Threat modeling a factory environment using Microsoft Security Development Lifecycle methodology

The number of security violations is increasing and a security breach could have irreversible impacts to business. There are several ways to improve organization security, but some of them may be difficult to comprehend. This thesis demystifies threat modeling as part of secure system development. Threat modeling enables developers to reveal previously undetected security issues from computer systems. It offers a structured approach for organizations to find and address threats against vulnerabilities. When implemented correctly threat modeling will reduce the amount of defects and malicious attempts against the target environment. In this thesis Microsoft Security Development Lifecycle (SDL) is introduced as an effective methodology for reducing defects in the target system. SDL is traditionally meant to be used in software development, principles can be however partially adapted to IT-infrastructure development. Microsoft threat modeling methodology is an important part of SDL and it is utilized in this thesis to find threats from the Acme Corporation’s factory environment. Acme Corporation is used as a pseudonym for a company providing high-technology consumer electronics. Target for threat modeling is the IT-infrastructure of factory’s manufacturing execution system. Microsoft threat modeling methodology utilizes STRIDE –mnemonic and data flow diagrams to find threats. Threat modeling in this thesis returned results that were important for the organization. Acme Corporation now has more comprehensive understanding concerning IT-infrastructure of the manufacturing execution system. On top of vulnerability related results threat modeling provided coherent views of the target system. Subject matter experts from different areas can now agree upon functions and dependencies of the target system. Threat modeling was recognized as a useful activity for improving security.

A software engineering approach to distributed systems for composite modeling

The goal of this thesis is to define and validate a software engineering approach for the development of a distributed system for the modeling of composite materials, based on the analysis of various existing software development methods. We reviewed the main features of: (1) software engineering methodologies; (2) distributed system characteristics and their effect on software development; (3) composite materials modeling activities and the requirements for the software development. Using the design science as a research methodology, the distributed system for creating models of composite materials is created and evaluated. Empirical experiments which we conducted showed good convergence of modeled and real processes. During the study, we paid attention to the matter of complexity and importance of distributed system and a deep understanding of modern software engineering methods and tools.

Dynamic modeling and simulation of the airflow cleaning device

The objective of the work is to study the flow behavior and to support the design of air cleaner by dynamic simulation.In a paper printing industry, it is necessary to monitor the quality of paper when the paper is being produced. During the production, the quality of the paper can be monitored by camera. Therefore, it is necessary to keep the camera lens clean as wood particles may fall from the paper and lie on the camera lens. In this work, the behavior of the air flow and effect of the airflow on the particles at different inlet angles are simulated. Geometries of a different inlet angles of single-channel and double-channel case were constructed using ANSYS CFD Software. All the simulations were performed in ANSYS Fluent. The simulation results of single-channel and double-channel case revealed significant differences in the behavior of the flow and the particle velocity. The main conclusion from this work are in following. 1) For the single channel case the best angle was 0 degree because in that case, the air flow can keep 60% of the particles away from the lens which would otherwise stay on lens. 2) For the double channel case, the best solution was found when the angle of the first inlet was 0 degree and the angle of second inlet was 45 degree . In that case, the airflow can keep 91% of particles away from the lens which would otherwise stay on lens.

Business modeling in turbulent environments

This study presents an agile tool set for the business modeling in companies, entering the turbulent environment. The study’s aim is to explore business modeling techniques and their tooling by utilizing a case study of a Finnish media monitoring company, expanding to the Russian market. This work proposes a tailored “two-approach” of business modeling development that analyzes both the past and future conditions of two key factors of business modeling for companies – internal and external environments. The study explores a case company by investigating the benefits and disadvantages of firm’s present business modeling tools, developing a new tooling and applying it to the case company. Among primary data utilization, such as interviews with media monitoring industry analysts and representatives of the competing companies, and academic experts, study leans up on the comprehensive analysis of Russian media monitoring niche and its players. This study benefits the business modeling research area and combines traditional analysis tools, such as market, PESTLE and competitor analyses, in a systemic manner, with the business modeling techniques. This transformation proceeds through applying of the integrated scenario, heat map and critical design issues’ analyses in the societal, industrial and competitive context of turbulent environments. The practical outcome of this approach is the development of agile business modeling tool set, customizable by company’s requirements.

A sequential analysis of therapist scaffolding and child concept formation in narrative therapy

Narrative therapy is a postmodern therapy that takes the position that people create self-narratives to make sense of their experiences. To date, narrative therapy has compiled virtually no quantitative and very little qualitative research, leaving gaps in almost all areas of process and outcome. White (2006a), one of the therapy's founders, has recently utilized Vygotsky's (1934/1987) theories of the zone of proximal development (ZPD) and concept formation to describe the process of change in narrative therapy with children. In collaboration with the child client, the narrative therapist formalizes therapeutic concepts and submits them to increasing levels of generalization to create a ZPD. This study sought to determine whether the child's development proceeds through the stages of concept formation over the course of a session, and whether therapists' utterances scaffold this movement. A sequential analysis was used due to its unique ability to measure dynamic processes in social interactions. Stages of concept formation and scaffolding were coded over time. A hierarchical log-linear analysis was performed on the sequential data to develop a model of therapist scaffolding and child concept development. This was intended to determine what patterns occur and whether the stated intent of narrative therapy matches its actual process. In accordance with narrative therapy theory, the log-linear analysis produced a final model with interactions between therapist and child utterances, and between both therapist and child utterances and time. Specifically, the child and youth participants in therapy tended to respond to therapist scaffolding at the corresponding level of concept formation. Both children and youth and therapists also tended to move away from earlier and toward later stages of White's scaffolding conversations map as the therapy session advanced. These findings provide support for White's contention that narrative therapists promote child development by scaffolding child concept formation in therapy.

