Search

The article of record as published may be found at https://doi.org/10.1109/TEM.2019.2958785 In the early stages of the systems engineering process, an important focus is to create an understanding of the stakeholder needs. This is primarily done to prepare the system specification that forms the basis for the system’s design. By extension, example steps in this process include surveying stakeholders to better capture their intent, deriving and documenting requirements, and then using those requirements for subsequent activities, such as developing a functional baseline and candidate design alternatives. During this process, it is important to consider the full system lifecycle. As such, one major objective of a systems engineer is to translate the stakeholder’s needs into functional and nonfunctional requirements (NFRs). Despite this important role, early system designs are often faulty because important NFRs are poorly prioritized or not prioritized at all. While the prioritization of all requirements can be useful, this work focuses specifically on NFRs. It has been identified that the inability to identify the most useful NFRs can lead to system failure. Furthermore, the lack of NFR prioritization is considered one of the most expensive and difficult errors to correct, as well as one of the ten most significant risks in engineering. Systems need more emphasis on the relationships between the system’s elements, rather than on the individual elements or the whole system. Relationships among elements in a system can illustrate more than just the behavior of each element. The illustration can include the purpose for the system and the implications of changing how the NFRs associated with those elements are prioritized. This emphasis requires quantifiable tools and rigor to inform the decision makers. This research’s objective is to contribute to quantifiable decision-making methods and prioritization of NFRs in three ways: the development of a process to determine unique random index; the use of a continuous ranking scale; and the development of a universal decision-making heuristic to accompany prioritization of NFRs.

To determine the overall trustworthiness of a complex Cyber Physical System (CPS), it is critical to consider several methods of engineering design. Traditionally, translating a customer needs into requirements, through physical realization and into an operation leans on processes that are largely dependent upon functional requirements. There are, however, no strict rules in design that dictate the use of functionality as the fundamental approach. Since no strict rules exist, non-functional requirements will be used contrary to the traditional use of functional requirements. In particular, trustworthiness is a relatively defined term relying on face-validity methods from a multitude of non-functional requirements identified in related research. A limited-design method with Analytical Hierarchy Process (AHP), Axiomatic Design (AD) and Design of Experiments (DOE) is tested using this nonfunctional requirement of trustworthiness. The aggregated measurement is used to determine where it is best to focus design efforts in the concept stage.

This article presents a combination of known models and system design techniques that result in an architectural framework that is predictive. The article also provides a methodical recommendation that develops the way defense of a cyber critical infrastructure can be most effective. Firstly, historical background has been provided to motivate the current political theme, followed by modeling theory that has been published. Classical systems engineering foundations are reviewed to adapt modeling environment to the current cyber conflict problem in a way that allows a systems owner to posture most effectively against anomalies across the network of communication nodes. The implication of the model presented in the article is that a protection posture can be estimated, and can quickly turn into a balanced engagement between the attacker and defender. When optimized within decision algorithms, data will exist to illuminate what process can be implemented in defense. Using classical systems engineering fundamentals, modeling simulation, and proven mathematical approaches, this research seeks to support such implementation.

This paper investigates a category of cyber-attacks on control systems, which regulate processes of a single plant while sharing a communication network. The design of these attacks aims to deceive conventional fault detectors that test locally generated residuals for inconsistent statistics. The authors propose a network-wide attack detector and isolator that collects information from other neighborhoods subject to availability of locality and network resources. Their method relies on estimating the output of a process, whose regulator may be under attack, from measurements gathered at other processes connected to the one under examination through links existing at the physical layer. Next, a notional consensus network coalesces all of these estimates into information that is independent of possibly deceptive sensory data at the suspect locality. The thesis of this paper is that residuals generated from far-flung estimates will reveal an anomaly (even if the statistics of local residuals are consistent). A necessary condition is the existence of an observable subsystem within the physical network of interconnected processes. The authors employ graph theory techniques to identify the subsystem and optimize its observability.

In this work we solve the problem of architecting a conceptual, cost-effective, near-term system of systems (SoS) to respond to terrorist threats to the United States emanating from the maritime domain. The threats include a weapon of mass destruction smuggled on a container ship, a commandeered ship used as a weapon, and small boats used by terrorists to attack maritime commerce traffic and critical shore infrastructures. We formulate the problem as an assignment problem, which is then solved using the orthogonal array experiment. The optimality of the resulting SoS architecture is validated against a heuristically developed architecture and an optimal effective, but not necessarily cost-effective, architecture obtained also with the orthogonal array experiment approach. The principal results of the orthogonal array experiment method reported herein underline this successful exploratory work in architecting an SoS. This method can be extended to architecting of other systems of systems.

In this study, the authors examined student and faculty expectations regarding college student course work behaviors. Three hundred and eighty-seven students and 52 faculty members from the same university completed surveys designed to measure their perceptions of the amount of student effort required to earn an A, B, C, or D letter grade. Findings show that students and faculty members generally agreed on how hard students have to work in an academic environment. The authors discuss some gender differences in students' expectations.