发帖
雷达卡
摘要翻译：
物联网（IoT）的传感器节点易受攻击，在物理或网络捕获后，这些节点可能会遭受数据伪造攻击。而且，这些网络常用的集中式决策和数据融合方案将这些决策点变成了单点故障，很可能被聪明的攻击者利用。为了应对这一严重的安全问题，我们提出了一种新的方案，通过遵循社会学习原则，实现分布式数据聚合和决策。我们提出的方案使传感器节点的行为类似于社会网络中代理的行为方式。我们分析研究了个体代理的局部行为如何在整个网络中传播，从而影响集体行为。最后，我们展示了社会学习如何使网络能够抵御数据伪造攻击，即使很大一部分节点已经被敌手破坏。
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英文标题：
《Social learning for resilient data fusion against data falsification
attacks》
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作者：
Fernando Rosas, Kwang-Cheng Chen and Deniz Gunduz
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
Internet of Things (IoT) suffers from vulnerable sensor nodes, which are likely to endure data falsification attacks following physical or cyber capture. Moreover, centralized decision-making and data fusion schemes commonly used by these networks turn these decision points into single points of failure, which are likely to be exploited by smart attackers. In order to face this serious security thread, we propose a novel scheme that enables distributed data aggregation and decision-making by following social learning principles. Our proposed scheme makes sensor nodes to act resembling the manners of agents within a social network. We analytically examine how local actions of individual agents can propagate through the whole network, affecting the collective behaviour. Finally, we show how social learning can enable network resilience against data falsification attacks, even when a significant portion of the nodes have been compromised by the adversary.
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PDF链接：
https://arxiv.org/pdf/1804.00356
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