloud monitoring, which deploys ensemble-model in the cloud and give the most possible label for each input copy received in the edge. The comparison experiment in the assumed edge scene with baseline methods demonstrates the effect of EADM, with a higher defense success rate and a lower false positive rate by an ensemble-model consisted of five pretrained models. The additional ablation experiment explores the influence of different model combinations and adversarial trained models. Besides, the possibility about transfering our method to other fields is discussed, showing the transferability of our method across domains.
由于轻量级神经网络的出现,深度学习在边缘场景中取得了巨大成功。然而,大量研究表明,这些网络容易受到对抗性实例的影响,从而带来安全风险。经典的对抗检测方法用于白盒环境,在边缘场景等黑盒环境中表现较弱。实验结果表明,不同模型对同一对抗性实例的预测结果大相径庭,受此启发,我们提出了一种名为 "集合模型对抗检测法(EADM)"的新型对抗检测方法。EADM 通过云监控来防御对边缘设备的潜在对抗性攻击,它在云中部署集合模型,并对边缘设备接收到的每个输入副本给出最可能的标签。在假设的边缘场景中与基线方法的对比实验证明了 EADM 的效果,由五个预训练模型组成的集合模型具有更高的防御成功率和更低的误报率。额外的消融实验探索了不同模型组合和对抗训练模型的影响。此外,我们还讨论了将我们的方法应用到其他领域的可能性,这表明我们的方法具有跨领域的可移植性。
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引用次数: 0
Privacy-preserving federated learning based on partial low-quality data 基于部分低质量数据的隐私保护联合学习
Pub Date : 2024-03-18 DOI: 10.1186/s13677-024-00618-8
Huiyong Wang, Qi Wang, Yong Ding, Shijie Tang, Yujue Wang
Traditional machine learning requires collecting data from participants for training, which may lead to malicious acquisition of privacy in participants’ data. Federated learning provides a method to protect participants’ data privacy by transferring the training process from a centralized server to terminal devices. However, the server may still obtain participants’ privacy through inference attacks and other methods. In addition, the data provided by participants varies in quality, and the excessive involvement of low-quality data in the training process can render the model unusable, which is an important issue in current mainstream federated learning. To address the aforementioned issues, this paper proposes a Privacy Preserving Federated Learning Scheme with Partial Low-Quality Data (PPFL-LQDP). It can achieve good training results while allowing participants to utilize partial low-quality data, thereby enhancing the privacy and security of the federated learning scheme. Specifically, we use a distributed Paillier cryptographic mechanism to protect the privacy and security of participants’ data during the Federated training process. Additionally, we construct composite evaluation values for the data held by participants to reduce the involvement of low-quality data, thereby minimizing the negative impact of such data on the model. Through experiments on the MNIST dataset, we demonstrate that this scheme can complete the model training of federated learning with the participation of partial low-quality data, while effectively protecting the security and privacy of participants’ data. Comparisons with related schemes also show that our scheme has good overall performance.
传统的机器学习需要收集参与者的数据进行训练,这可能会导致恶意获取参与者的数据隐私。联合学习提供了一种保护参与者数据隐私的方法,即把训练过程从集中服务器转移到终端设备上。不过,服务器仍有可能通过推理攻击和其他方法获取参与者的隐私。此外,参与者提供的数据质量参差不齐,低质量数据过多地参与训练过程会导致模型无法使用,这也是当前主流联合学习中存在的重要问题。针对上述问题,本文提出了一种具有部分低质量数据的隐私保护联合学习方案(PPFL-LQDP)。它既能取得良好的训练效果,又能允许参与者利用部分低质量数据,从而提高联合学习方案的隐私性和安全性。具体来说,我们使用分布式 Paillier 加密机制来保护联合训练过程中参与者数据的隐私和安全。此外,我们还为参与者持有的数据构建了复合评估值,以减少低质量数据的参与,从而将此类数据对模型的负面影响降至最低。通过在 MNIST 数据集上的实验,我们证明该方案可以在部分低质量数据参与的情况下完成联合学习的模型训练,同时有效保护参与者数据的安全和隐私。与相关方案的比较也表明,我们的方案具有良好的整体性能。
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引用次数: 0
期刊
Journal of Cloud Computing
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