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<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
				<PublisherName>Iranian Society of Cryptology</PublisherName>
				<JournalTitle>The ISC International Journal of Information Security</JournalTitle>
				<Issn>2008-2045</Issn>
				<Volume>4</Volume>
				<Issue>2</Issue>
				<PubDate PubStatus="epublish">
					<Year>2012</Year>
					<Month>07</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Real-Time intrusion detection alert correlation and attack scenario extraction based on the prerequisite consequence approach</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>125</FirstPage>
			<LastPage>136</LastPage>
			<ELocationID EIdType="pii">39130</ELocationID>
			
<ELocationID EIdType="doi">10.22042/isecure.2013.4.2.4</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Z.</FirstName>
					<LastName>Zali</LastName>
<Affiliation></Affiliation>

</Author>
<Author>
					<FirstName>M. R.</FirstName>
					<LastName>Hashemi</LastName>
<Affiliation></Affiliation>

</Author>
<Author>
					<FirstName>H.</FirstName>
					<LastName>Saidi</LastName>
<Affiliation></Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2012</Year>
					<Month>12</Month>
					<Day>30</Day>
				</PubDate>
			</History>
		<Abstract>Alert correlation systems attempt to discover the relations among alerts produced by one or more intrusion detection systems to determine the attack scenarios and their main motivations. In this paper a new IDS alert correlation method is proposed that can be used to detect attack scenarios in real-time. The proposed method is based on a causal approach due to the strength of causal methods in practice. To provide a picture of the current intrusive activity on the network, we need a real-time alert correlation. Most causal methods can be deployed offline but not in real-time due to time and memory limitations. In the proposed method, the knowledge base of the attack patterns is represented in a graph model called the Causal Relations Graph. In the offline mode, we construct Queue trees related to alerts&#039; probable correlations. In the real-time mode, for each received alert, we can find its correlations with previously received alerts by performing a search only in the corresponding tree. Therefore, the processing time of each alert decreases significantly. In addition, the proposed method is immune to deliberately slowed attacks. To verify the proposed method, it was implemented and tested using DARPA2000 dataset. Experimental results show the correctness of the proposed alert correlation and its efficiency with respect to the running time.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Attack</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Intrusion</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Attack Scenario</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Intrusion Detection System</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">IDS</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Alert</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Alert Correlation</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Graph</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://www.isecure-journal.com/article_39130_696ea2c7271f939ccafc511902e85604.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
