11 research outputs found

    Worm epidemics in vehicular networks

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    Connected vehicles promise to enable a wide range of new automotive services that will improve road safety, ease traffic management, and make the overall travel experience more enjoyable. However, they also open significant new surfaces for attacks on the electronics that control most of modern vehicle operations. In particular, the emergence of vehicle-to-vehicle (V2V) communication risks to lay fertile ground for self-propagating mobile malware that targets automobile environments. In this work, we perform a first study on the dynamics of vehicular malware epidemics in a large-scale road network, and unveil how a reasonably fast worm can easily infect thousands of vehicles in minutes. We determine how such dynamics are affected by a number of parameters, including the diffusion of the vulnerability, the penetration ratio and range of the V2V communication technology, or the worm self-propagation mechanism. We also propose a simple yet very effective numerical model of the worm spreading process, and prove it to be able to mimic the results of computationally expensive network simulations. Finally, we leverage the model to characterize the dangerousness of the geographical location where the worm is first injected, as well as for efficient containment of the epidemics through the cellular network.Peer ReviewedPostprint (author’s final draft

    Information dissemination in mobile networks

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    This thesis proposes some solutions to relieve, using Wi-Fi wireless networks, the data consumption of cellular networks using cooperation between nodes, studies how to make a good deployment of access points to optimize the dissemination of contents, analyzes some mechanisms to reduce the nodes' power consumption during data dissemination in opportunistic networks, as well as explores some of the risks that arise in these networks. Among the applications that are being discussed for data off-loading from cellular networks, we can find Information Dissemination in Mobile Networks. In particular, for this thesis, the Mobile Networks will consist of Vehicular Ad-hoc Networks and Pedestrian Ad-Hoc Networks. In both scenarios we will find applications with the purpose of vehicle-to-vehicle or pedestrian-to-pedestrian Information dissemination, as well as vehicle-to-infrastructure or pedestrian-to-infrastructure Information dissemination. We will see how both scenarios (vehicular and pedestrian) share many characteristics, while on the other hand some differences make them unique, and therefore requiring of specific solutions. For example, large car batteries relegate power saving techniques to a second place, while power-saving techniques and its effects to network performance is a really relevant issue in Pedestrian networks. While Cellular Networks offer geographically full-coverage, in opportunistic Wi-Fi wireless solutions the short-range non-fullcoverage paradigm as well as the high mobility of the nodes requires different network abstractions like opportunistic networking, Disruptive/Delay Tolerant Networks (DTN) and Network Coding to analyze them. And as a particular application of Dissemination in Mobile Networks, we will study the malware spread in Mobile Networks. Even though it relies on similar spreading mechanisms, we will see how it entails a different perspective on Dissemination

    Security, Privacy, Confidentiality and Integrity of Emerging Healthcare Technologies: A Framework for Quality of Life Technologies to be HIPAA/HITECH Compliant, with Emphasis on Health Kiosk Design

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    This dissertation research focused on the following: 1. Determined possible vulnerabilities that exist in multi-user kiosks and the computer systems that make up multi-user kiosk systems. 2. Developed an evaluation system and audit checklist for multi-user kiosk systems adapted from the Office for Civil Rights (OCR) audit protocols to address the vulnerabilities identified from our research. 3. Improved the design of a multi-user health kiosk to meet the HIPAA/HITECH standards by incorporating P&S policies. 4. Explored the feasibility and preliminary efficacy of an intervention to explore the magnitude of differences in users’ perceived risk of privacy and security (P&S) breaches as well as correlation between perceived risk and their intention to use a multi-user health kiosk. A gap analysis demonstrated that we successfully incorporated 81% of our P&S polices into the current design of our kiosk that is undergoing pilot testing. This is higher than our initial target of 50%. Repeated measures ANOVA was performed to analyze baseline and six-month follow-up of 36 study participants to measure the magnitude of the change in their “perceived risk”. Results from the ANOVA found significant group-by-time interaction (Time*Group) F (2, 33) = .27, P=.77, ηp2=.02, significant time interaction F (1, 33) = 4.73, P=.04, ηp2=.13, and no significant group interaction F (2, 33) =1.27, P=.30 ηp2=.07. The study intervention was able to significantly reduce users’ “perceived risk with time (baseline and six-month follow-up), even though the magnitude of the change was small. We were however, unable to perform the correlation analysis as intended since all the kiosk participants used in the analysis intended to use the kiosk both at baseline and at six-month follow-up. These findings will help in direct research into methods to reduce “perceived risk” as well as using education and communication to affect human behavior to reduce risky behavior on both internal and external use of new health IT applications and technologies. It could then serve as framework to drive policy in P&S of health applications, technologies and health IT systems

    Understanding, modeling and taming mobile malware epidemics in a large-scale vehicular network

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    The large-scale adoption of vehicle-to-vehicle (V2V) communication technologies risks to significantly widen the attack surface available to mobile malware targeting critical automobile operations. Given that outbreaks of vehicular computer worms self-propagating through V2V links could pose a significant threat to road traffic safety, it is important to understand the dynamics of such epidemics and to prepare adequate countermeasures. In this paper we perform a comprehensive characterization of the infection process of variously behaving vehicular worms on a road traffic scenario of unprecedented scale and heterogeneity. We then propose a simple yet effective data-driven model of the worm epidemics, and we show how it can be leveraged for smart patching infected vehicles through the cellular network in presence of a vehicular worm outbreak.Peer ReviewedPostprint (published version

    Understanding, modeling and taming mobile malware epidemics in a large-scale vehicular network

    No full text
    The large-scale adoption of vehicle-to-vehicle (V2V) communication technologies risks to significantly widen the attack surface available to mobile malware targeting critical automobile operations. Given that outbreaks of vehicular computer worms self-propagating through V2V links could pose a significant threat to road traffic safety, it is important to understand the dynamics of such epidemics and to prepare adequate countermeasures. In this paper we perform a comprehensive characterization of the infection process of variously behaving vehicular worms on a road traffic scenario of unprecedented scale and heterogeneity. We then propose a simple yet effective data-driven model of the worm epidemics, and we show how it can be leveraged for smart patching infected vehicles through the cellular network in presence of a vehicular worm outbreak.Peer Reviewe

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

    Columbus State University Honors College: Senior Theses, Fall 2020/Spring 2021

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    This is a collection of senior theses written by honors students at Columbus State University during the Fall 2020 and Spring 2021 semesters.https://csuepress.columbusstate.edu/honors_theses/1002/thumbnail.jp

    Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band II

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    Übersicht der Teilkonferenzen Band II • eHealth as a Service – Innovationen für Prävention, Versorgung und Forschung • Einsatz von Unternehmenssoftware in der Lehre • Energieinformatik, Erneuerbare Energien und Neue Mobilität • Hedonische Informationssysteme • IKT-gestütztes betriebliches Umwelt- und Nachhaltigkeitsmanagement • Informationssysteme in der Finanzwirtschaft • IT- und Software-Produktmanagement in Internet-of-Things-basierten Infrastrukturen • IT-Beratung im Kontext digitaler Transformation • IT-Sicherheit für Kritische Infrastrukturen • Modellierung betrieblicher Informationssysteme – Konzeptuelle Modelle im Zeitalter der digitalisierten Wirtschaft (d!conomy) • Prescriptive Analytics in I
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