The Experts below are selected from a list of 5022 Experts worldwide ranked by ideXlab platform
Leslie F. Sikos - One of the best experts on this subject based on the ideXlab platform.
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KSEM (2) - Automated Reasoning over Provenance-Aware Communication Network Knowledge in Support of Cyber-Situational Awareness
Knowledge Science Engineering and Management, 2018Co-Authors: Leslie F. Sikos, Catherine Howard, Shaun Voigt, Markus Stumptner, Wolfgang Mayer, Dean PhilpAbstract:Cyber-situational awareness is crucial to applications such as Network monitoring and management, vulnerability assessment, and defense. To gain improved cyber-situational awareness, analysts can benefit from automated reasoning-based frameworks. However, such frameworks would require the processing of enormous amounts of Network data, which are characterized by syntactic variability. The formal representation of Networking concepts, their properties, and interrelations using RDF can narrow the interoperability gaps between routing information and Network semantics. Formal Knowledge representation also enables automated reasoning, which facilitates Network Knowledge discovery by making implicit statements explicit. However, capturing and reasoning over the provenance of RDF statements, which is essential to build analysts’ trust in automated support tools, is not trivial. This paper presents a novel framework for capturing provenance-aware Network Knowledge to enable automated reasoning for Network applications that require cyber-situational awareness.
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Handling Uncertainty and Vagueness in Network Knowledge Representation for Cyberthreat Intelligence
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018Co-Authors: Leslie F. SikosAbstract:The overwhelming and constantly growing amount of data Network analysts have to process urges automated mechanisms for cyberthreat intelligence. The formal representation of Network Knowledge unifies data from diverse sources, and enables efficient automation via reasoning, however, Network data is inherently uncertain and/or imprecise. There are various approaches to capture data certainty and vagueness, both at the level of abstraction and implementation, but many of these are not decidable, diverge from standards, and are limited in terms of querying and inference support. This paper proposes a description logic formalism to represent uncertain and fuzzy cyber-Knowledge while keeping favorable computational properties and implementation concerns in mind.
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FUZZ-IEEE - Handling Uncertainty and Vagueness in Network Knowledge Representation for Cyberthreat Intelligence
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018Co-Authors: Leslie F. SikosAbstract:The overwhelming and constantly growing amount of data Network analysts have to process urges automated mechanisms for cyberthreat intelligence. The formal representation of Network Knowledge unifies data from diverse sources, and enables efficient automation via reasoning, however, Network data is inherently uncertain and/or imprecise. There are various approaches to capture data certainty and vagueness, both at the level of abstraction and implementation, but many of these are not decidable, diverge from standards, and are limited in terms of querying and inference support. This paper proposes a description logic formalism to represent uncertain and fuzzy cyber-Knowledge while keeping favorable computational properties and implementation concerns in mind.
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Automated Reasoning over Provenance-Aware Communication Network Knowledge in Support of Cyber-Situational Awareness
Knowledge Science Engineering and Management, 2018Co-Authors: Leslie F. Sikos, Catherine Howard, Shaun Voigt, Markus Stumptner, Wolfgang Mayer, Dean PhilpAbstract:Cyber-situational awareness is crucial to applications such as Network monitoring and management, vulnerability assessment, and defense. To gain improved cyber-situational awareness, analysts can benefit from automated reasoning-based frameworks. However, such frameworks would require the processing of enormous amounts of Network data, which are characterized by syntactic variability. The formal representation of Networking concepts, their properties, and interrelations using RDF can narrow the interoperability gaps between routing information and Network semantics. Formal Knowledge representation also enables automated reasoning, which facilitates Network Knowledge discovery by making implicit statements explicit. However, capturing and reasoning over the provenance of RDF statements, which is essential to build analysts’ trust in automated support tools, is not trivial. This paper presents a novel framework for capturing provenance-aware Network Knowledge to enable automated reasoning for Network applications that require cyber-situational awareness.
Dean Philp - One of the best experts on this subject based on the ideXlab platform.
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KSEM (2) - Automated Reasoning over Provenance-Aware Communication Network Knowledge in Support of Cyber-Situational Awareness
Knowledge Science Engineering and Management, 2018Co-Authors: Leslie F. Sikos, Catherine Howard, Shaun Voigt, Markus Stumptner, Wolfgang Mayer, Dean PhilpAbstract:Cyber-situational awareness is crucial to applications such as Network monitoring and management, vulnerability assessment, and defense. To gain improved cyber-situational awareness, analysts can benefit from automated reasoning-based frameworks. However, such frameworks would require the processing of enormous amounts of Network data, which are characterized by syntactic variability. The formal representation of Networking concepts, their properties, and interrelations using RDF can narrow the interoperability gaps between routing information and Network semantics. Formal Knowledge representation also enables automated reasoning, which facilitates Network Knowledge discovery by making implicit statements explicit. However, capturing and reasoning over the provenance of RDF statements, which is essential to build analysts’ trust in automated support tools, is not trivial. This paper presents a novel framework for capturing provenance-aware Network Knowledge to enable automated reasoning for Network applications that require cyber-situational awareness.
