SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

Detection of Mechanical Deformation in Old Aged Power Transformer Using Cross Correlation Co-Efficient Analysis Method

Detection of minor faults in power transformer active part is essential because minor faults may develop and lead to major faults and finally irretrievable damages occur. Sweep Frequency Response Analysis (SFRA) is an effective low-voltage, off-line diagnostic tool used for finding out any possible winding displacement or mechanical deterioration inside the Transformer, due to large electromechanical forces occurring from the fault currents or due to Transformer transportation and relocation. In this method, the frequency response of a transformer is taken both at manufacturing industry and concern site. Then both the response is compared to predict the fault taken place in active part. But in old aged transformers, the primary reference response is unavailable. So Cross Correlation Co-Efficient (CCF) measurement technique can be a vital process for fault detection in these transformers. In this paper, theoretical background of SFRA technique has been elaborated and through several case studies, the effectiveness of CCF parameter for fault detection has been represented.

The power spectrum and correlation of flow noise for an axisymmetric body in water

Understanding the physical features of the flow noise for an axisymmetric body is important for improving the performance of a sonar mounted on an underwater platform. Analytical calculation and numerical analysis of the physical features of the flow noise for an axisymmetric body are presented and a simulation scheme for the noise correlation on the hydrophones is given. It is shown that the numerical values of the flow noise coincide well with the analytical values. The main physical features of flow noise are obtained. The flow noises of two different models are compared and a model with a rather optimal fore-body shape is given. The flow noise in horizontal symmetry profile of the axisymmetric body is non-uniform, but it is omni-directional and has little difference in the cross section of the body. The loss of noise diffraction has a great effect on the flow noise from boundary layer transition. Meanwhile, based on the simulation, the noise power level increases with velocity to approximately the fifth power at high frequencies, which is consistent with the experiment data reported in the literature. Furthermore, the flow noise received by the acoustic array has lower correlation at a designed central frequency, which is important for sonar system design.

An Improved Empirical Mode Decomposition for Power Analysis Attack

Correlation power analysis(CPA) has become a successful attack method about crypto-graphic hardware to recover the secret keys. However, the noise influence caused by the random process interrupts(RPIs) becomes an important factor of the power analysis attack efficiency, which will cost more traces or attack time. To address the issue, an improved method about empirical mode decomposition(EMD) was proposed. Instead of restructuring the decomposed signals of intrinsic mode functions(IMFs), we extract a certain intrinsic mode function(IMF) as new feature signal for CPA attack. Meantime, a new attack assessment is proposed to compare the attack effectiveness of different methods. The experiment shows that our method has more excellent performance on CPA than others. The first and the second IMF can be chosen as two optimal feature signals in CPA. In the new method, the signals of the first IMF increase peak visibility by 64% than those of the tradition EMD method in the situation of non-noise. On the condition of different noise interference, the orders of attack efficiencies are also same. With external noise interference, the attack effect of the first IMF based on noise with 15dB is the best.

Dynamic inhomogeneous S-Boxes in AES: a novel countermeasure against power analysis attacks

Substitution boxes (S-Boxes) in advanced encryption standard (AES) are vulnerable to attacks bypower analysis.The general S-Boxes masking schemes in circuit level need to adjust the design flow andlibrary databases.The masking strategies in algorithm level view each S-Box as an independent moduleand mask them respectively,which are costly in size and power for non-linear characteristic of S-Boxes.The new method uses dynamic inhomogeneous S-Boxes instead of traditional homogeneous S-Boxes,andarranges the S-Boxes randomly.So the power and data path delay of substitution unit become unpre-dictable.The experimental results demonstrate that this scheme takes advantages of the circuit character-istics of various S-Box implementations to eliminate the correlation between crypto operation and power.Itneeds less extra circuits and suits resource constrained applications.

