To solve security problems in cross-technology communication(CTC),we take the Internet of Things(IoT)control system as an example,and propose a comprehensive solution against the attack on the physical layer CTC from ...To solve security problems in cross-technology communication(CTC),we take the Internet of Things(IoT)control system as an example,and propose a comprehensive solution against the attack on the physical layer CTC from ZigBee to Wi-Fi.Specifically,we propose a noise interference strategy by adding an appropriate amount of dedicated noise signals,which can interfere with the eavesdropping and simulation of ZigBee signals without affecting the reception of the receiver.Moreover,we also propose a regression modeling strategy which collects data,extracts features,and trains a binary logistic regression model so that the receiver can actively distinguish simulated attack signals.We build the experimental platform by using GNU Radio and USRP devices.Experimental results demonstrate that the security defense strategies can identify and distinguish the signals from the attacker with a high accuracy,effectively solving the signal emulation attack on the physical layer CTC from ZigBee to Wi-Fi.展开更多
文摘董志塬地区位于黄土高原中心地带,滑坡灾害频发,亟需明确滑坡易发性分区,以支持该区域滑坡隐患的科学防控。因此,本文以董志塬为研究区,选取高程、坡向和NDVI等12个影响因素作为评价因子,基于频率比(frequency ratio,FR)模型,结合随机森林(random forest,RF)与人工神经网络(artificial neural network,ANN)模型开展滑坡静态易发性评价,并分析各因子对评价精度的贡献。结果表明,FRRF和FR-ANN模型的曲线下面积(area under the curve,AUC)值分别为0.922和0.918,表明FR-RF模型在董志塬滑坡易发性评价中的精度更高。坡度、坡向和道路密度对滑坡易发性的贡献率分别为16.7%、15.3%和1.4%。为克服地形复杂和数据更新滞后的问题,本文将FR-RF模型的易发性结果与InSAR Stacking结果相结合,将静态滑坡易发性评价精度由6.9%提升到8.1%。动态易发性结果表明,董志塬滑坡高易发区主要分布于河流沿岸,占总面积的6.5%,该区域的滑坡数量占总滑坡数的23.6%,滑坡密度15.7个/km^(2)。低易发区主要位于远离河流的中部区域,占总面积的81.7%,滑坡数量占总滑坡数的57.8%,滑坡密度4.7个/km^(2)。本研究通过融合InSAR Stacking方法,解决了静态滑坡易发性评价数据更新滞后问题,减少了假阴性错误,为传统滑坡易发性评价赋予了时效性,可以实现董志塬滑坡易发性动态评价,为灾害防治提供了重要数据支持。
文摘To solve security problems in cross-technology communication(CTC),we take the Internet of Things(IoT)control system as an example,and propose a comprehensive solution against the attack on the physical layer CTC from ZigBee to Wi-Fi.Specifically,we propose a noise interference strategy by adding an appropriate amount of dedicated noise signals,which can interfere with the eavesdropping and simulation of ZigBee signals without affecting the reception of the receiver.Moreover,we also propose a regression modeling strategy which collects data,extracts features,and trains a binary logistic regression model so that the receiver can actively distinguish simulated attack signals.We build the experimental platform by using GNU Radio and USRP devices.Experimental results demonstrate that the security defense strategies can identify and distinguish the signals from the attacker with a high accuracy,effectively solving the signal emulation attack on the physical layer CTC from ZigBee to Wi-Fi.
