# Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks
## 6 Experimental Results  
We will analyze the results of computational experiments using the above calculation example. 1. The probability of successful delivery of data packets cannot exceed some maximum value, which is determined on the condition that there is no malefactor information impact against the network, there is no queue for service and the network elements are absolutely reliable, i.e.,  
\[P_{max}(t\leq T_{z})\ =\ \begin{array}{l}lim\\ \lambda\to 0\\ t_{ri\rightarrow\infty}\end{array} F(t\ =\ T_{z})\ =\ \frac{h^{\kappa_{h}}}{ \Gamma(\kappa_{h})}\int_{0}^{T_{z}}t^{\kappa_{h}-1}exp\{-ht\}dt. \tag{42}\]  
Meanwhile, the delivery time of the packet depends on only the number of sections on the route, the volume and speed of information transmission. The form of the limit distribution function of the information delivery time is represented in Figure 6.  
Figure 5: The graph of the distribution function of the data delivery time in the context of cyberattacks, when the initial data are specified in the calculation example. 2. If the intensity of information exchange increases, the influence of the type of network traffic model increases (Figure 7). Meanwhile, according to the value of the average delivery time of data packets, the most favorable is the Poisson data flow; when the Weibull flow is serviced, the packet delivery time, depending on the value of the Hurst index \(H\), can increase up to 5... 10 times in relation to the estimated time obtained for a Poisson flow. However, when the Poisson traffic is serviced, the variation in the packet delay time is almost twice as large  
Figure 6: The graph of the distribution function of the information delivery time when \(\lambda\to 0\) and there is no malefactor information impact. Figure 7: The graph of the distribution function of the information delivery time when \(\lambda\ =\ 100\) packets/s and there is no malefactor information impact. as when the Weibull flow is serviced. It should be noted that when the Weibull flow is serviced, we can observe an increase in the probability of packet loss, which is due to the limited storage capacity. When values of the Hurst index are low (\(H\leq\) 0.2) (Figure 8), the differences in the values of the probabilities of successful packet delivery (especially at the level of requirements \((F(t)\geq P_{z}\ =\ P(t\leq T_{z})\ =\ 0.95)\) are negligible, if there are different models of input traffic. When the Hurst index increases (\(H\geq\) 0.2), the distribution function of the packet delivery time in the Weibull flow comes close to an exponential distribution, and when the Pareto flow is serviced, the distribution function goes down, which leads to a considerable increase in the average time of successful data delivery and an increase in the probability of packet loss. This is explained by the difference in the dependence of the variation coefficients of the Weibull and Pareto flows on the value of the Hurst index. It should be noted that, when we simulate the delivery process, the Hurst index value, close to \(H\approx\) 0.5, is the limiting value for the Pareto flow, because if \(H\geq\) 0.5, dispersion for this distribution is not determined [21] (Figure 9), therefore the simulated results of the delivery process obtained at these \(H\) values will be wrong. Figure 8: The graphs of the distribution functions of the delivery time of data packets, if there are different models of network traffic. Analysis of the received results shows that they correspond completely with the data of works published previously by other authors [13; 16; 17; 19; 20; 21; 22; 23] devoted to the development of quality evaluation methods of the functioning of telecommunication networks when self-similar and stationary Poisson traffic is transmitted. This confirms the adequacy of the developed model and the consistency of the received results. 3.