The study aims to increase the functional efficiency of a machine learning cyber attack detection system. An information-extreme machine learning method of the cyberattack detection system with optimization of control tolerances for recognition features that reflect the traffic properties of the info-communication system has been developed. The method is developed within the framework of the functional approach to modeling of cognitive processes of natural intelligence at the formation and acceptance of classification decisions. This approach, in contrast to known methods of data mining, including neuron-like structures, allows giving the recognition system adaptability to arbitrary initial conditions of the learning matrix and flexibility in retraining the system by expanding the recognition classes alphabet. The method idea is to maximize the information capacity of the attack detection system in the machine learning process. A modified Kullback information measure is used as a criterion for optimizing machine learning parameters. According to the proposed categorical functional model, algorithmic software for attack detection system in the mode of machine learning with the depth of the second level has been developed and implemented. However, the depth level is determined by the number of machine learning parameters, which were optimized. The geometric parameters of the recognition hyperspherical containers classes and the control tolerances on the recognition features were considered as optimization parameters, which played the role of input data quantization levels in the transformation of the input Euclidean learning matrix of the type "object-property" into a working binary learning matrix given in the Hamming space. Admissible transformations of the working training matrix of the offered method allow adapting the input mathematical description of the attacks detection system to the maximum full probability of the correct classification decisions acceptance. Based on the results of information-extreme machine learning within the geometric approach, decisive rules are constructed as practically invariant to the multidimensionality of the recognition features space. The computer simulation results of information-extreme machine learning of the attack detection system to recognize four host traffic of different profiles confirm the developed method's efficiency.
The article considers the method of factor cluster analysis which allows automatically retrain the onboard recognition system of an unmanned aerial system. The task of informational synthesis of an on-board system for identifying frames is solved within the information-extreme intellectual technology of data analysis, based on maxi- mizing the informational ability of the system during machine learning. Based on the functional approach to modeling cognitive processes inherent to humans during forming and making classification decisions, it was proposed a categorical model in the form of a direct graph. According to this model, the algorithmic support of the information extreme factor cluster analysis is developed. It allows automatically retrain the system when expanding the alphabet of recognition classes. According to this algorithm, the on-board recognition system preliminarily carries out the information-extremal machine learning of recognition classes of relatively low power. When new classes appear, their unclassified structured recognition attribute vectors form additional learning matrixes. After reaching a representational volume, additional learning matrix joins the input learning matrix and the on-board recognition system is retrained. Forming additional learning matrixes of new recognition classes is carried out by the agglomerative algorithm of cluster analysis of unclassified vectors by k-means clustering. As a criterion of optimizing machine-learning parameters, we used the modified Kullback criterion which is a functional of the exact characteristics of classification solutions. To increase the functional efficiency of factor cluster analysis, it is proposed to increase the depth of machine learning by optimizing the parameters of image processing frames.
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