User-controlled cyber-security using automated key generation

Traditionally, several different methods are fully capable of providing an adequate degree of security to the threats and attacks that exists for revealing different keys. Though almost all the traditional methods give a good level of immunity to any possible breach in security keys, the biggest issue that exist with these methods is the dependency over third-party applications. Therefore, use of third-party applications is not an acceptable method to be used by high-security applications. For high-security applications, it is more secure that the key generation process is in the hands of the end users rather than a third-party. Giving access to third parties for high-security applications can also make the applications more venerable to data theft, security breach or even a loss in their integrity. In this research, the evolutionary computing tool Eureqa is used for the generation of encryption keys obtained by modelling pseudo-random input data. Previous approaches using this tool have required a calculation time too long for practical use and addressing this drawback is the main focus of the research. The work proposes a number of new approaches to the generation of secret keys for the encryption and decryption of data files and they are compared in their ability to operate in a secure manner using a range of statistical tests and in their ability to reduce calculation time using realistic practical assessments. A number of common tests of performance are the throughput, chi-square, histogram, time for encryption and decryption, key sensitivity and entropy analysis. From the results of the statistical tests, it can be concluded that the proposed data encryption and decryption algorithms are both reliable and secure. Being both reliable and secure eliminates the need for the dependency over third-party applications for the security keys. It also takes less time for the users to generate highly secure keys compared to the previously known techniques.The keys generated via Eureqa also have great potential to be adapted to data communication applications which require high security

Developing a universal Navy uniform adoption model for use in forecasting

MBA Professional ReportThe Navy Exchange Command (NEXCOM) Uniform Program Management Office (UPMO) is responsible for providing initial sales estimates to the Defense Logistics Agency (DLA) for new uniform programs, as a part of a Supply Request Package (SRP). The SRP contains a fielding plan that projects sale quantities through the Navy exchange (NEX) outlets, Recruit Training Command Great Lakes, and the Reserve Component. UPMO also provides annual revisions to DLA that reflect changes to expected sales, due to policy changes. As the item manager for most uniform programs, the DLA relies on these sales’ forecasts provided by the UPMO. In turn, the NEXCOM sources these uniforms from the DLA for commercial sales through the NEXs. This project endeavors to develop an accurate sales forecasting model for use by the NEXCOM to support SRP development. Data analysis software will be used to identify relationships between uniform sales, time, manpower, and allowance data in order to build the model. Once chosen, the best candidate model will be validated against alternate sales data from a comparable uniform program. By using this model, the NEXCOM can provide more accurate procurement estimates to DLA, thereby reducing the risk of inventory shortage or excess inventory holding costs caused by overestimation.http://archive.org/details/developinguniver1094547983Outstanding ThesisLieutenant, United States NavyApproved for public release; distribution is unlimited

On the Application of PSpice for Localised Cloud Security

The work reported in this thesis commenced with a review of methods for creating random binary sequences for encoding data locally by the client before storing in the Cloud. The first method reviewed investigated evolutionary computing software which generated noise-producing functions from natural noise, a highly-speculative novel idea since noise is stochastic. Nevertheless, a function was created which generated noise to seed chaos oscillators which produced random binary sequences and this research led to a circuit-based one-time pad key chaos encoder for encrypting data. Circuit-based delay chaos oscillators, initialised with sampled electronic noise, were simulated in a linear circuit simulator called PSpice. Many simulation problems were encountered because of the nonlinear nature of chaos but were solved by creating new simulation parts, tools and simulation paradigms. Simulation data from a range of chaos sources was exported and analysed using Lyapunov analysis and identified two sources which produced one-time pad sequences with maximum entropy. This led to an encoding system which generated unlimited, infinitely-long period, unique random one-time pad encryption keys for plaintext data length matching. The keys were studied for maximum entropy and passed a suite of stringent internationally-accepted statistical tests for randomness. A prototype containing two delay chaos sources initialised by electronic noise was produced on a double-sided printed circuit board and produced more than 200 Mbits of OTPs. According to Vladimir Kotelnikov in 1941 and Claude Shannon in 1945, one-time pad sequences are theoretically-perfect and unbreakable, provided specific rules are adhered to. Two other techniques for generating random binary sequences were researched; a new circuit element, memristance was incorporated in a Chua chaos oscillator, and a fractional-order Lorenz chaos system with order less than three. Quantum computing will present many problems to cryptographic system security when existing systems are upgraded in the near future. The only existing encoding system that will resist cryptanalysis by this system is the unconditionally-secure one-time pad encryption

