Ab s t r a c t. The subject matter of the article is the process of increased the information security of recommendation systems. The goal of this work is to develop a method of identification bot profiles in recommendation systems. In this work, the basic models of information attacks by the profile-injection method on recommendation systems were researched, the method of identification bot profiles in recommendation systems using the multilayer feedforward neural network was developed and the experiments to test the quality of its work were conducted. The developed method is to identify bot profiles that attempt to change item ratings in a recommendation system in order to increase the occurrence frequency of target items in recommendation lists to all authentic users, or to certain segments of authentic users. When removing bot profiles' data from the database of the recommendation system before generating recommendation lists, the accuracy of the system and the correctness of recommendations are significantly increased, and authentic users get protection from information attacks. Random, Average and Popular attacks were used to model the attacks on a recommendation system. To identify bots, their ratings for system items were analyzed. The experiments have shown that the neural network that analyzes only the numbers of different ratings in a profile, detects bot profiles with high accuracy, that use Random attack regardless of the number of target items for each bot. At the same time, the developed neural network can detect bots that use Average or Popular attacks only when they have several target items. Also, the results of the experiments show that type I errors, when the system identifies authentic users as bots, is very rarely appear in the developed method. To improve the accuracy of the neural network, there can add to analysis also other data of user profiles, such as the timestamp of each rating and as segments of items, which was rated.
The recommendation systems used to form a news feed in social networks or to create recommendation lists on content websites or Internet stores are often exposed to information profile injection attacks. These attacks are aimed at changing ratings, and thus at changing the frequency of appearing in recommendations, certain objects of a system. This can lead to threats to users' information security and losses of the system owners. There are methods to detect attacks in recommendation systems, but they require permanent repetitive checks of all users' profiles, which is a rather resource-intensive operation. At the same time, these methods do not contain any proposals as for determining the optimal frequency of attack checks. However, a properly chosen frequency of such checks will not overload a system too much and, at the same time, will provide an adequate level of its operational security. A mathematical model of the dynamics of states of a recommendation system under conditions of an information attack with the use of the mathematical apparatus of Markovian and semi-Markovian processes was developed. The developed model makes it possible to study the influence of profile injection attacks on recommendation systems, in particular, on their operation efficiency and amount of costs to ensure their information security. The practical application of the developed model enables calculating for recommendation systems the optimum frequency of information attack check, taking into consideration the damage from such attacks and costs of permanent inspections. Based on the developed mathematical model, the method for determining total costs of a recommendation system as a result of monitoring its own information security, neutralization of bot-networks activity and as a result of information attacks was proposed. A method for determining the optimal frequency of checking a recommendation system for information attacks to optimize the overall costs of a system was developed. The application of this method will enable the owners of websites with recommendation systems to minimize their financial costs to provide their information security
Computer vision and image processing techniques have been extensively used in various fields and a wide range of applications, as well as recently in surface treatment to determine the quality of metal processing. Accordingly, digital image evaluation and processing are carried out to perform image segmentation, identification, and classification to ensure the quality of metal surfaces. In this work, a novel method is developed to effectively determine the quality of metal surface processing using computer vision techniques in real time, according to the average size of irregularities and caverns of captured metal surface images. The presented literature review focuses on classifying images into treated and untreated areas. The high computation burden to process a given image frame makes it unsuitable for real-time system applications. In addition, the considered current methods do not provide a quantitative assessment of the properties of the treated surfaces. The markup, processed, and untreated surfaces are explored based on the entropy criterion of information showing the randomness disorder of an already treated surface. However, the absence of an explicit indication of the magnitude of the irregularities carries a dependence on the lighting conditions, not allowing to explicitly specify such characteristics in the system. Moreover, due to the requirement of the mandatory use of specific area data, regarding the size of the cavities, the work is challenging in evaluating the average frequency of these cavities. Therefore, an algorithm is developed for finding the period of determining the quality of metal surface treatment, taking into account the porous matrix, and the complexities of calculating the surface tensor. Experimentally, the results of this work make it possible to effectively evaluate the quality of the treated surface, according to the criterion of the size of the resulting irregularities, with a frame processing time of 20 ms, closely meeting the real-time requirements.
Modern computer vision systems often use neural networks to process images. But to use neural networks, you need to create databases to train these neural networks. In some cases, creating a training database takes the vast majority of the project's financial and human resources. Therefore, the actual task of finding methods to improve the quality of learning neural networks on small data is considered in this article. The ability to process data, which nature was not present in the original training database is relevant, also. To solve the problem of improving the quality of image segmentation by textural anomalies, this research is proposed to use as input to the neural network not only the image but also its local statistic data. It can increase the information content of the input information for the neural network. Therefore, neural networks do not need to learn to choose statistical features but simply use them. This investigation classifies the requirements for image segmentation systems to indicate atypical texture anomalies. The literature analysis revealed various methods and algorithms for solving such problems. As a result, in this science work, the process of finding features in the photo is summarized in stages. The division into stages of search for features allowed to choose arguments for methods and algorithms that can perform the task. At each stage, requirements were formed for methods, that allowed separate the transformation of image fragments into a vector of features by using an artificial neural network (trained on a separate image of the autoencoder). Statistical features supplement by the vector of features of the image fragment. Numerous experiments have shown that the generated feature vectors improve the classification result for an artificial Kohonen neural network, which is able to detect atypical image fragments.
Today, state and municipal services are being actively digitized in Ukraine. In particular, the Kropyvnytskyi city authorities initiated the creation of several information systems (IS) necessary for the development of various spheres of activity based on public needs for municipal services. Among these are IS of medical services provided by the city's health care institutions. Thus, the scientific and technical task of implementing the software for the municipal medical services information system in the city of Kropyvnytskyi is relevant. The work aims to implement access to information about medical services of health care institutions in the city of Kropyvnytskyi by creating municipal information systems with iOS-client. The scientific novelty of the obtained results is to improve the model of municipal information systems of medical services through the implementation of the offline mode of system operation, which in contrast to existing models of similar municipal systems provides access to IP data in the absence of Internet connection. The practical value of the results of scientific work is determined by the developed algorithms of the system, non-creation, work with the map and collection center of analytical, mobile iOS-application of the municipal medical services information system for the city of Kropyvnytskyi, published in the "App Store".
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