Analysis of information security issues in corporate computer networks

Lavrov, Evgeniy; Zolkin, A. L.; Aygumov, Timur Gadzhievich; Chistyakov, M.S.; Akhmetov, I. V.

doi:10.1088/1757-899x/1047/1/012117

Cited by 16 publications

(6 citation statements)

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“…The economy with stocks and bonds is analyzed here, for example, annual and quarterly reports of emitter companies. Based on this analyses, the conclusion on which stocks should act better, and which ones are worse is made [6].…”

Section: Resultsmentioning

confidence: 99%

Methodology and principles of assets allocation

et al. 2021

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Every year the number of those wishing to dive in investments is growing at exponential way. In despite of situation in 2020 many people could efficiently use this time, and some of them could even reserve the assets. But now the other challenge is arisen, how should the assets be invested in an effective way using all power of modern financial markets? The slight knowledge intended for experienced people are presented in the most books, articles and manuals. But how the novice investors should manage which do not see the difference between stock and bond, and the “diversification” word ignites panic-driven fear and discourages from development in this area. The material helping every of those wishing to start without fear and doubts the diving in investment from the very beginning is presented in this work. The main classes of assets with which the Wall Street’s financial experts work, principles for assets allocation and methods helping to choose the optimal model for profit-risk calculation are described in this article.

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Section: Resultsmentioning

confidence: 99%

Methodology and principles of assets allocation

et al. 2021

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“…At the next stage, a study of the use of machine learning to prevent incidents and occupational risks at the workplace has been carried out. Statistical methods and machine learning algorithms, such as logistic regression and random forest, are applied to analyze the data and build models for prediction of incidents and occupational risks [12].…”

Section: Equipment Damage 150mentioning

confidence: 99%

Use of machine learning for prevention of incidents and reduction of occupational risks at the workplace

Kuleshov

Aygumov

Zolkin

et al. 2023

International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023)

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This article examines the use of machine learning to prevent incidents and occupational hazards at the workplace. The authors have analyzed existing research in this area and have demonstrated the possibility of use of the machine learning to predict risks and prevent incidents. Specifically, the authors studied the methods of logical regression and classification to analyze data on past incidents. The results of the study showed that the use of machine learning can significantly improve the accuracy of risk prediction and enable more effective incident prevention. The importance of introduction of machine learning is discussed in the context of workplace safety and directions for further research are suggested. Overall, the article is an important addition to the general literature on the use of machine learning in the field of workplace safety and provides an opportunity to better understand the background and improve the accuracy of risk prediction and incident prevention at the workplace. The study is of interest to scientists, practitioners and politicians involved in workplace safety, as well as to those who are involved in the development and implementation of machine learning systems.

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“…Access is a significant component of insider threats, as proven by observations from the real world, assertions from academics, and discoveries from the information technology sector. The bulk of research that have been conducted on the topic of insider threats have come to the conclusion that the primary reason that insiders steal data from their organisations is because they were given authorized access to the material that was subsequently taken [23][24][25][26]. Seventy-five percent of thefts committed with authority were carried out, as reported by CERT, by workers or other insiders [1].…”

Section: Related Workmentioning

confidence: 99%

“…The unintended insider risk also includes a significant component that involves the unauthorised use of data obtained from an outside source [25]. Access to the company's networks is relatively easy to come by, but not everyone has direct access to the company's information technology resources [26]. In the prior studies, it was difficult to track down findings associated to those different resources.…”

Section: Introductionmentioning

confidence: 99%

Threats From Unintentional Insiders: An Assessment of an Organization’s Readiness Using Machine Learning

2022

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Today's organisations are facing a number of challenges, one of the most significant of which is ensuring the safety of their digital data. This is as a result of the fact that they are frequently faced with internal and external threats that can put the data they have been entrusted with in jeopardy of being compromised. As a result of this, this study investigates the dimension of threats associated to unintentional internal user of an organisation and utilises NARX to model and test a detection scheme associated to the menace. In addition, this study aims to provide a better understanding of the current state of the threat landscape. The data adopted for this research is primarily a "user activity logs" dataset from CERT (release version r4.2). From the data, the study conceptualized "Access", "Motivation", and "Action" to be the key dimensions influencing "insider", whereas "Intent", "+Action", "Method", and "knowledge" are the key dimension influencing "threats". Experimental analyses conducted by NARX within several numbers of partitions of the data point to a good detection capacity, with the greatest value of R 2 coming in at 0.97. This indicates that NARX was able to detect the crucial dimension that was formulated for by the research to be the detections parameter of an inadvertent insider threat when operating under the best partition. In light of these findings, organisations can use the proposed approach to assess their preparedness for Insider attacks.

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Analysis of information security issues in corporate computer networks

Cited by 16 publications

References 0 publications

Methodology and principles of assets allocation

Methodology and principles of assets allocation

Use of machine learning for prevention of incidents and reduction of occupational risks at the workplace

Threats From Unintentional Insiders: An Assessment of an Organization’s Readiness Using Machine Learning

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