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time as simulated in Run 2 and Run 3 of the data collection As a result, the data containing the effect of the attack also contains the effect of the normal use activity In other words, the attack and the normal use activity produce the mixed data effect in the data collected from the computer and network system The ANNs are trained using both the attack data and the normal use data but not the mixture of both which is included in the testing data The mixture of the attack data and the normal use data can distort the attack signature patterns and the normal use signature patterns, which creates the dif culty for the ANNs in recognizing in the mixed attack and norm data the attack and normal use patterns that the ANNs learn from the attack data and the normal use data during the training One option to improve the detection performance of the ANNs is to add the mixed attack and normal use data to the training data To capture possibly the more complicated input-output function presented in the mixed attack and normal use data, ANNs with two hidden layers may be necessary The cuscore models, which are described in more details in s 16 and 17, provide another means of handling the mixed attack and normal use data by rst separating the effects of the attack data and the normal use data As shown in Tables 132 134, the cuscore-based attack norm separation models produce the better performance not only in the detection accuracy measured by the false alarms but also in detection earliness measured by the rst hits when handling the mixed attack and normal use data The worse performance of the ANNs in the false alarms cannot be attributed to the lack of handling the mixed attack and normal use data because only the normal use data is involved in producing the false alarms Note that most data variables in Table 131 manifest the attack characteristics through advanced data features such as the autocorrelation, probability distribution, and wavelet features, rather than the mean feature which works directly on the raw data values of the data variables The ANNs rely on the raw data values to learn the differences between the attacks and the normal use activities Hence, for most data variables with attack characteristics with more subtle data features than the mean feature, it is dif cult for the ANNs to accurately classify the attacks and normal use activities based on the raw data values of those variables The cuscore models are built on both subtle data features and the mean feature, and use the attack and norm data models which accurately represent the data features and the attack and normal use differences in those data features (see details in s 16 and 17) As a result, when the ANNs fail to accurately classify the attacks and the normal use activities based on the raw data values of those data variables which have the attack characteristics in subtle data features, the cuscore models are capable of performing well Hence, the feature extraction is an important step in building cyber attack detection models for detection accuracy (eg, reducing false alarms) and earliness by rst uncovering attack characteristics through various data features and then building accurate detection models based on those attack characteristics.

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Peaks are formed from the current individuals of the population, by sequentially conr sidering each as a tness peak If an individual is n nK from already de ned tness x peaks, then that individual is considered as the next peak This process continues until nK peaks have been found or until all individuals have been considered Recombination considers a uniform distribution of resources Each peak is allowed = n parents, and produces = n o spring in every generation For each K K o spring are created as follows: One of the parents is selected from the peak, individuals of that niche using tournament selection The other parent, which should di er from the rst parent, is selected as the best individual of that niche If a niche contains only one individual, the second parent will be the best individual from another niche Intermediate recombination is used for the strategy parameters, and discrete recombination for the genotypes The selection operator selects individuals for the next generation by selecting n of the best o spring, along with the best n parents from that niche If there are not enough parents to make up a total of individuals, the remainder is lled up by generating random individuals New niches are generated at each generation Repeated application of the above process results in niches, where each niche represents a di erent solution Shir and B ck [785] expanded the approach above to a (1, )-ES with corrrelated a mutations

The feedforward ANNs with the back-propagation learning algorithm, which are commonly employed for classi cation problems, are used as a signature recognition technique for cyber attack detection Although only the attack signature patterns are needed to recognize the attacks, the ANNs learn the signature patterns of both the attack data and the normal use data in order to distinguish them in the training The ANNs then use such signature patterns to classify the computer and network data into attack or normal use in the testing The detection performance of the ANNs is worse than that of the cuscore-based attack norm separation models, likely due

Table 121 Applications of Evolution Strategies Application Class Parameter optimization Controller design Induction motor design Neural network training Transformer design Computer security Power systems References [256, 443, 548, 679] [59, 456, 646] [464] [59, 550] [952] [52] [204]

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