Influences of Institutional Pressures on Corporate Social Performance： Empirical Analysis on the Panel Data of Chinese Power Generation Enterprises

Institutional theory has proved the influence of institutional pressures on organization practices and structures. Meanwhile, with the soaring use of corporate social performance （CSP）, more researchers are focusing on exploring the relationship between institution pressures and CSP which is still not completely understood yet. Against this background, the paper aims to fill the gap through generally hypothesizing that different types of institutional pressures individually and collectively affect CSP via the mediating effect of corporate environmental strategy. First, based on the previous and extensive literature review, the theoretical framework and research hypotheses are constructed. Next, canonical correlation analysis about the panel data of 51 Chinese large-scale power generation enterprises from 2004 to 2009 is made to test the relevant hypotheses. Finally, based on the data analysis results, the study draws some conclusions and policy implications for promoting the CSP of Chinese enterprises, including enhancing the steering function of government policies and industry regulations and emphasizing the intermediary role of media.

结合EDCA和CPA的容错双向选择攻击

当所设计的攻击方案带有容错功能时,往往需要在非常大的候选空间中挑出正确的密钥。如何有效地实现这个目标是侧信道攻击中非常重要且具有挑战性的问题。针对这一问题,以AES-128为目标研究了结合欧式距离增强碰撞攻击(EDCA)和相关能量分析攻击(CPA)的容错双向选择攻击。为了提高碰撞检测的成功率,提出了EDCA,与传统的相关增强碰撞攻击(CCA)相比,EDCA利用欧式距离来区分两组能量迹之间的相似性,其碰撞检测的成功率更高,从而使优化更加实用和有意义。除此之外,结合EDCA和CPA,将密钥以及对应的碰撞对做分组处理,然后进行双向筛选,得到最优的碰撞链,大大减少了候选空间,从而降低了密钥枚举的复杂性,有效地恢复密钥。实验结果表明,在低信噪比SNR=-3 dB和SNR=-6 dB的条件下,设置碰撞对的阈值ThΔ=5,所提出的方案在3000条能量迹时成功率达到98.78%和80.25%,均优于现有方案。

Nonlinear Dynamic Analysis of MPEG-4 Video Traffic

The main research motive is to analysis and to veiny the inherent nonlinear character of MPEG-4 video. The power spectral density estimation of the video trafiic describes its 1/f^β and periodic characteristics.The priraeipal compohems analysis of the reconstructed space dimension shows only several principal components can be the representation of all dimensions. The correlation dimension analysis proves its fractal characteristic. To accurately compute the largest Lyapunov exponent, the video traffic is divided into many parts.So the largest Lyapunov exponent spectrum is separately calculated using the small data sets method. The largest Lyapunov exponent spectrum shows there exists abundant nonlinear chaos in MPEG-4 video traffic. The conclusion can be made that MPEG-4 video traffic have complex nonlinear be havior and can be characterized by its power spectral density,principal components, correlation dimension and the largest Lyapunov exponent besides its common statistics.

科技强省评价指标体系构建与监测研究

科技强省建设评价监测是评估科技强省建设各方面目标实现程度的重要手段。通过面向湖北科技强省建设需求,确定评价监测目标和内容,在此基础上开展评价监测指标体系研究。实证分析表明,湖北在科技强省建设五个要素方面表现较为平衡,但与先进地区相比有较大差距,要建成科技强省,还需要在各方面进行全面提升。还分析了湖北推进科技强省建设的优势以及关键制约因素,通过评价和持续监测分析,能提高科技主管部门制定科技政策和发展战略的科学性,对提高区域科技创新治理能力和治理水平起到重要支撑作用。

基于注意力特征融合时空图网络的超短期风电功率预测

为提高风电功率的预测精度,综合考虑时间和空间多维度因素的影响,提出了一种基于注意力机制和多阶段特征融合的时空图神经网络(Spatio-temporal graph neural network with attention mechanism and multistage feature fusion,AMF-STGNN)的超短期风电功率预测方法。首先基于Pearson相关系数法对数据特征进行降维,确定影响风电功率的关键因素。然后构建AMF-STGNN预测模型。该模型主要由时空关联网络构建模块和多维度时空特征抽取模块组成。通过时空关联网络构建模块构建时空图,以揭示风电气象因素的空间连接关系。通过多维度时空特征抽取模块应用时间卷积和图卷积挖掘数据的时空特征,并利用注意力机制学习重要特征。此外,该方法还引入残差结构和多阶段特征融合结构提高模型的表达能力。最后以宁夏3个风电场真实数据为例,验证了所提方法在提升风电功率预测精度方面的有效性。

