Shahryar Sarkani

Unmanned aerial vehicle smart device ground control station cyber security threat model

The Department of the Defense has transitioned smart devices into the battlefield as a portable hand-held unmanned aerial vehicle ground control station without adequate cyber security protections, putting critical mission data at risk to cyber security attacks. Industry has developed software apps for smart phones and tablets that allows soldiers to not only pilot unmanned aerial vehicles (UAVs) but to share and receive intelligence and reconnaissance videos and images remotely from the ground control station (GCS) or directly from the UAV. The Department of Defense has not developed a secure communication network that will support a large quantity of smart devices, nor certification, standards or policies for operation of secure smart devices. Therefore, mission critical information will be shared through unsecured, mobile and wireless networks and through unclassified, unsecure smart technology that are vulnerable to cyber security risks. Lack of security of the mobile and wireless networks and smart devices could result in the unintentional sharing of data as well as loss of control of the UAV to enemies. The Department of Defense has failed to develop a threat model and risk assessment to identify the cyber security threats and ensure the proper security countermeasures are in place. This paper will analyze the cyber security vulnerabilities within the communication links, smart devices hardware, specifically smart phones and tablets, and software applications to develop a risk model of the threat profile of the GCS networking hub. This model will help designers and users of the military and civilian UAV communities to understand the threat profile of the GCS networking hub to develop a secure communication network based upon the vulnerabilities identified for smart phones and tablets.

Software requirements prioritization and selection using linguistic tools and constraint solvers--a controlled experiment

Implementing the entire set of requirements for a software system is often not feasible owing to time and resource limitations. A key driver for successful delivery of any software system is the ability to prioritize the large number of requirements. Prioritization of requirements is a key challenge because current methods are not scalable to handle a realistic number of requirements. Current methods for requirements prioritization in market-driven software development projects are neither sufficient nor proven. A prioritization technique that is more time-efficient, accurate, and easier to implement for large-scale projects than current practices is needed. We address these challenges with a prioritization method that incorporates the use of a linguistic tool and constraint solver. In this paper we propose a method, referred to as SNIPR, for requirements prioritization and selection based on natural language processing and satisfiability modulo theories solvers. We present a controlled experiment in which 40 systems engineers prioritized and selected 20 requirements from a list of 100 using SNIPR and the weighted sum model. Results show that the SNIPR method consumes less time, improves selection accuracy, and is easier to perform than the weighted sum model. These results motivate further research using linguistic tools and constraint solvers for the prioritization of large sets of requirements.

Risk Identification Biases and Their Impact to Space System Development Project Performance

Abstract: Risk identification during the design and development phases of complex systems is commonly implemented, but often fails to result in the identification of events and circumstances that truly challenge project performance. Inefficiencies in cost and schedule estimation are usually held accountable for cost and schedule overruns, but the true root cause is often the realization of programmatic and technical risks. This research explores risk identification trends and biases pervasive during space system development that limit the awareness of such risks. The findings and results provide insight into how future engineering and risk managers can potentially avoid these shortfalls.

A semantic mediation framework for architecting federated ubiquitous systems

Despite 20 years of research, ubiquitous systems have yet to become truly ubiquitous. A key challenge is the design for volatility and evolution experienced when those systems are deployed in more than one environment as well as for a substantial time period. The work presented here describes a proposed federated System of Systems (SoS) engineering approach for creating ubiquitous systems based on service-oriented principles. Service orientation is becoming more common for SoS implementation as it supports operational independence, managerial independence, and geographic distribution of constituent systems. However, in a virtual SoS, there is no central management authority and centrally agreed purpose, making interface standardization and integration of capabilities a difficult task. In this paper, we approach this problem by proposing a conceptual ontology-based semantic mediation framework to orchestrate the system engineering activities related to publishing constituent system capabilities during the design stage of the lifecycle, and enable automating capability discovery, selection, and composition at runtime.

Systems engineering design and spatial modeling for improved natural hazard risk assessment

Purpose – The purpose of this paper is to present a novel modeling approach that combines a balanced systems engineering design model with a geospatial model to explore the complex interactions between natural hazards and engineered systems.Design/methodology/approach – The approach taken in this work was to assemble a combined systems engineering design/geospatial model and interface it with a physics‐based hazard model to assess how to visualize the coupling of potential hazard effects from the physical domain into the functional/requirements domain.Findings – It was demonstrated that it is possible to combine the two models and apply them to realistic hazard cases. A number of potential benefits are described and made possible by this approach including the generation of systems‐level damage assessments, the potential reduction of geo‐information data collection requirements, the incorporation of socio‐technical elements, the generation of functional templates, and the creation of a superior mitigation...

