Browsing by Author "Barker, Ken E."

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    Data-Driven Cyber Prediction in Hybrid Warfare
    (2019-06-17) Devereux, Hannah; Barker, Ken E.; Franceschet, Antonio; Chastko, Paul
    Cyber Warfare, despite being a thoroughly discussed tactic, is consistently misunderstood and taken out of context. Cyberattacks, most often committed during hybrid warfare, are often studied apart from the physical attributes of war. There is a lack of literature that studies the interplay of cyber and physical attributes within hybrid warfare. By analyzing and assessing the Ukrainian Crisis, this thesis investigates how physical attributes may be used to predict cyberattacks based on real world data. Using the Axelrod-Iliev equation optimal timing of cyberattack against Ukraine could be determined and, from this, defensive postures could be suggested. To test if the Axelrod-Iliev equation held true, statistical analysis was used. The statistical analysis verified the findings of the Axelrod-Iliev equation and provided groundwork for future research in the subject area. The statistical analysis found a lack of correlation between Military Personnel/Policemen Killed/Wounded and cyberattacks, Civilians/Politicians Killed/Wounded and cyberattacks, Protests and cyberattacks. Conversely it strongly suggested links between Bombings and cyberattack, and Open Firing and cyberattacks, which can be expounded upon to further understand the interplay of cyber and physical attributes in hybrid warfare. Keywords: Hybrid warfare, Cyber, Military, Ukraine, Russia, Data Analytics
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    Exploring Context for Privacy Protection in North American Higher Education and Beyond
    (2020-01) Wu, Leanne; Barker, Ken E.; Denzinger, Jörg; Oehlberg, Lora A.; Lock, Jennifer V.; Veletsianos, George
    Undergraduate students in North American post-secondary institutions are subject to a wide range of data collection. It includes data generated in the course of teaching and learning, but also can include a wide range of other aspects of modern life, such as closed-circuit security cameras, internet and wireless network use, and what students buy and consume. This makes the post-secondary institution an ideal model for understanding the privacy impact of modern and future technologies, as a single organization which collects and potentially uses wide-ranging amounts and kinds of data about our daily lives. This thesis proposes a framework which separates context into three interrelated layers so that systems can be designed which more fully protect the privacy of individuals, examines the ways in which we collect and use data about undergraduate students, and makes a quantitative study of undergraduate privacy behaviours and attitudes. Thus we present the case that context is a core concept for privacy protections which better protect undergraduate students and their privacy.
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    Managing Multitasking in Software Development Tasks Using Visual Analytics and Machine Learning
    (2018-09-13) Shakeri Hossein Abad, Zahra; Barker, Ken E.; Høyer, Peter; Maurer, F.
    Task switching and interruptions are a daily reality in software development projects: developers switch between Requirements Engineering (RE), coding, testing, daily meetings, and other tasks. As developing software involves a mix of analytical and creative work, and requires a significant load on brain functions, such as working memory and decision making, task switching in the context of software development imposes a cognitive load that causes software developers to lose focus and concentration thereby taking a toll on their productivity. Task switching may increase productivity through increased information flow and effective time management. However, it might also cause a cognitive load to reorient the primary task, which accounts for the decrease in developers? productivity and increases in errors. Thus, there is a need to understand and explore the multitasking behavior of software developers to model the factors that make task switchings more disruptive in development tasks through a multidisciplinary combination of software engineering, cognitive psychology, information visualization, and machine learning researches. Moreover, recent advances in visual analytics, e.g. visual storytelling, natural language processing, and classification methods offer an opportunity to advance the understanding of and support for multitasking in software development teams through the integration of cognitive psychology, machine learning, and information visualization. This dissertation studies the behavior of multitasking and task switching in software development teams through designing and implementing five in-depth comprehensive, explorative and retrospective studies aiming at explorations of the concept of task switching and interruption in the context of software development as well of the operationalization of the interruption characteristics that impact the vulnerability of development tasks to task switching. Following the outcomes of these explorations, and to assist analysts by identifying relevant information from documental sources during an interactive interview or after resuming an information-intensive task, a novel machine learning technique is proposed to dynamically extract requirements-relevant knowledge from existing documents. On the technical side, this technique proposes to use non-contiguous n-gram kernels in the context of requirements classification and applies rational kernels combined with SVMs to model and analyze the incoming information in real-time.