基于PSD特征的FBCCA脑电信号识别方法

当前基于稳态视觉诱发电位(steady-state visual evoked potential,SSVEP)的脑机接口(brain-computer interfaces,BCIs)使用的都是单一识别算法,针对不同时间长度的识别准确率较低。提出了一种基于滤波器组的典型相关分析(filter bank canonical correlation analysis,FBCCA)与功率谱密度(power spectral density,PSD)分析相结合的SSVEP识别算法,可以提高SSVEP识别的普适性与准确率。该方法使用FBCCA寻找高相似度的参考频率信号,再通过多组PSD分析来锁定最终的响应频率,完成频率识别。该方法无需经过训练就能得到较高的识别准确率。实验结果表明:在刺激时长为1 s时,该方法能达到86.61%的准确率,比PSD分析方法提升了5.44%,比典型相关性分析方法(canonical correlation analysis,CCA)提升了10.38%的准确率,比FBCCA提升了8.86%的准确率。

基于自适应优化AP聚类与BP加权网络的多区域复合短期风电功率预测

精准的风电集群区域功率预测对电源侧的竞价上网具有重要意义。由于同一地区多个风电场受气候影响波动程度相近,可看作具有时空相关性的风电场群,并以此进行集群的合理划分。为此,提出一种基于自适应优化近邻传播(AP)聚类与反向传播(BP)加权神经网络的多区域复合短期风电功率预测模型。首先,通过粒子群优化算法(PSO)优化AP聚类方法对风电场群的历史数据进行集群的聚类与划分;然后,根据得到的最优聚类结果构建风电场群子区域样本训练集;最后,利用基于相关系数权重的BP神经网络对各子区域进行功率预测。算例结果表明:所提方法在24 h日前预测相较传统叠加法与单一BP神经网络可提高1.35%和2.62%的精度,可表明该模型具有优越的预测性能。

基于相关性分析和生成对抗网络的电网缺失数据填补方法

城市电网新型电力系统中多元资源增多,数据采集难度加大,导致数据随机缺失率升高,难以满足精细化分析决策需求。为解决新型电力系统中配网量测数据在采集与传输过程中频发的缺失问题,文中提出一种基于波动互相关分析(fluctuation cross-correlation analysis,FCCA)算法和生成对抗网络(generative adversarial network,GAN)的电网缺失数据填补方法。首先,融合FCCA算法提出强相关性电网数据多维特征提取方法;其次,基于核主成分分析(kernel principal component analysis,KPCA)对多维特征数据集进行降维处理;最后,设计改进型GAN结构,融合电网数据多维特征对低维向量进行重构,实现缺失数据填补。算例采用真实电网数据进行算法验证,并在某城市电网试运行。结果表明,所提方法比传统数据填补方法具有更高填补精度。因此,在新型电力系统中量测数据连续缺失和缺失量较大的情况下,融合强相关性特征进行数据填补,对提升量测数据的完整性和可用性有明显优势。

基于CEEMD-BiLSTM-RFR的短期光伏功率预测

由于短期光伏预测中气象因素的时间尺度不同,直接分析其对光伏功率的相关性,易忽略时间尺度的影响,进而导致预测模型误差。为提高光伏功率预测精度,构建了预测模型。首先,利用互补集合经验模态分解(complementary empirical mode decomposition,CEEMD)将光伏序列进行分解,得到在不同时间尺度上的光伏分量;然后,通过Pearson相关系数分析各光伏分量与空气温度、太阳辐射度、风速、风向和空气湿度的关系,对于强相关分量建立关于气象因素的随机森林回归(random forest regression,RFR)预测模型,弱相关分量直接通过双向长短期记忆网络(bidirectional long short-term memory neural network,BiLSTM)进行预测;并将预测求和输出。通过安徽省蚌埠市光伏电站7月实测数据进行验证,实验结果表明,所提预测模型CEEMD-BiLSTM-RFR相比传统预测模型有较好的预测精度。

