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	<pub:MastersThesis rdf:about="http://ebiquity.umbc.edu/paper/html/id/626/Information-Extraction-of-Security-related-entities-and-concepts-from-unstructured-text-">
		<rdfs:label><![CDATA[Information Extraction of Security related entities and concepts from unstructured text.]]></rdfs:label>
		<pub:title><![CDATA[Information Extraction of Security related entities and concepts from unstructured text.]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2013-05-30T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[Cyber Security has been a big concern especially in past one decade where it is witnessed
that targets ranging from large number of internet users to government agencies are
being attacked because of vulnerabilities present in the system. Even though these vulnerabilities
are identified and published publicly but response has always been slow in covering
up these vulnerabilities because there is no automatic mechanism to understand and process
this unstructured text that is published on internet. Our system will be tackling this
problem of processing unstructured text by identifying the security related terms including
entities and concepts from various unstructured data sources. This information extraction
task will help expediting the process of understanding and realizing the vulnerabilities and
thus making systems secure at faster rate.

This work will be describing a system that automatically extracts the terms from Cybersecurity
blogs and security bulletins using Natural Language Processing (NLP) and text
mining methods. Our NLP model is trained on manually annotated data using open-ended
blogs and more structured text like company’s official security bulletins. This manually
annotated data is unique of its kind since no such previous work has been done using this
methodology and it can be a significant contribution for people working in this domain.
Our named entity recognition model is trained on conditional random fields (CRFs) based
Stanford NER. This automation system will be able to help administrators of organizations
and governments to prioritize the task of beefing up their security and moreover track
unofficial data sources like chat rooms and twitter for zero day attacks.]]></pub:abstract>
		<pub:pages><![CDATA[54]]></pub:pages>
		<pub:organization><![CDATA[Ebiquity Lab]]></pub:organization>
		<pub:counter>2645</pub:counter>
		<pub:publisher><![CDATA[University of Maryland Baltimore County]]></pub:publisher>
		<pub:author>
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					<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Ravendar/Lal"><person:name><![CDATA[Ravendar Lal]]></person:name><rdfs:label><![CDATA[Ravendar Lal]]></rdfs:label></person:Person>
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<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Ravendar/Lal"><person:name><![CDATA[Ravendar Lal]]></person:name><rdfs:label><![CDATA[Ravendar Lal]]></rdfs:label></person:Person>
		</pub:firstAuthor>
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			<pub:softCopyFormat><![CDATA[PDF Document]]></pub:softCopyFormat>
			<pub:softCopyURI><![CDATA[http://ebiquity.umbc.edu/get/a/publication/680.pdf]]></pub:softCopyURI>
			<pub:softCopySize>748778</pub:softCopySize>
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	</pub:MastersThesis>

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