The article discusses some aspects of the design of a decision support system (DSS) module during the analysis of major accidents or emergencies in urban transport in large cities, megalopolises, as well as in Smart City. It is shown that the computational core of such a DSS can be based on the methods of cluster analysis (CA). It is shown that the implementation of even basic spacecraft algorithms in the computational core of the DSSS allows an iterative search for optimal solutions to prevent a large number of emergencies in urban transport by establishing characteristic signs of accidents and emergencies and measures of proximity between two objects. It is shown that such a toolkit as DSS can provide all interested parties with a scientifically grounded classification of multidimensional observations, which summarize the set of selected indicators and make it possible to identify internal connections between emergencies in urban transport. The DSS module for analyzing emergencies in urban transport is described. It has been found that to solve such a problem, it is possible to use the "weighted" Euclidean distance in the computational core of the DSS. It is this parameter that makes it possible to take into account the significance of each characteristic of emergency situations in urban transport, which, in turn, will contribute to obtaining reliable analysis results. It is shown that the spacecraft methods can also be in demand when, along with the analysis of emergency situations in urban transport, problems of designing and reconstructing the configurations of urban street-road networks are solved in parallel. This task, in particular, requires an analysis phase (not least using CA methods) in order to minimize unnecessary uncompensated costs in the event of errors in the road network. When solving such a problem, sections of the urban street and road network are analyzed in order to identify problem areas that need reconstruction or redevelopment. The use of CA methods in such conjugate problems is due to the absence of a priori hypotheses regarding the classes that will be obtained as a result.
Rapid growth of information technologies causes the changes in many production processes. The effectiveness of elec-trotechnical means of infrared radiation is due to the application of new, scientifically grounded methods and means of energy supply control using controllers and personal computers, new information technologies and systems.Therefore, there is a need for further mathematical models development as well as information technology used in deci-sion support systems on infrared grain drying from elevators and granaries.Research and publication analysis on information support questions of grain drying based on infrared radiation technol-ogy has shown that the issues of information support systems are currently underexplored in scientific literature.The goal of this research is mathematical modeling of the IR grain drying process and information support development of the drying process, taking into account the obtained mathematical model.The improved model of the infrared drying process for grain crops is proposed, which, in contrast to the existing ones, is focused on application in decision support systems when organizing the drying process. The results of the experiments proved that infrared radiation use contributes to the intensification of the grain drying process due to a significant heat flux rate increasing on the material’s irradiated surface and the of infrared rays’ penetration into material. It has been proved that modern information technology, rational schemes and operating parameters of infrared grain drying processes utilization will reduce dehydration time with "gentle" drying modes to ensure the required quality parameters of the product.In this work it was first proposed to abandon probabilistic models of grain flow behavior under IR irradiation. A new mathematical model has been proposed based on information about the characteristics of the heat flow, dehydration and grain mass’ condition. This will significantly improve the IR grain drying process and get a higher quality product
The paper proposes an algorithm with self-learning elements for intrusion detection systems, as well as an improved clustering technique which is recorded by the data system concerning information security events. The proposed approaches differ from those known using an entropy approach allowing data to be presented as homogeneous groups, moreover, each such group (or cluster) may correspond to predetermined parameters. The proposed solutions relate to the possibilities of assessing dynamic dependencies between clusters characterizing the analysed classes of invasions. The studies have found that in case of manifestation of new signs of information security events, the corresponding scale changes and describes the distances between clusters. A computational experiment was conducted to verify the operability and adequacy of the proposed solutions. During the computational experiment, it has been found that step-by-step calculation of parameters of informative characteristics of network attacks allows to form sufficiently informative cluster structures of data having characteristic attributes. These attributes further become the basis for the knowledge base of intelligent network attack detection systems. Dynamic dependencies between clusters are calculated allowing for a sufficiently accurate definition of the many information security events that can become the source data for further automatic assessment of current threats extent detected by attack detection systems. The methodology and algorithm presented in the paper for clustering the signs of network attacks, in our opinion it is simpler for software implementation than existing analogues.
The development of computer networks is gaining momentum. There are new challenges to data security and the end users themselves. With the advent of the Internet of Things, this problem has become quite acute for network engineers and cyber analysts. Increasingly, there are illegal actions to interfere with the work of the network itself and the use of users' devices for criminal purposes. Various distributed attacks, SQL injections and identity theft are becoming more complex. Given the growing infrastructure of both the network and IoT devices, there is a need to protect them. Especially when it comes to the computer network of a higher education institution. Where little attention is usually paid to full infrastructure protection, and with the integration of IoT devices, such possible gaps can occur quite a lot. This article attempts to reveal theoretical approaches to the design and implementation of a computer network of higher education institutions, which in recent years are increasingly beginning to suffer from outside interference. Possible attacks on the infrastructure of higher education institutions are analyzed, as well as the possibility of attack and interference in the work of IoT devices based on the killer chain approach. Internet The possibility of using a web application firewall and appropriate software for security and incident management at the L5-L7 OSI level is considered in such networks. Preliminary testing of the network for the ability to respond to L3-L4 level attacks using standard firewall capabilities. And with the response to interventions at the upper levels of the OSI L5-L7 model, namely: SQL injections, distributed DDoS, bot-net attacks. The results are summarized and further directions of research are determined, which are based on the improvement of the group security policy for the higher education institution. Development of security infrastructure for IoT devices and the ability to respond quickly to non-standard attacks.
It is shown that the application of multi-step quality games theory allows financing of various information technologies considering various factors. In particular, there are lots of approaches to building effective information security systems in the enterprise. Using such model will make it possible to develop, based on game models, decision support systems (DSS), for example, software products (PP). Which, in turn, will allow making rational decisions on investing in the development of such technologies. This circumstance makes it necessary and relevant to develop new models and software products that can implement decision support procedures in the process of finding rational investment strategies, including in information security field of enterprises, and obtaining forecast assessment for feasibility of a specific strategy. The model proposed by us is based on analysis of financing process by investors in information technology for protecting information tasks for the case of their multi-factoring in fuzzy setting. The investment process management model is proposed, using the example of investing in the information security of informatization objects taking into account multi-factoring and in fuzzy setting for DSS computational core. The difference between the model and previously developed ones is that it considers the investment process as complex structure, for which it is not enough to model it as a single-factor category. Computational experiments were performed for the developed model. The simulation results are visualized in the Python programming language, which allows you to optimize the procedures for investment process managing.
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