对同步流密码设备的相关性功耗分析(CPA)攻击

利用同步流密码的再同步弱点,提出了用相关性功耗分析(CPA)攻击同步流密码的技术,该技术采用统计学中的相关系数来分析密钥并实施攻击。概括了CPA技术的攻击过程,给出了CPA攻击两个同步流密码A5/1和E0的步骤和实验。实验证明,用CPA攻击同步流密码是可行的。最后给出了有效的防御对策来抵抗这种攻击。

针对AES密码芯片的CPA攻击点选择研究

为解决AES密码芯片的相关性能量攻击(CPA)的攻击点选择问题,提出了一种CPA攻击点的选择方法,搭建了验证CPA攻击点有效性的仿真平台,并针对AES密码芯片进行了选择分析和验证实验。针对AES密码芯片提出了两种CPA攻击点选择——异或(XOR)操作攻击点和S盒(Sbox)操作攻击点。验证结果表明,两种选择都有效,其中后者的效果更好。

针对DES密码芯片的CPA攻击仿真

为研究密码芯片抗功耗分析性能,构造了一个功耗分析研究平台,结合DES算法在平台上进行了相关性功耗分析(CPA)攻击仿真实验。根据猜测部分密钥时的模拟功耗与猜测整个密钥时模拟功耗之间的相关系数大小来确定猜测密钥的正确性,由此可以确定整个密钥。这种功耗分析仿真方法,能够揭示未经防御的DES算法面临CPA攻击时的脆弱性。

针对ZUC算法硬件实现的相关能量攻击与多粒度时域敏感区域检测

本文通过合理分解模加操作,优化流水线结构,构建ZUC算法硬件实现,并基于FPGA开发板搭建ZUC算法功耗采集平台.构建粗细粒度时域敏感区域的泄漏检测方法,利用移动平均算子,发掘高信噪比风险区域,采用自相关滤波器与峰值检测算子,发掘具有明显重复特征的运算区间,并对泄漏高风险区域进行标定,实现高精度的泄漏风险定位.通过分析ZUC算法的脆弱性,本文提出一种基于选择初始向量IV的CPA攻击方案,对输入的IV信息进行选择,通过固定部分明文的方法有效提高采集信号的信噪比,并采集实际3000条功耗曲线,在算法初始化首轮,成功提取8 bit密钥信息,从而证明该方案的有效性,对ZUC算法的硬件实现进行侧信道防护是必要的.总结ZUC算法侧信道防护方案,对比方案优劣.

基于GRU-贝叶斯的分布式光伏功率异常检测方法

为有效识别分布式光伏故障系统,提出一种基于GRU-贝叶斯神经网络的分布式光伏功率异常检测方法。首先,选取晴天为检测场景,降低天气因素的干扰;然后,引入灰色绝对关联度算法,利用同地区光伏系统出力的相似性,筛除不合格光伏出力数据,构建光伏用户正常的光伏出力数据集。使用GRU-贝叶斯神经网络训练得到用户正常的光伏功率区间再进行检测。最后,用实际数据进行算例分析,表明所提方法的可行性和有效性。

大型机场航站楼逐时电耗特性及电耗强度

以北京市某大型机场航站楼为研究对象,应用皮尔逊相关分析、k均值聚类方法,对2020—2023年实测数据变化特性及其影响因素进行了分析研究,包括逐时电耗特性提取及其电耗强度指标构建。结果表明:航站楼电耗可分为3类,即与建筑规模相关的基础电耗量、与旅客吞吐量相关的变动电耗量Ⅰ,以及与室外气象条件和旅客吞吐量相关的变动电耗量Ⅱ;k均值聚类分析结果反映了部分设计客流负荷对航站楼主要用电设备系统运行方式及其电耗特性的影响规律,可将日旅客吞吐量分为4级。基于这些分析结果,进一步给出了航站楼日总电耗强度及CO_(2)排放指标的试算值。研究结果可为合理确定各分项用电设备系统的电耗强度降低目标提供参考。