Using Neural Networks to Create and Test Pseudorandom Number Generators

The article presents an overview of modern researches in the field of neural cryptography in relation to pseudorandom number generators (PRNG). Various types of PRNGs and their implementation are provided. We provide the criteria, due to which the PRNG can be considered cryptographically secure (CSPRNG). There are reasons for using certain types of generators. We briefly describe the theory underlying neural networks (NN). We carry the comparison of the NN architectures in the application to the tasks of creating a PRNG and testing output sequences out. Various sets of statistical tests for the analysis of output sequences from PRNG are presented. We consider the results of the most significant articles on the creation of a PRNGs based on the NN. We study articles that based on both classical recurrent networks (Elman, LSTM) and modern generative-adversarial network (GAN). The study of the methods of testing the RNG with the help of NN is implemented. We consider methods of analyzing the output sequences of the RNG and the negative consequences of underestimating the importance of this stage. We describe trends in the neural cryptography, such as the study of numbers that were originally considered random (for example, the number π) and the analysis of the output sequences of quantum random number generators (QRNG) for the presence of patterns

Нейросетевая обфускация вычислений над зашифрованными данными

Предложен подход по нейросетевой криптографической обфускации вычислений. Опираясь на ранее полученные результаты о свойстве строгой обфускации неразличимости для нейросетевого аппроксиматора, мы предлагаем использовать нейросети для выполнения арифметических и других операций над зашифрованными данными, реализуя таким образом идею применения гомоморфного шифрования для выполнения доверенных вычислений в недоверенной среде. Проводится оценка криптографических свойств предложенного механизма и сопоставление с традиционными подходами к шифрованию на основе секретного ключа. Обсуждаются достоинства и недостатки нейронных сетей применительно к задачам обфускации и обработки зашифрованных данных

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Client-side encryption and key management: enforcing data confidentiality in the cloud.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2016.Cloud computing brings flexible, scalable and cost effective services. This is a computing paradigm whose services are driven by the concept of virtualization and multi-tenancy. These concepts bring various attractive benefits to the cloud. Among the benefits is reduction in capital costs, pay-per-use model, enormous storage capacity etc. However, there are overwhelming concerns over data confidentiality on the cloud. These concerns arise from various attacks that are directed towards compromising data confidentiality in virtual machines (VMs). The attacks may include inter-VM and VM sprawls. Moreover, weaknesses or lack of data encryption make such attacks to thrive. Hence, this dissertation presents a novel client-side cryptosystem derived from evolutionary computing concepts. The proposed solution makes use of chaotic random noise to generate a fitness function. The fitness function is used to generate strong symmetric keys. The strength of the encryption key is derived from the chaotic and randomness properties of the input noise. Such properties increase the strength of the key without necessarily increasing its length. However, having the strongest key does not guarantee confidentiality if the key management system is flawed. For example, encryption has little value if key management processes are not vigorously enforced. Hence, one of the challenges of cloud-based encryption is key management. Therefore, this dissertation also makes an attempt to address the prevalent key management problem. It uses a counter propagation neural network (CPNN) to perform key provision and revocation. Neural networks are used to design ciphers. Using both supervised and unsupervised machine learning processes, the solution incorporates a CPNN to learn a crypto key. Using this technique there is no need for users to store or retain a key which could be compromised. Furthermore, in a multi-tenant and distributed environment such as the cloud, data can be shared among multiple cloud users or even systems. Based on Shamir's secret sharing algorithm, this research proposes a secret sharing scheme to ensure a seamless and convenient sharing environment. The proposed solution is implemented on a live openNebula cloud infrastructure to demonstrate and illustrate is practicability

Applicability of Neural Networks to Software Security

Software design flaws account for 50% software security vulnerability today. As attacks on vulnerable software continue to increase, the demand for secure software is also increasing thereby putting software developers under more pressure. This is especially true for those developers whose primary aim is to produce their software quickly under tight deadlines in order to release it into the market early. While there are many tools focusing on implementation problems during software development lifecycle (SDLC), this does not provide a complete solution in resolving software security problems. Therefore designing software with security in mind will go a long way in developing secure software. However, most of the current approaches used for evaluating software designs require the involvement of security experts because many software developers often lack the required expertise in making their software secure. In this research the current approaches used in integrating security at the design level is discussed and a new method of evaluating software design using neural network as evaluation tool is presented. With the aid of the proposed neural network tool, this research found out that software design scenarios can be matched to attack patterns that identify the security flaws in the design scenarios. Also, with the proposed neural network tool this research found out that the identified attack patterns can be matched to security patterns that can provide mitigation to the threat in the attack pattern