國立虎尾科技大學 |

Graph-based Event Correlation for Network Security Defense.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Graph-based Event Correlation for Network Security Defense./
Author:	Neise, Patrick.
Description:	1 online resource (96 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: A.
Contained By:	Dissertation Abstracts International79-08A(E).
Subject:	Management. -
Online resource:	click for full text (PQDT)
ISBN:	9780355826203

Graph-based Event Correlation for Network Security Defense.
Neise, Patrick.

Graph-based Event Correlation for Network Security Defense. - 1 online resource (96 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: A.

Thesis (D.Engr.)--The George Washington University, 2018.

Includes bibliographical references

Organizations of all types and their computer networks are constantly under threat of attack. While the overall detection time of these attacks is getting shorter, the average detection time of weeks to months allows the attacker ample time to potentially cause damage to the organization. Current detection methods are primarily signature based and typically rely on analyzing the available data sources in isolation. Any analysis of how the individual data sources relate to each other is usually a manual process, and will most likely occur as a forensic endeavor after the attack identification occurs via other means. The use of graph theory and the graph databases built to support its application can provide a repeatable and automated analysis of the data sources and their relationships. By aggregating the individual data sources into a graph database based on a model that supports the data types and relationships, database queries can extract information relevant to the detection of attack behavior within the network. The work in this Praxis shows how the graph model and database queries will reduce the overall time to detection of a successful attack by enabling defenders to understand better how the data elements and what they represent are related.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355826203Subjects--Topical Terms:

558618
Management.
Index Terms--Genre/Form:

554714
Electronic books.

Graph-based Event Correlation for Network Security Defense.
LDR:02493ntm a2200337Ki 4500 001 920743
005 20181203094033.5
006 m o u
007 cr mn||||a|a||
008 190606s2018 xx obm 000 0 eng d
020 $a 9780355826203
035 $a (MiAaPQ)AAI10785425
035 $a (MiAaPQ)gwu:14129
035 $a AAI10785425
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Neise, Patrick. $3 1195618
245 1 0 $a Graph-based Event Correlation for Network Security Defense.
264 0 $c 2018
300 $a 1 online resource (96 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: A.
500 $a Adviser: Thomas F. Bersson.
502 $a Thesis (D.Engr.)--The George Washington University, 2018.
504 $a Includes bibliographical references
520 $a Organizations of all types and their computer networks are constantly under threat of attack. While the overall detection time of these attacks is getting shorter, the average detection time of weeks to months allows the attacker ample time to potentially cause damage to the organization. Current detection methods are primarily signature based and typically rely on analyzing the available data sources in isolation. Any analysis of how the individual data sources relate to each other is usually a manual process, and will most likely occur as a forensic endeavor after the attack identification occurs via other means. The use of graph theory and the graph databases built to support its application can provide a repeatable and automated analysis of the data sources and their relationships. By aggregating the individual data sources into a graph database based on a model that supports the data types and relationships, database queries can extract information relevant to the detection of attack behavior within the network. The work in this Praxis shows how the graph model and database queries will reduce the overall time to detection of a successful attack by enabling defenders to understand better how the data elements and what they represent are related.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Management. $3 558618
650 4 $a Information technology. $3 559429
655 7 $a Electronic books. $2 local $3 554714
690 $a 0454
690 $a 0489
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The George Washington University. $b Engineering Management. $3 1179162
773 0 $t Dissertation Abstracts International $g 79-08A(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10785425 $z click for full text (PQDT)