Investigations in flow injection analysis (FIA): conventional FIA determination of chloride in water; sequential injection (SI)-FIA determination of mercury in water

Flow injection analysis (FIA) was applied to the determination of both chloride ion and mercury in water. Conventional FIA was employed for the chloride study. Investigations of the Fe3 +/Hg(SCN)2/CI-,450 nm spectrophotometric system for chloride determination led to the discovery of an absorbance in the 250-260 nm region when Hg(SCN)2 and CI- are combined in solution, in the absence of iron(III). Employing an in-house FIA system, absorbance observed at 254 nm exhibited a linear relation from essentially 0 - 2000 Jlg ml- 1 injected chloride. This linear range spanning three orders of magnitude is superior to the Fe3+/Hg(SCN)2/CI- system currently employed by laboratories worldwide. The detection limit obtainable with the proposed method was determin~d to be 0.16 Jlg ml- 1 and the relative standard deviation was determined to be 3.5 % over the concentration range of 0-200 Jig ml- 1. Other halogen ions were found to interfere with chloride determination at 254 nm whereas cations did not interfere. This system was successfully applied to the determination of chloride ion in laboratory water. Sequential injection (SI)-FIA was employed for mercury determination in water with the PSA Galahad mercury amalgamation, and Merlin mercury fluorescence detection systems. Initial mercury in air determinations involved injections of mercury saturated air directly into the Galahad whereas mercury in water determinations involved solution delivery via peristaltic pump to a gas/liquid separator, after reduction by stannous chloride. A series of changes were made to the internal hardware and valving systems of the Galahad mercury preconcentrator. Sequential injection solution delivery replaced the continuous peristaltic pump system and computer control was implemented to control and integrate all aspects of solution delivery, sample preconcentration and signal processing. Detection limits currently obtainable with this system are 0.1 ng ml-1 HgO.

Modeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using Computational Intelligence

Experimental Extended X-ray Absorption Fine Structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, pre- dicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proven somewhat successful in structure refinement but have not been successful in finding the global minima. Multiple population based algorithms, including a genetic algorithm, a restarting ge- netic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure refinement of EXAFS. The oxygen-evolving com- plex in S1 is used as a benchmark for comparing the algorithms. These algorithms were successful in finding new atomic structures that produced improved calculated EXAFS spectra over atomic structures previously found.

Modeling short-range ballistic missile defense and Israel's Iron Dome system

This paper develops a model of short-range ballistic missile defense and uses it to study the performance of Israel’s Iron Dome system. The deterministic base model allows for inaccurate missiles, unsuccessful interceptions, and civil defense. Model enhancements consider the trade-offs in attacking the interception system, the difficulties faced by militants in assembling large salvos, and the effects of imperfect missile classification by the defender. A stochastic model is also developed. Analysis shows that system performance can be highly sensitive to the missile salvo size, and that systems with higher interception rates are more “fragile” when overloaded. The model is calibrated using publically available data about Iron Dome’s use during Operation Pillar of Defense in November 2012. If the systems performed as claimed, they saved Israel an estimated 1778 casualties and $80 million in property damage, and thereby made preemptive strikes on Gaza about 8 times less valuable to Israel. Gaza militants could have inflicted far more damage by grouping their rockets into large salvos, but this may have been difficult given Israel’s suppression efforts. Counter-battery fire by the militants is unlikely to be worthwhile unless they can obtain much more accurate missiles.

On the Political Economy of Sequential Reforms

This paper proposes an explanation for why efficient reforms are not carried out when losers have the power to block their implementation, even though compensating them is feasible. We construct a signaling model with two-sided incomplete information in which a government faces the task of sequentially implementing two reforms by bargaining with interest groups. The organization of interest groups is endogenous. Compensations are distortionary and government types differ in the concern about distortions. We show that, when compensations are allowed to be informative about the government’s type, there is a bias against the payment of compensations and the implementation of reforms. This is because paying high compensations today provides incentives for some interest groups to organize and oppose subsequent reforms with the only purpose of receiving a transfer. By paying lower compensations, governments attempt to prevent such interest groups from organizing. However, this comes at the cost of reforms being blocked by interest groups with relatively high losses.

An Eigenfunction Approach for Volatility Modeling.

In this paper, we introduce a new approach for volatility modeling in discrete and continuous time. We follow the stochastic volatility literature by assuming that the variance is a function of a state variable. However, instead of assuming that the loading function is ad hoc (e.g., exponential or affine), we assume that it is a linear combination of the eigenfunctions of the conditional expectation (resp. infinitesimal generator) operator associated to the state variable in discrete (resp. continuous) time. Special examples are the popular log-normal and square-root models where the eigenfunctions are the Hermite and Laguerre polynomials respectively. The eigenfunction approach has at least six advantages: i) it is general since any square integrable function may be written as a linear combination of the eigenfunctions; ii) the orthogonality of the eigenfunctions leads to the traditional interpretations of the linear principal components analysis; iii) the implied dynamics of the variance and squared return processes are ARMA and, hence, simple for forecasting and inference purposes; (iv) more importantly, this generates fat tails for the variance and returns processes; v) in contrast to popular models, the variance of the variance is a flexible function of the variance; vi) these models are closed under temporal aggregation.

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

Affiliation: Institut de recherche en immunologie et en cancérologie, Université de Montréal