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of a given attack, are useful for detecting events of the attack Such data variables are identi ed for each of the eleven attacks in s 8 11 A feature is a easure of a property which exists in a sample of data observations for one or multiple data variables Only univariate mathematical/statistical features features of a data sample from one data variable are investigated in Part III These univariate mathematical/ statistical features include the statistical mean in 8, the probability distribution in 9, the autocorrelation in 10, and the wavelet-based signal strength in 11 covering the Haar wavelet, the Daubechies wavelet, the Derivative of Gaussian wavelet, the Paul wavelet and the Morlet wavelet These wavelets are used to extract the time-frequency signal changes associated with the data patterns of step change, steady change, random change, spike change and sine-cosine wave with noise s 8 11 provide mathematical/statistical methods of extracting the mean, probability distribution, autocorrelation, and wavelet features from attack and normal use data Among the four features, the distribution feature gives a more comprehensive picture of a data sample than the mean feature Both the wavelet feature and the autocorrelation feature reveal relations of data observations over time The autocorrelation feature focuses on the general autocorrelation aspect of time series data, whereas the wavelet feature focuses on special forms of time-frequency data patterns Both various wavelet forms and various probability distributions are linked to certain data patterns The distribution feature describes the general pattern of the data, whereas the wavelet feature reveals time locations and frequencies of special data patterns Hence, the wavelet feature reveals more special data features than the distribution feature and the autocorrelation feature Note that there are other types of univariate features (eg, features extracting other trends or patterns of data) as well as multivariate features (ie, features of data from multiple data variables) which are not investigated in Part III but may be useful in revealing data characteristics of various attacks If one or more events of a given attack cause a signi cant change in a speci c feature of a data variable, this change is considered a data characteristic of the attack s 8 11 describe statistical tests to identify a signi cant change in a given data feature, and reveal data characteristics of eleven attacks in the mean, probability distribution, autocorrelation, and wavelet features If a speci c data characteristic appears during a given attack but not during other attacks or normal use activities, this data characteristic is considered a unique data characteristic of that attack and can be used to uniquely detect and identify this attack Note that an event may manifest through more than one data characteristic (eg, more than one data variable or more than one feature of a data variable) The identi ed attack characteristics in the mean, distribution, autocorrelation and wavelet features are used to uncover the similarity and difference of the attacks The data characteristics of attack and normal use activities discovered in Part III are essential to building attack detection models for detection accuracy and earliness Attack detection models are covered in Parts IV VI.

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First, it eliminates a problem associated with applying the same mask or cell type twice internally The problem is that failures (designs, process, and wearout) undetected by the comparators could occur in noncomplementary duplication where a mask or cell fault might materialize in both the functional and duplicate circuits and thus create an identical failure state Second, the design with complementary duplication will be much less susceptible to bridging faults than noncomplementary duplication This is because there will be far fewer occurrences of long nets of metalization with the same Boolean function and the same polarity signal, which, if bridged, might result in an undetected error should a subsequent failure occur Each of the code checkers and comparators is implemented using a self-checking design approach The self-checking comparators shown in Figures 1224 and 1225 are extensively used.

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