Multivariate models using MCMCBayes for web-browser vulnerability discovery

Abstract Vulnerabilities that enable well-known exploit techniques are preventable, but their public discovery continues in software. Vulnerability discovery modeling (VDM) techniques were proposed to assist managers with decisions, but do not include influential variables describing the software release (SR) (e.g., code size and complexity characteristics) and security assessment profile (SAP) (e.g., security team size or skill). Consequently, they have been limited to modeling discoveries over time for SR and SAP scenarios of unique products, whose results are not readily comparable without making assumptions that equate all SR and SAP combinations under study. This article introduces a groundbreaking capability that allows forecasting expected discoveries over time for arbitrary SR and SAP combinations, thus enabling managers to better understand the effects of influential variables they control on the phenomenon. To do this, we use variables that describe arbitrary SR and SAP combinations and construct VDM extensions that parametrically scale results from a defined baseline SR and SAP to the arbitrary SR and SAP of interest. Scaling parameters are estimated using expert judgment data gathered with a novel pairwise comparison approach. These data are then used to demonstrate predictions and how multivariate VDM techniques could be used by software-makers.

Why a counterfeit risk avoidance strategy fails

Despite implementing a risk-avoidance strategy of purchasing semiconductor chips directly from a semiconductor manufacturers authorized sources, the electronics industry continues to discover counterfeit chips within the semiconductor supply chain and demand chain (i.e., the semiconductor ecosystem). This article presents a mathematical model of supply chain risk developed based on an analysis of how vulnerabilities, if exploited, increase the risk of the supply chain integrity being compromised. The model is developed based on a case study of an actual supply chain that was compromised and is tested for validity. A critical finding of this work is that the most important vulnerability of a supply chain is a malicious insider. This finding is important because prior research assumes that any breakdown in supply chain integrity originates outside of the trusted partners of the semiconductor ecosystem. Another finding is that the semiconductor industrys recommended counterfeit-avoidance strategy may have the unintended consequence of increasing, rather than reducing, the risk of counterfeit chips entering the supply chain. Based on the results, we propose a framework for counterfeit-risk management to complement existing counterfeit-risk programs.

Managing Technical Performance Margins to Control Earth and Space Science Instrument Costs

Abstract: Design margins are additional resources carried in technical performance parameters to account for uncertainties in designs. Margins are traditionally derived and allocated based upon historical experience as opposed to quantitative methods, jeopardizing the development of low-cost, space-based instruments. This article examines 62 instruments, assessing the interrelationships between pre-launch and actual launch margins, and utilizes multiple linear regression to examine margins and actual launch costs. Findings confirm use of margins above suggested industry standards in implementing space-based instruments, impacting affordability. Results provide a methodology to detect deficiencies or excesses in performance parameters, which can be used in making trade-offs to reduce cost growth.

Comparative analysis of Bayesian and classical approaches for software reliability measurement

Software failure remains an important cause of reported system outage. Yet, developing reliable software is still not well understood by the programmer, the Software Engineer and the Program manager. Software reliability growth models (SRGMs) provide a framework to analyze software failures by using past failure data to predict the reliability of the software. Most models that have been used have limitations in predicting accurately. There is a need to conduct research aimed at improving the performance of these models. To accurately predict reliability, the models parameters should be estimated in such a way that the mathematical function of the model fits with the failure data. While the majority of previous software reliability studies have used classical methods to estimate models parameters, a few other studies have used a Bayesian approach. Bayesian approaches allow the incorporation of prior information into models and they have been claimed to be more successful than classical approaches in certain situations. Our research goal is to investigate if the use of Bayesian methods improves the predictability of SRGMs by conducting a direct comparative analysis of Bayesian and classical approaches for software reliability assessment.

An approach to semantic interoperability in federations of systems

A central concern in system of systems engineering is the issue of coordination and interoperability between constituent systems. Special attention has to be given to interoperability with respect to federations of systems, which are coalitions of partner systems with decentralised power and authority. A major challenge in these systems is how to enable large-scale data and service interoperability without centralised management of the system of systems engineering activities. In this paper, we address this challenge by exploring the application of semantic interoperability principles and techniques to system of systems interoperability. Semantic interoperability refers to the ability of systems to share information meaningfully and to engage in meaningful collaborative activities. Subsequently, we propose a service-oriented approach which leverages semantic interoperability techniques to provide capabilities to system of systems constituents, resolve conceptual mismatches between heterogeneous systems, and automate the capability discovery process in a federation of systems.