Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity
Decision support tools are key components of intelligent sociotechnical systems, and their successful implementation faces a variety of challenges, including the multiplicity of information sources, heterogeneous format, and constant changes. Handling such challenges requires the ability to analyze...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/6/3/91 |
| _version_ | 1797491157072084992 |
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| author | Mario A. Leiva Alejandro J. García Paulo Shakarian Gerardo I. Simari |
| author_facet | Mario A. Leiva Alejandro J. García Paulo Shakarian Gerardo I. Simari |
| author_sort | Mario A. Leiva |
| collection | DOAJ |
| description | Decision support tools are key components of intelligent sociotechnical systems, and their successful implementation faces a variety of challenges, including the multiplicity of information sources, heterogeneous format, and constant changes. Handling such challenges requires the ability to analyze and process inconsistent and incomplete information with varying degrees of associated uncertainty. Moreover, some domains require the system’s outputs to be explainable and interpretable; an example of this is cyberthreat analysis (CTA) in cybersecurity domains. In this paper, we first present the P-DAQAP system, an extension of a recently developed query-answering platform based on defeasible logic programming (DeLP) that incorporates a probabilistic model and focuses on delivering these capabilities. After discussing the details of its design and implementation, and describing how it can be applied in a CTA use case, we report on the results of an empirical evaluation designed to explore the effectiveness and efficiency of a possible world sampling-based approximate query answering approach that addresses the intractability of exact computations. |
| first_indexed | 2024-03-10T00:43:25Z |
| format | Article |
| id | doaj.art-b46d321970e4407bbc9f45ffc48df468 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T00:43:25Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-b46d321970e4407bbc9f45ffc48df4682023-11-23T15:03:45ZengMDPI AGBig Data and Cognitive Computing2504-22892022-08-01639110.3390/bdcc6030091Argumentation-Based Query Answering under Uncertainty with Application to CybersecurityMario A. Leiva0Alejandro J. García1Paulo Shakarian2Gerardo I. Simari3Department of Computer Science and Engineering, Universidad Nacional del Sur (UNS), Bahia Blanca 8000, ArgentinaDepartment of Computer Science and Engineering, Universidad Nacional del Sur (UNS), Bahia Blanca 8000, ArgentinaSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USADepartment of Computer Science and Engineering, Universidad Nacional del Sur (UNS), Bahia Blanca 8000, ArgentinaDecision support tools are key components of intelligent sociotechnical systems, and their successful implementation faces a variety of challenges, including the multiplicity of information sources, heterogeneous format, and constant changes. Handling such challenges requires the ability to analyze and process inconsistent and incomplete information with varying degrees of associated uncertainty. Moreover, some domains require the system’s outputs to be explainable and interpretable; an example of this is cyberthreat analysis (CTA) in cybersecurity domains. In this paper, we first present the P-DAQAP system, an extension of a recently developed query-answering platform based on defeasible logic programming (DeLP) that incorporates a probabilistic model and focuses on delivering these capabilities. After discussing the details of its design and implementation, and describing how it can be applied in a CTA use case, we report on the results of an empirical evaluation designed to explore the effectiveness and efficiency of a possible world sampling-based approximate query answering approach that addresses the intractability of exact computations.https://www.mdpi.com/2504-2289/6/3/91intelligent sociotechnical systemshuman-in-the-loop computingstructured probabilistic argumentationcybersecurity |
| spellingShingle | Mario A. Leiva Alejandro J. García Paulo Shakarian Gerardo I. Simari Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity Big Data and Cognitive Computing intelligent sociotechnical systems human-in-the-loop computing structured probabilistic argumentation cybersecurity |
| title | Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity |
| title_full | Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity |
| title_fullStr | Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity |
| title_full_unstemmed | Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity |
| title_short | Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity |
| title_sort | argumentation based query answering under uncertainty with application to cybersecurity |
| topic | intelligent sociotechnical systems human-in-the-loop computing structured probabilistic argumentation cybersecurity |
| url | https://www.mdpi.com/2504-2289/6/3/91 |
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