A New Set of Spectroscopic Metallicity Calibrations for RR Lyrae Variable Stars

RR Lyrae stars are old, iron-poor, Helium-burning variable stars. RR Lyraes are extremely useful for tracing phase-space structures and metallicities within the galaxy because they are easy to identify, have consistent luminosities, and are found in large numbers in the galactic disk, bulge, and halo. Here we present a new set of spectroscopic metallicity calibrations that use the equivalent widths of the Ca II K, Hγ, and Hδ lines to calculate metallicity values. Applied to spectroscopic survey data, these calibrations will help shed light on the evolution of the Milky Way and other galaxies

Econophysics and Fractional Calculus: Einstein\u27s Evolution Equation, the Fractal Market Hypothesis, Trend Analysis and Future Price Prediction

This paper examines a range of results that can be derived from Einstein’s evolution equation focusing on the effect of introducing a Lévy distribution into the evolution equation. In this context, we examine the derivation (derived exclusively from the evolution equation) of the classical and fractional diffusion equations, the classical and generalised Kolmogorov–Feller equations, the evolution of self-affine stochastic fields through the fractional diffusion equation, the fractional Poisson equation (for the time independent case), and, a derivation of the Lyapunov exponent and volatility. In this way, we provide a collection of results (which includes the derivation of certain fractional partial differential equations) that are fundamental to the stochastic modelling associated with elastic scattering problems obtained under a unifying theme, i.e., Einstein’s evolution equation. This includes an analysis of stochastic fields governed by a symmetric (zero-mean) Gaussian distribution, a Lévy distribution characterised by the Lévy index γ∈[0,2] role= presentation style= box-sizing: border-box; max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3eγ∈[0,2] and the derivation of two impulse response functions for each case. The relationship between non-Gaussian distributions and fractional calculus is examined and applications to financial forecasting under the fractal market hypothesis considered, the reader being provided with example software functions (written in MATLAB) so that the results presented may be reproduced and/or further investigated

The Effect Of Biodiesel Blends On Particle Number Emissions From A Light Duty Diesel Engine

Numerous studies have shown that respirable particles contribute to adverse human health outcomes including discomfort in irritated airways, increased asthma attacks, irregular heartbeat, non-fatal heart attacks, and even death. Particle emissions from diesel vehicles are a major source of airborne particles in urban areas. In response to energy security and global climate regulations, the use of biodiesel as an alternative fuel for petrodiesel has significantly increased in recent years. Particle emissions from diesel engines are highly dependent on fuel composition and, as such, the increased use of biodiesel in diesel vehicles may potentially change the concentration, size, and composition of particles in respirable air. One indicator used to evaluate the potential health risk of these particles to humans is particle diameter (Dp). Ultrafine particles (UFPs, Dp Current research in automotive emissions primarily focuses on particle emissions measured on a total particle mass (PM) basis from heavy-duty diesel vehicles. The nation\u27s light-duty diesel fleet is, however, increasing; and because the mass of a UFP is much less than that of larger particles, the total PM metric is not sufficient for characterization of UFP emissions. As such, this research focuses on light-duty diesel engine transient UFP emissions, measured by particle number (PN), from petrodiesel, biodiesel, and blends thereof. The research objectives were to determine: 1) the difference in UFP emissions between petrodiesel and blends of waste vegetable oil-based biodiesel (WVO), 2) the differences between UFP emissions from blends of WVO and soybean oil-based biodiesel (SOY), and 3) the feasibility of using genetic programming (GP) to select the primary engine operating parameters needed to predict UFP emissions from different blends of biodiesel. The results of this research are significant in that: 1) Total UFP number emission rates (ERs) exhibited a non-monotonic increasing trend relative to biodiesel content of the fuel for both WVO and SOY that is contrary to the majority of prior studies and suggests that certain intermediate biodiesel bends may produce lower UFP emissions than lower and higher blends, 2) The data collected corroborate reports in the literature that fuel consumption of diesel engines equipped with pump-line-nozzle fuel injection systems can increase with biodiesel content of the fuel without operational changes, 3) WVO biodiesel blends reduced the overall mean diameter of the particle distribution relative to petrodiesel more so than SOY biodiesel blends, and 4) Feature selection using genetic programming (GP) suggests that the primary model inputs needed to predict total UFP emissions are exhaust manifold temperature, intake manifold air temperature, mass air flow, and the percentage of biodiesel in the fuel; These are different than inputs typically used for emissions modeling such as engine speed, throttle position, and torque suggesting that UFP emissions modeling could be improved by using other commonly measured engine operating parameters

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