IMMUNOLOGICAL MODEL OF KEYBOARD MONITORING OF INFORMATION SYSTEM OPERATORS

Abstract

The purpose of this work is to develop a model of keyboard monitoring of information system operators, based on the use of a chain method of accounting keyboard handwriting parameters. The specified method provides estimation of operator's keyboard handwriting on chains of characters of given length, reflecting linguistically related parameters of keyboard set, characteristic for the given operator. The keyboard typing of such chains by the operator with "good" keyboard handwriting has significantly higher individuality due to correlation dependences between the time parameters of successive characters and pauses. As a result, the chain method allows to provide higher accuracy of operator's identity verification. Keyboard monitoring based on the chain method is proposed to be implemented in the basis of artificial immune systems using an immunological model of clonal selection, in which the detectors are represented by identifying parameters of the distribution area of the keyboard parameters of "friend". In the tasks of keyboard monitoring the area of distribution of keyboard parameters of the verified operator is always significantly less than the cumulative area of distribution of keyboard parameters of other possible operators. The choice of the specified model allows to significantly reduce the required volume of the detector population, and as a consequence - to significantly reduce the verification time of the working operator. The decision to replace "friend" operator with "stranger" is proposed to be considered reasonable when the frequency of operation of detectors exceeds the established threshold value. The proposed immunological model has a number of advantages. The use of the chain method of keyboard parameters accounting allows to verify the operator with greater accuracy in comparison with traditional methods. The clonal selection model in combination with vector representation of the keyboard data allows to significantly speed up the learning process and reduce the time required to make a timely decision on the presence of a "stranger" operator. An important advantage of the model is the ability to learn solely from the examples of keyboard handwriting operationally available "friend" operators. The use of the clonal selection model also makes it possible to significantly reduce the required volume of the population of detectors capable of effectively "covering" the distribution area of the keyboard parameters of "friend" operator