The emergence of a large number of e-learning platforms and courses does not solve the problem of improving the quality of education. This is primarily due to insufficient implementation or lack of mechanisms for adaptation to the individual parameters of the student. The level of adaptation in modern e-learning systems to the individual characteristics of the student makes the organization of human-computer interaction relevant. As the solution of the problem, a mathematical model of the organization of human-computer interaction was proposed in this work. It is based on the principle of two-level adaptation that determines the choice of the most comfortable module for studying at the first level. The formation of an individual learning path is performed at the second level. The problem of choosing an e-module is solved using a fuzzy logic. The problem of forming a learning path is reduced to the problem of linear programming. The input data are the characteristics of the quality of student activity in the education system. Based on the proposed model the computer technology to support student activities in modular e-learning systems is developed. This technology allows increasing the level of student’s cognitive comfort and optimizing the learning time. The most important benefit of the proposed approach is to increase the average score and increase student satisfaction with learning.

Random numbers play an important role in many areas, for example, encryption, cryptography, static analysis, simulations. It is also a fundamental resource in science and engineering. There are algorithmically generated numbers that are similar to random distributions, but are not actually random, called pseudo random number generators. In many cases the tasks to be solved are based on the unpredictability of random numbers, which cannot be guaranteed in the case of pseudo random number generators, true randomness is required. In such situations, we use real random number generators whose source of randomness is unpredictable random events.
Quantum Random Number Generators (QRNGs) generate real random numbers based on the inherent randomness of quantum measurements. Our goal is to generate fast random numbers at a lower cost. At the same time, a high level of randomness is essential.
Through quantum mechanics, we can obtain true numbers using the unpredictable behavior of a photon, which is the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the operation of the device and the quality of the random bits generated.
We present the improved novel quantum random number generator, which is based the on time of arrival QRNG. It uses the simple version of the detectors with few requirements. The novel QRNG produces more than one random bit per each photon detection. It is rather efficient and has a high level of randomness.
Self-testing as well as device independent quantum random number generation methods are analyzed. The advantages and disadvantages of both methods are identified. The model of a novel semi self-testing certification method for quantum random number generators (QRNG) is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. Finally, the novel certification method is integrated into the model of the new quantum random number generator. The paper analyzes its security and efficiency.

Random numbers have many uses, but finding true randomness is incredibly difficult. Therefore, quantum mechanics is used, using the essentially unpredictable behavior of a photon, to generate truly random numbers that form the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the performance of our device and the quality of the random bits generated. Self-testing as well as device independent quantum random number generation methods are analyzed in the paper. The advantages and disadvantages of both methods are identified. The model of a novel semi self-testing certification method for quantum random number generators is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. The method is very important for computer science, because it combines the best features from self-testing and device independent methods. It can be used, when the random numbers’ entropy depends on the device and when it does not. In the related researches, these approaches are offered to be used separately, depending on the random number generator. The offered novel certification technology can be properly used, when the device is compromised or spoiled. The technology can successfully detect unintended irregularities, operational problems, abnormalities and problems in the randomization process. The offered mythology assists to eliminate problems related to physical devices. The offered system has the higher certification randomness security and is faster than self-testing approaches. The method is rather efficient because it implements the different certification approaches in the parallel threads. The offered techniques make the offered research must more efficient than the other existing approaches. The corresponding programming simulation is implemented by means of the simulation techniques.

Represented paper is currently topical, because of year on year increasing quantity and diversity of attacks on computer networks that causes significant losses for companies. This work provides abilities of such problems solving as: existing methods of location of anomalies and current hazards at networks, statistical methods consideration, as effective methods of anomaly detection and experimental discovery of choosed method effectiveness. The method of network traffic capture and analysis during the network segment passive monitoring is considered in this work. Also, the processing way of numerous network traffic indexes for further network information safety level evaluation is proposed. Represented methods and concepts usage allows increasing of network segment reliability at the expense of operative network anomalies capturing, that could testify about possible hazards and such information is very useful for the network administrator. To get a proof of the method effectiveness, several network attacks, whose data is storing in specialised DARPA dataset, were chosen. Relevant parameters for every attack type were calculated. In such a way, start and termination time of the attack could be obtained by this method with insignificant error for some methods.

Due to the fundamentally different approach underlying quantum cryptography (QC), it has not only become competitive, but also has significant advantages over traditional cryptography methods. Such significant advantage as theoretical and informational stability is achieved through the use of unique quantum particles and the inviolability of quantum physics postulates, in addition it does not depend on the intruder computational capabilities. However, even with such impressive reliability results, QC methods have some disadvantages. For instance, such promising trend as quantum secure direct communication – eliminates the problem of key distribution, since it allows to transmit information by open channel without encrypting it. However, in these protocols, each bit is confidential and should not be compromised, therefore, the requirements for protocol stability are increasing and additional security methods are needed. For a whole class of methods to ensure qutrit QC protocols stability, reliable trit generation method is required. In this paper authors have developed and studied trit generation method and software tool TriGen v.2.0 PRNG. Developed PRNG is important for various practical cryptographic applications (for example, trit QC systems, IoT and Blockchain technologies). Future research can be related with developing fully functional version of testing technique and software tool.

In this paper the method of network-centric monitoring of cyberincidents was developed, which is based on network-centric concept and implements in 8 stages. This method allows to determine the most important objects for protection, and predict the category of cyberincidents, which will arise as a result of cyberattack, and their level of criticality.

Continuous growth of using the information technologies in the modern world causes gradual accretion amounts of data that are circulating in information and telecommunication system. That creates an urgent need for the establishment of large-scale data storage and accumulation areas and generates many new threats that are not easy to detect. Task of accumulation and storing is solved by datacenters – tools, which are able to provide and automate any business process. For now, almost all service providers use quite promising technology of building datacenters – Cloud Computing, which has some advantages over its traditional opponents. Nevertheless, problem of the provider’s data protection is so huge that risk to lose all your data in the “cloud” is almost constant. It causes the necessity of processing great amounts of data in real-time and quick notification of possible threats. Therefore, it is reasonable to implement in data centers’ network an intellectual system, which will be able to process large datasets and detect possible breaches. Usual threat detection methods are based on signature methods, the main idea of which is comparing the incoming traffic with databases of known threats. However, such methods are becoming ineffective, when the threat is new and it has not been added to database yet. In that case, it is more preferable to use intellectual methods that are capable of tracking any unusual activity in specific system – anomaly detection methods. However, signature module will detect known threats faster, so it is logical to include it in the system too. Big Data methods and tools (e.g. distributed file system, parallel computing on many servers) will provide the speed of such system and allow to process data dynamically. This paper is aimed to demonstrate developed anomaly detection system in secure cloud computing environment, show its theoretical description and conduct appropriate simulation. The result demonstrate that the developed system provides the high percentage (>90%) of anomaly detection in secure cloud computing environment.