Timothy Eveleigh

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.

A framework for capturing the hidden stakeholder system

As programs rise in intricacy and scope, the number of stakeholders involved also increases, often driving an exponential growth in program complexity. This complexity is caused by the stakeholder relationships which form an underlying system that influences all aspects of the program. Understanding, managing, and leveraging this stakeholder system will greatly increase a programs probability of success. This paper provides a framework for capturing this stakeholder system in a series of architectural views. These architectural products document the programs stakeholder concerns and also illustrate how those stakeholders interrelate over the systems lifecycle. The ultimate objective for the framework and use of the resulting products is to allow for right-sized stakeholder involvement, promote effective use of resources, and increase the probability of overall program success with the assurance of lasting stakeholder commitment. Additionally, this unique insertion of Stakeholder Analysis and Social Network Analysis into an Architecture Framework fulfills an original intent of Architecture Framework, capturing the entire sociotechnical enterprise system. ©2012 Wiley Periodicals, Inc. Syst Eng 16

A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations

An Internet of Things’ (IoT) connected society and system represents a tremendous paradigm shift. We present a framework for a decision-support system (DSS) that operates within the IoT ecosystem. The DSS leverages advanced analytics of electric smart meter (ESM) network communication-quality data to improve cost predictions for smart meter field operations and provide actionable decision recommendations regarding whether to send a technician to a customer location to resolve an ESM issue. The model is empirically evaluated using data sets from a commercial network. We demonstrate the efficiency of our approach with a complete Bayesian network prediction model and compare with three machine learning prediction model classifiers: 1) Naïve Bayes; 2) random forest; and 3) decision tree. Results demonstrate that our approach generates statistically noteworthy estimations and that the DSS will improve the cost efficiency of ESM network operations and maintenance.

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.

Optimizing residential energy management using an autonomous scheduler system

Abstract In this paper, a smart home energy management system (SHEMS) is developed using a limited memory algorithm for bound constrained problems known as L-BFGS-B, along with time-of-use (ToU) pricing to optimize appliance scheduling in a 24-h period. The allocation of energy resources for each appliance is coordinated by a smart controllable load (SCL) device embedded in the households smart meter. SCL guarantees automation of the proposed SHEMS and prevents manual participation of customers in demand response (DR) programs. The model is simulated on a population of 247 residential prosumers with solar PV systems based on 15-min interval electric load data from a residential community in Austin, TX. After clustering households based on their electricity profiles, the proposed optimization model is performed. Simulation results show that the proposed autonomous scheduling system reduces cumulative energy consumption for customers across the different clusters. In addition, when households are grouped based on their respective category according to the ToU pricing scheme, the simulation reports a notable decrease in total energy consumption, from 65.771 kWh to 44.295 kWh, as well as a reduction in the cumulative cost of energy, from

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

6.550 to

Comparative analysis of Bayesian and classical approaches for software reliability measurement

4.393 per day. Simulation results confirm that the proposed algorithm effectively improves the operational efficiency of the distribution system, reduces power congestion at key times, and decreases electricity costs for prosumers.