Research and forecasts in IT *

Money theft is one of the most important risks for any organization, regardless of its scope of activity. According to our data, 42% of cyberattacks on companies are committed to obtain direct financial benefits.  You can detect an attack at various stages—from network penetration to the moment when attackers start withdrawing money.  In this article, we will show how to detect an attack at each of its stages and minimize the risk, as well as analyze two common scenarios of such attacks: money theft manually using remote control programs and using special malware—a banking trojan.

The year 2021 started on such a high note for Qrator Labs: on January 19, our company celebrated its 10th anniversary. Shortly after, in February, our network mitigated quite an impressive 750 Gbps DDoS attack based on old and well known DNS amplification. Furthermore, there is a constant flow of BGP incidents; some are becoming global routing anomalies. We started reporting in our newly made Twitter account for Qrator.Radar.

Nevertheless, with the first quarter of the year being over, we can take a closer look at DDoS attacks statistics and BGP incidents for January - March 2021.

Technology is as adaptable and compatible as mankind; it finds its way through problems and situations. 2020 was one such package of uncertain events that forced businesses to adapt to digital transformation, even to an extent where many companies started to consider the remote work culture to be a beneficiary long-term model. Technological advancements like Hyper automation, AI Security, and Distributed cloud showed how any people-centric idea could rule the digital era. The past year clearly showed the boundless possibilities through which technology can survive or reinvent itself. With all those learnings let's deep-dive and focus on some of the top technology trends to watch out for in 2021.

Annotation. This article gives an analogy between the forces of nature and various types of money. A justification for the "money conservation laws" is made. Explanation of the IT-money phenomenon by analogy to physics laws is given, as well as gold and currency money. The transition from the gold and currency to the gold-currency-computing economy is considered. A reasonable assumption is made that the fourth type of money after gold, securities and IT money will be so-called "citation indices" or "ratings", which are similar in their properties to stock indices.

This article is an attempt to understand what money is from the physics and econophysics points of view. Econophysics (economics and physics) is an interdisciplinary research field, applying theories and methods originally developed by physicists to solve problems in economics, usually those including uncertainty or stochastic processes, nonlinear dynamics and evolutionary games.

The last decades the world economy regularly falls into this vortex of financial crises that have affected each country. It almost led to the collapse of the existing financial system, due to this fact, experts in mathematical and economic modelling have become to use methods for controlling the losses of the asset and portfolio in the financial world (Lechner, L. A., and Ovaert, T. C. (2010). There is an increasing trend towards mathematical modelling of an economic process to predict the market behaviour and an assessment of its sustainability (ibid). Having without necessary attention to control and assess properly threats, everybody understands that it is able to trigger tremendous cost in the development of the organisation or even go bankrupt.

Value at Risk (VaR) has eventually been a regular approach to catch the risk among institutions in the finance sector and its regulator (Engle, R., and Manganelli S., 2004). The model is originally applied to estimate the loss value in the investment portfolio within a given period of time as well as at a given probability of occurrence. Besides the fact of using VaR in the financial sector, there are a lot of examples of estimation of value at risk in different area such as anticipating the medical staff to develop the healthcare resource management Zinouri, N. (2016). Despite its applied primitiveness in a real experiment, the model consists of drawbacks in evaluation, (ibid).

The goal of the report is a description of the existing VaR model including one of its upgrade versions, namely, Conditional Value at Risk (CVaR). In the next section and section 3, the evaluation algorithm and testing of the model are explained. For a vivid illustration, the expected loss is estimated on the asset of one of the Kazakhstani company trading in the financial stock exchange market in a long time period. The final sections 4 and 5 discuss and demonstrate the findings of the research work.