Semantic Filters in Intelligence Analysis

It is a challenge to identify the relevant pieces for further intelligence analysis among a big chunk of data. Filters have been built to provide such a function in almost all the network traffic capture and analysis tools as well as signature-based intrusion detection systems. However, most filters...

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Bibliographic Details
Main Author: Chen, Jim Q.
Format: Conference Proceeding
Language:English
Subjects:
analsysis
Authentication
Authorization
Computer networks
Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Intelligent agents
Computing methodologies > Artificial intelligence > Knowledge representation and reasoning
Filtering theory
filters
Humans
intelligence
Ontological semantics
Protocols
Semantics
Theory of computation > Logic
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Summary:It is a challenge to identify the relevant pieces for further intelligence analysis among a big chunk of data. Filters have been built to provide such a function in almost all the network traffic capture and analysis tools as well as signature-based intrusion detection systems. However, most filters only work on strings of words, numbers, and/or other symbols. This paper proposes a type of context-aware and semantically relevant filters. This proposal is built on the findings in ontological semantics [1]. A detailed case study is used to show the effectiveness and efficiency of this proposal. The result of this research indicates that a good filter for intelligence analysis should incorporate relevant linguistic theories, which can explain one major aspect of human intelligence at another level.
DOI:10.1109/WI-IAT.2011.218