The paper presents the main key features of social engineering and a social engineer activity. Emphasis is placed on the study of social engineering techniques in the system of human-machine interaction used to implement the illegal (malicious) manipulation of human behavior patterns. The matrix of social engineering qualification criteria and the map of information security risks caused by social engineer actions were built. IntroductionThe practice of human-machine interaction (HMI) makes increasingly high demands on the level of information security. This process is like an eternal dispute -what is stronger, a sword or a shield. The evolution of information protection methods reflects the evolution of unauthorized data access methods [1]. Nowadays people are actively embedding computers in their environment; they are trying to make the computer more "human", the level of computer dependence on the outside world also increases with the development of networks [2,3]. On the other hand, the human factor continues to be the least controlled element of HMI [4], which, in turn, according to the modern information and communication technologies development creates not only new opportunities, but also new risks [5]. As a result, the number of information security [6] vulnerability factors increases. Someday, a computer will learn how to evaluate human behavior [7,8] and make decisions based on the results of cognitive processes analysis, considering the value and semantic aspects of personality behavior. Consequently, we are waiting for a new evolutionary leap in the confrontation of "sword and shield". But, so long as a person is only understandable by the other person, the social engineering knowledge will be in demand [9]. qualitative terms of "probability" and "impact" on the risk object dispersed in an ascending order.
The article considers the ethics of machine learning in connection with such categories of social philosophy as justice, conviction, value. The ethics of machine learning is presented as a special case of a mathematical model of a dilemma -whether it corresponds to the "learning" algorithm of the intuition of justice or not. It has been established that the use of machine learning for decision making has a prospect only within the limits of the intuition of justice field based on fair algorithms. It is proposed to determine the effectiveness of the decision, considering the ethical component and given ethical restrictions. The cyclical nature of the relationship between the algorithmic algorithms subprocesses in machine learning and the stages of conducting mining analysis projects using the CRISP methodology has been established. The value of ethical constraints for each of the algorithmic processes has been determined. The provisions of the Theory of System Restriction are applied to find a way to measure the effect of ethical restrictions on the "learning" algorithm SHS Web of Conferences 69, 00150 (2019)
The article describes the process of developing a model, the implementation of which will change the process of the work of the company’s specialists in selecting a partner offer to the client. A practical request for the development of the model was the fact that a huge amount of available information about the client, which affects decision-making to varying degrees, complicates its processing and increases the risks of making a biased decision. The theoretical significance of the research results lies in the presentation of a tool for making the right decision with a high degree of probability. We proceeded from the practice of implementing a business process, according to which the most time-consuming and risky stage of the selection process is the stage of forming the initial data sample. And they suggested using machine learning in order to simplify it significantly. The process of developing the model is presented in stages in this paper so that it can be reproduced and verified. The practical significance of this work is that the results obtained can be applied in the entire range of marketing services and can be used in companies working with large amounts of customer data.
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