The article presents a novel method of fractal time series classification by meta-algorithms based on decision trees. The classification objects are fractal time series. For modeling, binomial stochastic cascade processes are chosen. Each class that was singled out unites model time series with the same fractal properties. Numerical experiments demonstrate that the best results are obtained by the random forest method with regression trees. A comparative analysis of the classification approaches, based on the random forest method, and traditional estimation of self-similarity degree are performed. The results show the advantage of machine learning methods over traditional time series evaluation. The results were used for detecting denial-of-service (DDoS) attacks and demonstrated a high probability of detection.

This article studies the existing methods of virtualization of different resources. The positive and negative aspects of each of the methods are analyzed, the perspectivity of the approach is noted. It is also made an attempt to classify virtualization methods according to the application domain, which allows us to discover the method weaknesses which are needed to be optimized.

The paper presents an analysis of the main mechanisms of decryption of SSL/TLS traffic. Methods and technologies for detecting malicious activity in encrypted traffic that are used by leading companies are also considered. Also, the approach for intercepting and decrypting traffic transmitted over SSL/TLS is developed, tested and proposed. The developed approach has been automated and can be used for remote listening of the network, which will allow to decrypt transmitted data in a mode close to real time.

The article considers classification task of fractal time series by the random forest method. It is proposed to classify time series as features to use the quantitative characteristics of recurrent plots. Time series of fractal Brownian motion were selected as input time series. The comparative analysis of different time series classification was held. The difference between fractal Brownian motion and fractal Gaussian noise in probabilities determining class and significant features are quite large. For fractal Brownian motion, it is enough recurrence features, while for fractal Gaussian noise it is necessary to use the estimate of H (except persistent series). The average probabilities for fractal Brownian motion are higher than for fractal Gaussian noise. For fractal Brownian motion, the most important features are ones of laminarity and determinism. For fractal Gaussian noise the laminar measure is not important, but the measures of average recurrence time and white vertical lines are significant. The results show that the important for the classification of fractal series is what kind of series is persistent or antipersistent. The results indicate the advantage of the recurrent features over the statistical and fractal ones.

The paper suggests methods to the assessment of information risks, which makes the transition from a qualitative assessment of information risks (according to the factor analysis of information risks methodology) to a quantitative assessment. The development factor analysis of information risks methodology of the methodology was carried out using the mathematical apparatus of probability theory, namely Bayesian networks. A comparative analysis of the standard factor analysis of information risks methodology and the developed methodology using statistical data was carried out. During the analysis, the cause and effect relationships of the confidentiality violation have been formed, defined and given in the corresponding table and in the form of the Ishikawa diagram. As an example, it was calculated the amount of risk the company may be exposed to in case of violation of information confidentiality according to the standard factor analysis of information risks methodology and the developed methodology. It is shown that the use of proposed technique allows quantifying the risk assessment that can be obtained using the factor analysis of information risks methodology.

The article considers classification task of fractal time series by the meta algorithms based on decision trees. Binomial multiplicative stochastic cascades are used as input time series. Comparative analysis of the classification approaches based on different features is carried out. The results indicate the advantage of the machine learning methods over the traditional estimating the degree of self-similarity.