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Automated Reasoning over Provenance-Aware Communication Network Knowledge in Support of Cyber-Situational Awareness
Knowledge Science Engineering and Management, 2018Co-Authors: Leslie F. Sikos, Catherine Howard, Shaun Voigt, Markus Stumptner, Wolfgang Mayer, Dean PhilpAbstract:Cyber-situational awareness is crucial to applications such as Network monitoring and management, vulnerability assessment, and defense. To gain improved cyber-situational awareness, analysts can benefit from automated reasoning-based frameworks. However, such frameworks would require the processing of enormous amounts of Network data, which are characterized by syntactic variability. The formal representation of Networking concepts, their properties, and interrelations using RDF can narrow the interoperability gaps between routing information and Network semantics. Formal Knowledge representation also enables automated reasoning, which facilitates Network Knowledge discovery by making implicit statements explicit. However, capturing and reasoning over the provenance of RDF statements, which is essential to build analysts’ trust in automated support tools, is not trivial. This paper presents a novel framework for capturing provenance-aware Network Knowledge to enable automated reasoning for Network applications that require cyber-situational awareness.
Michael Steketee - One of the best experts on this subject based on the ideXlab platform.
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Network Knowledge and the use of power
Social Networks, 2011Co-Authors: Brent Simpson, Barry Markovsky, Michael SteketeeAbstract:Complementing recent work on the effects of power on Network perceptions, we offer a theory specifying how Knowledge of Network structures and exchange processes differentially affect the use of power by advantaged and disadvantaged positions. We argue that under certain conditions, Network Knowledge is beneficial to occupants of low-power positions, but not to occupants of high-power positions. Any low-power actor can benefit from having superior information, but if all low-power actors have equally sound Knowledge, then all are worse off—a type of social trap. We tested these arguments by manipulating power and the availability of information on Network structure and exchange processes in an experimental exchange Network setting. The results were supportive.
Hsiao-ting Tzeng - One of the best experts on this subject based on the ideXlab platform.
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Notice of Retraction: Using classified Social Bookmarking of Network Knowledge to improve elementary students' science problem-solving ability
2010 3rd International Conference on Computer Science and Information Technology, 2010Co-Authors: Hsian-shen Wang, Hsiao-ting TzengAbstract:This article has been retracted by the publisher.
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Notice of Retraction Using classified Social Bookmarking of Network Knowledge to improve elementary students' science problem-solving ability
2010 3rd International Conference on Computer Science and Information Technology, 2010Co-Authors: Hsian-shen Wang, Hsiao-ting TzengAbstract:This study explored students' information sorting ability and problem-solving ability on the effect of using classified Social Bookmarking of Network Knowledge. A quasi-experimental research design was conducted in a medium elementary school in northern Taiwan. The results show there was a positive effect on information sorting ability and problem-solving ability. It also implies that students built an information organizing model by using classified Social Bookmarking of Network Knowledge to assist project-based inquiry learning. The internalized model starts from information needs, clarifying main topic, searching keywords, browsing, selecting, reading, searching for solutions, tags recording as bookmarks, classifying, and uploading to share.
R. Richardson - One of the best experts on this subject based on the ideXlab platform.
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Runtime Semantic Interoperability for Gathering Ontology-based Network Context
2006 IEEE IFIP Network Operations and Management Symposium NOMS 2006, 2006Co-Authors: J. Keeney, D. Lewis, D. O'sullivan, A. Roelens, V. Wade, A. Boran, R. RichardsonAbstract:The trends for pushing more operational intelligence towards Network elements to achieve more context-aware and self-managing behavior often requires elements to gather Network Knowledge without necessarily binding explicitly to all of the potential sources of that Knowledge. Though event-based publish-subscribe models allow efficient distribution of Knowledge where the event types are known globally, dynamic service chains, ad hoc Networks and pervasive computing application all introduce a more fluid and heterogeneous range of context Knowledge. This requires some runtime translation of Knowledge between sources and sinks of Network context. This paper builds on existing mapping techniques that use ontological forms of existing management information models to examine the extent to which these can be employed for runtime semantic interoperability for Network Knowledge. It presents results in developing a management Knowledge delivery framework based on existing models and platforms, but which offers a more decentralized Knowledge exchange mechanism