High Level Fusion in the Cyber Domain

Sudit, Moises; Yang, Shanchieh Jay; Kühl, Michael; Stotz, Adam; Holender, Michael; Holsopple, Jared; Bohannon, Eric; Kistner, Jason

doi:10.21236/ada441263

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…In our previous work [13], we introduced INFERD (INformation Fusion Engine for Real-Time DecisionMaking), an adaptable information fusion engine which performs fusion at levels zero, one, and two to provide real-time situational assessment. Since then, many improvements have been made to INFERD allowing it to be a more robust system minimizing the dependence on perfect or complete a priori knowledge, while allowing dynamic generation of hypothesis of interest.…”

Section: Information Fusion Engine For Real-time Decision-making (Infmentioning

confidence: 99%

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A Graph-Based Framework for Fusion: From Hypothesis Generation to Forensics

Sudit

Nagi

Stotz

et al. 2006

2006 9th International Conference on Information Fusion

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The intent of this paper is to show enhancements in Level 2 and 3 fusion capabilities through a new class of graph models and solution strategies. The problem today is not often lack of information, but instead, information overload. Graphs have demonstrated to be a useful framework to represent and analyze large amounts of information. Classical strategies such as Bayesian Networks, Semantic Networks and Graph Matching are some examples of the power ofgraphs. We will introduce two different but related graph-based structures that will allow us to span the temporal performance of decisionmaking processes. Given that most of the high level information fusion problems of interest are NP-Hard, there is a need to separate methodologies between "near real-time" tools and forensic heuristics. With this in mind we will introduce a real-time decision-making tool (INFERD) and a forensic graph matching algorithm (TruST)

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Section: Information Fusion Engine For Real-time Decision-making (Infmentioning

confidence: 99%

“…As described in [13], INFERD continues to be a hierarchical approach which builds knowledge from Level 0 to Level 1 to finally obtain Situational Awareness (Level 2). Input to the LO process is taken in raw data form and then necessary information is extracted.…”

Section: Information Fusion Engine For Real-time Decision-making (Infmentioning

confidence: 99%