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    The Privacy Policy Permission Diagram - Toward a Unified View of Privacy
    (2020-07-24) Majedi, Maryam; Barker, Ken E.; Hagen, Gregory R.; Henry, Ryan; Kusalik, Peter G.; Ghorbani, Ali Akbar
    Data collection is inevitable. To receive services, customers must provide personal information to organizations. When individuals disclose information, they are making decisions about giving up a portion of their privacy. Organizations use privacy policies to communicate their practices to their clients. A privacy policy is a set of statements that specifies how an organization gathers, uses, discloses, and maintains a client’s data. Most privacy policies, however, lack a clear, complete explanation of how data providers' information is used. In his 1976 paper titled The Entity-Relationship Model -- Toward a Unified View of Data, Peter Chen proposes a diagrammatic technique to model entities and their relationships. This technique is independent of the entities' domains. Inspired by this contribution, we propose a modeling methodology called Privacy Policy Permission Diagram (PPPD), which provides a uniform, easy-to-understand representation of privacy policies, that can accurately and clearly determine how data is used within an organization's practice. Using this modeling methodology, privacy policies are presented in a diagram, and are populated into a privacy catalog. The privacy catalog can then be used to store privacy policies and their relationships. This methodology highlights inconsistencies and inaccuracies in the privacy policy.
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    A System to Ensure Robust, Honest Reporting of Sensor Data
    (2020-09-25) Muhtasim, Md. Adib; Reardon, Joel; Barker, Ken E.; Yanushkevich, Svetlana N.
    Due to the advancement of modern technologies, most systems and devices have become increasingly more sophisticated and autonomous. As these systems and their associated sensors are becoming an indispensable part of our lives, the question about the integrity of their reported data arises. Different parties have the incentive to modify the data of these systems and their associated sensors, particularly in the event of an accident or if they are being monitored or audited. If these systems are monitored all the time, then their privacy is invaded, and if they are not being monitored or audited at all, then they can get away with anything by providing false or incorrect information as there is no way to verify it. To overcome these challenges, we propose a monitoring and auditing system that prevents a system or a sensor from modifying its data while retaining the privacy of the monitored entity. As a proof of concept, our proposed system ensures robust, honest reporting of system or sensor data. Our monitoring and auditing system is mainly comprised of a private blockchain and a compliance checking system. We follow a two-step auditing process to ensure the integrity of the monitored system’s or sensor’s data. Any entity which is altering its data is caught in our system. We implement our proposed model to monitor and audit autonomous vehicles in the CARLA driving simulator environment to imitate real-world scenarios. To validate the efficiency and feasibility of our system, besides monitoring and auditing these entities, we also measure and analyze how long it takes for our system to catch a cheater entity and predict how much network traffic it has to handle in a real-world implementation and show how the audit frequency will impact the data storage in a large-scale implementation.
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    A Team Composition Approach For Social Crowdsourcing Communities
    (2020-09-22) Zaamout, Khobaib; Barker, Ken E.; Ruhe, Guenther; Tang, Anthony Hoi Tin
    This research takes place in an emerging paradigm of social computation that we name social crowdsourcing communities (SCCs). These are moderated online communities where members participate in collaborative activities (i.e. queries) designed to elicit their opinions concerning some topics, products, or services. This paradigm is distinct in that it combines the powers of crowdsourcing and social networking (SN) to allow for systematic querying of crowds and synthesizing response data (i.e. contributions) into coherent reports for decision-makers. SCCs consist of a beneficiary (i.e. the operators, the moderators, the analysts, and the organization that benefits from the reports), queries, a working crowd, and a platform where all activities occur. We show that it is possible to apply methods and techniques from existing fields to alleviate many of their challenges. One of these challenges is improving teamwork outcomes (i.e. contribution quality). Currently, SCC members, who are interested in a specific task, self-assemble into teams without considering any factors that may cause them to exhibit lower levels of productivity, participation, and contribution quality. The growth and query frequency restrictions imposed on these platforms by their operators to control operation costs further exacerbate this challenge. This thesis demonstrates how member behaviour can guide team formations and identifies specific behavioural characteristics related to improved team performance through an exploratory case study. It accomplishes this goal by capturing member behaviour in a model and using it to explore the compositions of existing teams. In doing so, this thesis identifies the specific compositions associated with increased team performance. The outcomes indicate the validity of this approach and provide a strong foundation for further investigation.