传感器网络中抗数据伪造的社会学习

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摘要翻译：
虽然监控和传感器网络在物联网中起着关键作用，但由于传感器节点分布广泛，往往容易受到篡改。在这封信中，我们考虑数据伪造攻击，一个聪明的攻击者控制网络中的关键节点，包括作为融合中心的节点。为了解决这一关键的安全问题，我们提出了一种基于社会学习的数据聚合方案，类似于社会网络中Agent的决策方式。我们的结果表明，即使攻击者已经破坏了很大一部分节点，社会学习也能使网络恢复力增强。最后，我们通过在计算能力受限的设备中开发一个简化社会学习数据融合规则的低复杂度算法来说明我们的方案对传感器网络的适用性。
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英文标题：
《Social Learning Against Data Falsification in Sensor Networks》
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作者：
Fernando Rosas and Kwang-Cheng Chen
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最新提交年份：
2017
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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：
  Although surveillance and sensor networks play a key role in Internet of Things, sensor nodes are usually vulnerable to tampering due to their widespread locations. In this letter we consider data falsification attacks where an smart attacker takes control of critical nodes within the network, including nodes serving as fusion centers. In order to face this critical security thread, we propose a data aggregation scheme based on social learning, resembling the way in which agents make decisions in social networks. Our results suggest that social learning enables network resilience, even when a significant portion of the nodes have been compromised by the attacker. Finally, we show the suitability of our scheme to sensor networks by developing a low-complexity algorithm to facilitate the social learning data fusion rule in devices with restricted computational power.
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PDF链接：
https://arxiv.org/pdf/1710.06531

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关键词：传感器 社会学 Applications Optimization Surveillance data 受到 攻击者 网络 篡改